Vision-Language-Action (VLA) models revolutionize robotic systems by enabling end-to-end perception-to-action pipelines that integrate multiple sensory modalities, such as visual signals processed by cameras and auditory signals captured by microphones. This multi-modality integration allows VLA models to interpret complex, real-world environments using diverse sensor data streams. Given the fact that VLA-based systems heavily rely on the sensory input, the security of VLA models against physical-world sensor attacks remains critically underexplored. To address this gap, we present the first systematic study of physical sensor attacks against VLAs, quantifying the influence of sensor attacks and investigating the defenses for VLA models. We introduce a novel ``Real-Sim-Real" framework that automatically simulates physics-based sensor attack vectors, including six attacks targeting cameras and two targeting microphones, and validates them on real robotic systems. Through large-scale evaluations across various VLA architectures and tasks under varying attack parameters, we demonstrate significant vulnerabilities, with susceptibility patterns that reveal critical dependencies on task types and model designs. We further develop an adversarial-training-based defense that enhances VLA robustness against out-of-distribution physical perturbations caused by sensor attacks while preserving model performance. Our findings expose an urgent need for standardized robustness benchmarks and mitigation strategies to secure VLA deployments in safety-critical environments.

Aspect term extraction (ATE) is an important Natural Language Processing task, which aims to extract aspect terms from reviews. Recently, data augmentation has emerged as a reliable approach for relieving data sparsity in the NLP area. For ATE, self-labeling and semi-generation methods have been proposed to implement effective data augmentation. However, they either rely on external data or a pretrained generation model. In this paper, we propose a simple and self-contained augmentation method, which produces new instances for augmentation by context decoupling and infrequent term refilling, without using external data and generation models. We conduct experiments on four benchmark SemEval datasets. The test results show that our method yields substantial improvements, and performs comparably to the state-of-the-art method which uses external data.

Continual learning (CL) is a fundamental topic in machine learning, where the goal is to train a model with continuously incoming data and tasks. Due to the memory limit, we cannot store all the historical data, and therefore confront the “catastrophic forgetting” problem, i.e., the performance on the previous tasks can substantially decrease because of the missing information in the latter period. Though a number of elegant methods have been proposed, the catastrophic forgetting phenomenon still cannot be well avoided in practice. In this paper, we study the problem from the gradient perspective, where our aim is to develop an effective algorithm to calibrate the gradient in each updating step of the model; namely, our goal is to guide the model to be updated in the right direction under the situation that a large amount of historical data are unavailable. Our idea is partly inspired by the seminal stochastic variance reduction methods (e.g., SVRG and SAGA) for reducing the variance of gradient estimation in stochastic gradient descent algorithms. Another benefit is that our approach can be used as a general tool, which is able to be incorporated with several existing popular CL methods to achieve better performance. We also conduct a set of experiments on several benchmark datasets to evaluate the performance in practice.

We tackle Aspect Term Extraction (ATE), a task that automatically recognizes aspect terms conditioned on the under-standing of word-level semantics. Due to the capacity of enriching linguistic phenomena for learning, data augmentation contributes to the establishment of robust ATE models. In this paper, we propose to leverage back translation to augment the training data for ATE. It is grounded on the potential advantages that the back-translated instances generally appear as paraphrases, providing diverse pragmatic modes for learning when semantics remains unchanged. This helps to enhance ATE models in recognizing aspect terms when varied contexts and morphologically-different words occur during test. In our experiments, we apply an off-the-shelf Neural Machine Translation (NMT) model for back translation, using French, Chinese and German as interlanguages, respectively. Besides, word alignment is conducted to designate aspect terms in the back-translated cases. Experimental results on SemEval benchmarks show that retraining with the augmented data produces substantial improvements, reaching up to 3.46% at best. In addition, the experiments suggest that 1) family languages are more beneficial than non-family for the aforementioned data augmentation, and 2) selective sampling produces positive effects in the low-resource settings, It is noteworthy that back translation has been explored for data augmentation in other fields, with the aim to enhance neural language modeling. Nevertheless, it hasn't yet been systematically studied towards the ATE task. Although a vest-pocket method is provided in this paper, the comprehensive analysis is conducted, including that on interlanguage selection, low-resource application, as well as compatibility with both conventional and pretrained neural models, besides that in the common comparison and ablation experiments. All the models and codes in the experiments will be made publicly available to support reproducible research.

Comparative Opinion Quintuple Extraction (COQE) is a task of recognizing comparative relationships in a sentence-level comment. It is additionally required to extract the quintuple constituents that attribute to a specific comparative relation, including a subject and object, as well as comparative aspect, opinion and preference. Previous study employ pipeline models in addressing the COQE task, which tend to suffer from error propagation. To address the issue, in this paper, we propose a BERT-based end-to-end neural model as the alternative. Specifically, we first boil COQE down to a set prediction problem, due to the finding that all the quintuple constituents fail to hold coherent or sequential relationships. In other word, we consider a group of quintuple components as a set, and intent to bag-and-drag them as a whole, instead individually and one-by-one. Furthermore, we leverage Graph Convolutional Network (GCN) to enhance the end-to-end model, which plays the role of perceiving and representing the relevant relations among the quintuple components. We experiment on three benchmark datasets, including Camera-COQE, Car-COQE and Ele-COQE. The experimental results show that our model (GCN-E2E) yields a significant improvement in most cases, compared to the BERT-based pipeline baseline. The performance reaches the F1-scores of 14.10%, 36.46% and 39.29%, with the improvements of 0.74%, 6.71% and 8.56%.

Pose-guided person image generation that aims to transfer the pose of a given person to a target pose has recently received lots of research attention. Due to the spatial misalignment and occlusions of different local body parts by pose variations, this task is still challenging especially in maintaining high-fidelity textures and body structures in generated images. Besides, most works also suffer from the limited number of texture styles in the given person datasets, restricting the diversity of generated persons' appearances. To solve these problems, we design a Kernel-based Texture-Fusion Joint Refinement Network (TFJR-Net) to jointly refine the structure and texture information of generated images. First, we leverage a bone-map representation to guide the generation of human parsing maps, which has more structure priors and richer context information than traditional key-point maps, thus reduce the uncertainty of generated body structures. Next, a Texture-Kernel Injection Normalization module (TKIN) is proposed to inject the per-region texture-kernel into the corresponding semantic region from the human parsing map, which decouples the texture and shape information, and also preserves fine-grained features for complex textures. Furthermore, we are the first to introduce external texture patterns outside of the dataset in human semantic regions such as the upper clothes. We fuse the two texture domains in a shared texture space through our designed texture-fusion TKIN modules. Extensive experiments are conducted on the Deepfashion dataset, with the DTD dataset as an external texture source. The experimental results demonstrate the superiority of our proposed method in generating persons of better textures and structures than state-of-the-art works, and also show the generalization ability of our proposed method to absorb diversified external textures for generating person images. The source codes are available athttps://github.com/pilgrim00/TKIN.

We tackle Aspect Term Extraction (ATE), an important natural language processing task that automatically identifies words of domain-specific aspects. Recently, a variety of sophisticated neural models and learning strategies have been explored for enhancing ATE, and significant improvements have been obtained. We intend to strengthen the neural models by involving features of Part-Of-Speech (POS) and syntactic dependency into the encoding process. It is motivated by the empirical findings that features of linguistic structure help to refine the understanding of semantics. Accordingly, we propose a stepwise encoding approach, where POS and syntactic dependency are successively leveraged step by step, including 1) joint encoding over both word and POS sequences using a pretrained language model; 2) BiGRU-based representational refinement conditioned on semantics-aware POS information and POS-aware semantic information; 3) representational augmentation by convolutional encoding of dependency graph. We conduct experiments on the four benchmark datasets of Semantic Evaluation (SemEval) for ATE. Experimental results show that our method obtains substantial improvements on all the considered datasets, and the performance ($F1$-score) reaches 87.69%, 89.76%, 77.94% and 83.96% for L-14, R-14, R-15 and R-16, respectively. All the models and source codes in the experiments will be made publicly available to support reproducible research.

Aspect Term Extraction (ATE) is a natural language processing task, which identifies the languages describing product attributes. Such languages (words) are referred to aspect terms in this field. The current neural ATE models suffer from sparsity of available training data. As a result, they fail to be robust in real applications due to overfitting. More seriously, the distinguishable underlying features cannot be learned sufficiently by neural networks, which causes high misjudgement rates. Deliberate data expansion by human annotation undoubtedly helps to alleviate the problem. However, it is time-consuming. In order to overcome the bottleneck, we utilize the Regularized Dropout (R-Drop) approach to implement self data augmentation, creating variant distributed representations for learning in the real-time computation process of neural networks. More importantly, we propose to conduct contrastive learning over the self-augmented data, which sufficiently leverages the variant distributed representations to explore the distinguishable features. We experiment on four widely-used benchmark datasets (R14-16 and L14) in the shared tasks of Semantic Evaluation (SemEval). Experimental results show that contrastive learning over self-augmented data yields significant performance gains, where the improvement is up to 1.9% F1-score at best. In addition, it is demonstrated in the experiments that our method outperforms the state of the art on two test sets (R14 and R16), and meanwhile achieves competitive performance on the rest test sets.

A coreset is a small set that can approximately preserve the structure of the original input data set. Therefore we can run our algorithm on a coreset so as to reduce the total computational complexity. Conventional coreset techniques assume that the input data set is available to process explicitly. However, this assumption may not hold in real-world scenarios. In this paper, we consider the problem of coresets construction over relational data. Namely, the data is decoupled into several relational tables, and it could be very expensive to directly materialize the data matrix by joining the tables. We propose a novel approach called ``aggregation tree with pseudo-cube'' that can build a coreset from bottom to up. Moreover, our approach can neatly circumvent several troublesome issues of relational learning problems [Khamis et al., PODS 2019]. Under some mild assumptions, we show that our coreset approach can be applied for the machine learning tasks, such as clustering, logistic regression and SVM.

Distributed Virtual Reality (DVR) is a combination of network and virtual reality technology, it could facilitate to construct a uniformly shared Distributed Virtual Environment (DVE) by using network to connect geographically distributed multiplayers. This paper concentrates on the theoretical research and practical development about Multiplayer Virtual Intelligent System (MVIS), and the main contribution could be summarized as two points. (1) Based on the DVR technology, this paper presented some theoretical research on MVIS, including the classification of virtual entities, communication pattern of entities, and the behavioral consistency research. Furthermore, a Multiplayer Earliest Deadline First (MEDF) program was proposed in order to guarantee the consistency of entities. (2) A prototype algorithm experiment system, called Multiplayer Graph-algorithm Intelligent System (MGIS), was designed. MGIS not onlyefficiently solves many problems in traditional computer algorithm teaching, such as high-abstraction, difficulty to understand, and lack of interaction mechanism; but also extends the application of DVR to cultural tourism, because MGIS is developed on the 3D scene of Lushan Mountain, which is one of the notable tourist attractions in China, and was included in the UNESCO World Heritage list in 1996. What i’s more, MGIS illustrates the ability of expression, applicability and generality of the theoretical research about MVIS.