Unsupervised Change Detection (UCD) in Very High Resolution (VHR) Remote Sensing (RS) images remains to be a difficult challenge due to the inherent spatio-temporal complexity within data. Inspired by recent advancements in Visual Foundation Models (VFMs) and Contrastive Learning (CL), this research aims to develop CL methodologies to translate implicit knowledge in VFM into change representations, thus eliminating the need for explicit supervision. To this end, we introduce a Semantic-to-Change (S2C) learning framework for UCD in VHR RS images. Differently from existing CL methodologies that typically focus on learning multi-temporal similarities, we introduce a novel triplet learning strategy that explicitly models temporal differences, which are crucial to the CD task. Furthermore, random spatial and spectral perturbations are introduced during training to enhance robustness to temporal noise. In addition, a grid sparsity regularization is defined to suppress insignificant changes, and an IoU-matching algorithm is developed to refine the CD results. Experiments on three benchmark CD datasets demonstrate that the proposed S2C learning framework achieves significant improvements in accuracy, surpassing current state-of-the-art by over 31%, 9% and 23%, respectively. It also demonstrates robustness and sample efficiency, suitable for training and adaptation of various VFMs or backbone neural networks.

Nowadays, research of Text Classification (TC) based on graph neural networks (GNNs) is on the rise. Both inductive methods and transductive methods have made significant progress. For transductive methods, the semantic interaction between texts plays a crucial role in the learning of effective text representations. However, it is difficult to perform inductive learning while modeling interactions between texts on the graph. To give a universal solution, we propose the graph neural network based on pre-trained semantic interaction called PaSIG. Firstly, we construct a text-word heterogeneity graph and design an asymmetric structure to ensure one-way message passing from words to the test texts. Meanwhile, we use the context representation capability of the pre-trained language model to construct node features that contain classification semantic information. Afterward, we explore the adaptative aggregation methods with a gated fusion mechanism. Extensive experiments on five datasets have shown the effectiveness of PaSIG, with the accuracy exceeding the baseline by 2.7% on average. While achieving state-of-the-art performance, we have also taken measures of subgraph sampling and intermediate state preservation to achieve fast inference.

Stance detection, a natural language processing technique, captures user attitudes on controversial social media topics. However, the semantic ambiguity of social texts makes accurate stance determination challenging. Existing annotated data is often domain-specific, resulting in poor model generalization for unseen targets and cross-domain scenarios. To tackle this challenge, we introduce inference tasks related to stance detection as auxiliary tasks and use a multitask learning approach to improve the model’s understanding of textual semantics. Meanwhile, to alleviate the scarcity of annotated data, we use argumentation corpus with abundant resources as the source domain to train the basic stance detection model. We integrate this model with a large-scale framework to facilitate weakly supervised learning for machine annotation of unlabeled social texts, thereby boosting the model’s adaptability across different targets and domains. Experimental results on four Twitter datasets demonstrate that our method can significantly improve the model’s stance discriminative ability and generalization performance.

The goal of stance detection is to automatically recognize the author's expressed attitude in text towards a given target. However, social media users often express themselves briefly and implicitly, which leads to a significant number of comments lacking explicit reference information to the target, posing a challenge for stance detection. To address the missing relationship between text and target, existing studies primarily focus on incorporating external knowledge, which inevitably introduces noise information. In contrast to their work, we are dedicated to mining implicit relational information within data. Typically, users tend to emphasize their attitudes towards a relevant topic or aspect of the target while concealing others when expressing opinions. Motivated by this phenomenon, we suggest that the potential correlation between text and target can be learned from instances with similar topics. Therefore, we design a pretext task to mine the topic associations between samples and model this topic association as a dynamic weight introduced into contrastive learning. In this way, we can selectively cluster samples that have similar topics and consistent stances, while enlarging the gap between samples with different stances in the feature space. Additionally, we propose a nearest-neighbor prediction mechanism for stance classification to better utilize the features we constructed. Our experiments on two datasets demonstrate the advanced and generalization ability of our method, yielding the state-of-the-art results.

As the intelligent software, the development and application of large language models are extremely hot topics recently, bringing tremendous changes to general AI and software industry. Nonetheless, large language models, especially open source ones, incontrollably suffer from some potential software quality issues such as instability, inaccuracy, and insecurity, making software testing necessary. In this paper, we propose the first solution for functional testing of open large language models to check full-scene availability and conclude empirical principles for better steering large language models, particularly considering their black box and intelligence properties. Specifically, we focus on the model’s causal reasoning ability, which is the core of artificial intelligence but almost ignored by most previous work. First, for comprehensive evaluation, we deconstruct the causal reasoning capability into five dimensions and summary the forms of causal reasoning task as causality identification and causality matching. Then, rich datasets are introduced and further modified to generate test cases along with different ability dimensions and task forms to improve the testing integrity. Moreover, we explore the ability boundary of open large language models in two usage modes: prompting and lightweight fine-tuning. Our work conducts comprehensive functional testing on the causal reasoning ability of open large language models, establishes benchmarks, and derives empirical insights for practical usage. The proposed testing solution can be transferred to other similar evaluation tasks as a general framework for large language models or their derivations.

With the dimension of steganalysis feature increases rapidly, ensemble steganalysis has become the trend, and its performance is greatly influenced by the selection of feature subspaces. In order to select feature subspaces more effectively to improve the performance of ensemble steganalysis, a feature subspace selection algorithm based on Fisher criterion is proposed. The proposed selection algorithm computes weight for each feature component according to its Fisher criterion value and a base probability value, then selects the feature components with the probabilities in proportion to their weights. When it is used to improve the ensemble steganalysis, the appropriate base probability value is searched by steps. Experimental results show that for J-UNIWARD (JPEG UNIversal WAvelet Relative Distortion) steganography, the proposed feature subspace selection algorithm can select more effective feature subspaces, and enhance the detection performance of GFR (Gabor Filter Residual) feature.

The model-based steganographic algorithm MB2 modified the blockiness after secret messages are embedded, which makes the existed detection algorithm based on border artifacts invalidate. By further researching on the embedding principle of MB2, this paper analyzes the coefficients alteration results of given stego image after re-embedding with maximum messages. Based on the conclusions, this paper proposes an evaluation method for the relativity between intra-block pixels, the approximately linear relationship between the evaluated value and embedding ratio is derived by experiments. Based on these, an embedding ratio estimation method to MB2 is proposed. Experimental results show that the proposed method can estimate the embedding ratio of MB2 effectively.