Graph Neural Networks (GNNs) increasingly follow the ''pre-training, adaptation'' paradigm, where a GNN is pre-trained on large-scale graphs and then adapted to downstream tasks. Graph prompting adapts to the frozen encoder by modifying the input graph structure, rather than fine-tuning the model parameters. However, existing graph prompting methods often rely on probabilistic rewiring and auxiliary regularizers to control sparsity, which makes the prompting process sensitive to hyperparameters and can introduce instability in few-shot settings. To address the issue, we propose ProtoCalib, a lightweight local graph prompt for few-shot adaptation of frozen GNNs. ProtoCalib uses prototypes from the support set to score candidate edges for each anchor node. It then calibrates these scores with a per-node edge budget, which keeps the prompted graph sparse and removes the need for extra sparsity or entropy losses. We further adopt a deterministic construction of the prompted adjacency to reduce sampling noise at inference time. Extensive experiments on five graph datasets under four pre-training strategies demonstrate that our proposed ProtoCalib outshines baselines on multiple node classification datasets.

Recommender Systems (RS) play a critical role in enhancing user experiences across online platforms by modeling user-item interactions as bipartite graphs. Predicting signed links in such graphs remains challenging due to the sparsity and complexity of sign distributions and the limitations of traditional methods like matrix factorization and Graph Convolutional Networks (GCNs), which often fail to capture the intricate local topological and sign-based patterns essential for accurate predictions. To address these challenges, we propose SMA-GNN, a framework specifically designed for signed link prediction in bipartite graphs. SMA-GNN combines Local Subgraph Extraction, Two-Anchor Distance Labeling (TADL), and a Symbol-aware Multi-head Attention Mechanism to enhance predictive capability and interpretability. By extracting a closed local subgraph around the target link, our method captures relevant topological and sign contexts. TADL refines this by assigning unique structural labels to nodes based on their proximity to anchor nodes, encapsulating roles and relationships. The symbol-aware attention mechanism integrates edge sign information into the message-passing process, generating highly discriminative subgraph embeddings. Experiments on benchmark datasets show that SMA-GNN outperforms global embedding methods in prediction accuracy and provides deeper insights into user-item interactions, enabling more precise and personalized recommendations. Our code is avilable at https://github.com/xiaohuzidefeijian/SMAGNN/tree/master

Multimodal models have demonstrated powerful capabilities in complex tasks requiring multimodal alignment, including zero-shot classification and cross-modal retrieval. However, existing models typically rely on millions of paired multimodal samples, which are prohibitively expensive or infeasible to obtain in many domains. In this work, we explore the feasibility of building multimodal models with limited amount of paired data by aligning pretrained unimodal foundation models. We show that high-quality alignment is possible with as few as tens of thousands of paired samples$\unicode{x2013}$less than 1\% of the data typically used in the field. To achieve this, we introduce STRUCTURE, an effective regularization technique that preserves the neighborhood geometry of the latent space of unimodal encoders. Additionally, we show that aligning last layers is often suboptimal and demonstrate the benefits of aligning the layers with the highest representational similarity across modalities. These two components can be readily incorporated into existing alignment methods, yielding substantial gains across 24 zero-shot image classification and retrieval benchmarks, with average relative improvement of 51.6\% in classification and 91.8\% in retrieval tasks. Our results highlight the effectiveness and broad applicability of our framework for limited-sample multimodal learning and offer a promising path forward for resource-constrained domains.

In many real-world applications, test data may commonly exhibit categorical shifts, characterized by the emergence of novel classes, as well as distribution shifts arising from feature distributions different from the ones the model was trained on. However, existing methods either discover novel classes in the open-world setting or assume domain shifts without the ability to discover novel classes. In this work, we consider a cross-domain open-world discovery setting, where the goal is to assign samples to seen classes and discover unseen classes under a domain shift. To address this challenging problem, we present CROW, a prototype-based approach that introduces a cluster-then-match strategy enabled by a well-structured representation space of foundation models. In this way, CROW discovers novel classes by robustly matching clusters with previously seen classes, followed by fine-tuning the representation space using an objective designed for cross-domain open-world discovery. Extensive experimental results on image classification benchmark datasets demonstrate that CROW outperforms alternative baselines, achieving an 8% average performance improvement across 75 experimental settings.

We present a practical and effective method for human action transfer. Given a sequence of source action and limited target information, we aim to transfer motion from source to target. Although recent works based on GAN or VAE achieved impressive results for action transfer in 2D, there still exists a lot of problems which cannot be avoided, such as distorted and discontinuous human body shape, blurry cloth texture and so on. In this paper, we try to solve these problems in a novel 3D viewpoint. On the one hand, we design a skeleton-to-3D-mesh generator to generate the 3D model, which achieves huge improvement on appearance reconstruction. Furthermore, we add a temporal connection to improve the smoothness of the model. On the other hand, instead of directly utilizing the image in RGB space, we transform the target appearance information into UV space for further pose transformation. Specially, unlike conventional graphics render method directly projects visible pixels to UV space, our transformation is according to pixel’s semantic information. We perform experiments on Human3.6M and HumanEva-I to evaluate the performance of pose generator. Both qualitative and quantitative results show that our method outperforms methods based on generation method in 2D. Additionally, we compare our render method with graphic methods on Human3.6M and People-snapshot. The comparison results show that our render method is more robust and effective.

Access control is an extremely important and error-prone practice during web application. The emergence of NoSQL databases and the flexible data models they bring impose new challenges on the implementation of access control within web applications. This paper presents Scout, a novel methodology for discovering access control vulnerabilities in existing web applications. Meanwhile (1) features of NoSQL database can be addressed and (2) neither application source code nor server-side session information from the developers is required. This paper implements a prototype of Scout, which targets MongoDB backend web applications. By automatically discovering the protocol layer in the web application stack, Scout introduces a data access operation model precisely representing the MongoDB actions performed in the web application, as well as inferring the access control policies. The prototype is shown to be able to identify comprehensive access control vulnerabilities in MongoDB backend web applications, and generate detailed report as the facilitator to manually fix the identified vulnerabilities.

Internet-scale software becomes an important mode of constructing software systems with the development of internet. Open, dynamic and uncontrollable Internet environment makes dependability evaluation of Internet-scale software very important. It is lack of a dependability evaluation model that analyzes system architecture from the most foundational elements and integrate aspects that impacts on the system, such as the technical, organizational, decisional and human aspects. This paper proposes an evaluation model of dependability for Internet-scale software on the basis of Bayesian Networks. The model analyzes the structure of Internet-scale software and establishes an evaluation system of dependability for Internet-scale software including static metrics, dynamic metrics, prior metrics and correction metrics. It integrates subjective and objective factors which impact on system quality. In this paper, we build Bayesian Network according to the structure analysis and refer to a bottom-up method that use Bayesian reasoning to analyses and calculate entity dependability and integration dependability layer by layer. A unified dependability of the whole system is worked out and is corrected by objective data. The analysis of experiment in a real system proves that the model in this paper is capable of evaluating the dependability of Internet-scale software clearly and objectively. Moreover, it offers effective help to the design, development, deployment and assessment of Internet-scale software.