The rise of 6G-enabled Internet of Things (IoT) environments opens new frontiers for deploying intelligent music generation systems that must operate in real-time, across heterogeneous devices, and under strict resource constraints. Traditional symbolic music generation (SMG) methods are often manually designed without considering the complex and dynamic requirements of 6G networks, such as ultra-low latency, high connectivity, and resource efficiency. These limitations result in unreliable performance and poor adaptability to diverse edge devices. To address these challenges, we propose a novel evolutionary multi-objective framework for the automatic design of Transformer-based architectures tailored for 6G IoT environments. Unlike previous methods, our approach incorporates trustworthiness as an optimization objective alongside generation quality and inference latency. This ensures that the generated music is both coherent and reliable, even under varying network conditions. Furthermore, we introduce a population memory mechanism that reuses architectural encodings to reduce computational costs and improve search efficiency. We conduct both objective and qualitative evaluations on the POP909 dataset, demonstrating that the discovered architectures produce musically coherent and stylistically faithful compositions. Additionally, real-world deployment on 6G-compatible edge hardware shows that our architectures perform efficiently under constrained resources, exhibiting excellent adaptability across different devices. Our framework bridges the gap between high-quality SMG and trustworthy real-time deployment in 6G IoT environments, providing a scalable path toward next-generation intelligent music services.

Abstract Recovering spatially‐varying materials from a single photograph of a surface is inherently ill‐posed, making the direct application of a gradient descent on the reflectance parameters prone to poor minima. Recent methods leverage deep learning either by directly regressing reflectance parameters using feed‐forward neural networks or by learning a latent space of SVBRDFs using encoder‐decoder or generative adversarial networks followed by a gradient‐based optimization in latent space. The former is fast but does not account for the likelihood of the prediction, i.e., how well the resulting reflectance explains the input image. The latter provides a strong prior on the space of spatially‐varying materials, but this prior can hinder the reconstruction of images that are too different from the training data. Our method combines the strengths of both approaches. We optimize reflectance parameters to best reconstruct the input image using a recurrent neural network, which iteratively predicts how to update the reflectance parameters given the gradient of the reconstruction likelihood. By combining a learned prior with a likelihood measure, our approach provides a maximum a posteriori estimate of the SVBRDF. Our evaluation shows that this learned gradient‐descent method achieves state‐of‐the‐art performance for SVBRDF estimation on synthetic and real images.

Money laundering (ML) poses a severe threat to financial stability and social security. Various money laundering detection methods have emerged in the past two decades. Among these methods, some semi-supervised ones based on graph neural networks (GNNs) have achieved impressive performance. However, the homogeneity hypothesis of GNN-based methods does not fit the ML detection scenario, affecting the detection performance. This paper presents a semi-supervised money laundering detection framework based on decoupling training (MLaD2). MLaD2 constructs a transaction relationship network based on node similarity (TRNNS) to model account interactions. Performing on TRNNS, MLaD2 learns the representation of accounts using a GNN. The weighting mechanism of TRNNS can overcome the drawback of the homogeneity hypothesis. Based on the learned account representations, MLaD2 adopts a decoupling training mechanism to build an ML accounts detection model, reducing its dependence on annotated data. The pre-training phase of the decoupling training employs a contrastive self-supervised learning model to learn the intrinsic characteristics of accounts. The fine-tuning phase extracts discriminative features between ML accounts and benign accounts with labeled data. Comprehensive evaluations and comparisons on a real-world ML dataset demonstrate that MLaD2 yields results that surpass existing methods, especially when training with a small scale of labeled samples.

Money laundering is a significant problem in the financial system and provides the conditions for financing various crimes. Previous methods apply many flexible algorithms, such as machine learning, graph mining, and anomaly detection. However, most of these contemporary methods do not adequately consider the dynamic characteristics of transactions, which may contain discriminative information for money laundering detection. To address this issue, in this paper, we propose a dynamic transaction pattern aggregation neural network (DTPAN) for money laundering detection. DTPAN utilizes two feature extractors to learn the dynamic features of transaction behaviors and the evolution of transfer relationships between accounts. Furthermore, it employs a feature enhancement module to enhance the behavior dynamic features, capturing the latent dependency between behavior dynamic and relationship evolution. Experimental results obtained with a real-world dataset demonstrate the effectiveness of DTPAN. The results also reveal that DTPAN can enhance the performance of ML detection by adequately exploring the dynamic information of transactions.

Abstract Texture is a key characteristic in the definition of the physical appearance of an object and a crucial element in the creation process of 3D artists. However, retrieving a texture that matches an intended look from an image collection is difficult. Contrary to most photo collections, for which object recognition has proven quite useful, syntactic descriptions of texture characteristics is not straightforward, and even creating appropriate metadata is a very difficult task. In this paper, we propose a system to help explore large unlabeled collections of texture images. The key insight is that spatially grouping textures sharing similar features can simplify navigation. Our system uses a pre‐trained convolutional neural network to extract high‐level semantic image features, which are then mapped to a 2‐dimensional location using an adaptation of t‐SNE, a dimensionality‐reduction technique. We describe an interface to visualize and explore the resulting distribution and provide a series of enhanced navigation tools, our prioritized t‐SNE, scalable clustering, and multi‐resolution embedding, to further facilitate exploration and retrieval tasks. Finally, we also present the results of a user evaluation that demonstrates the effectiveness of our solution.

Motion blur in a photo is the consequence of object motion during the image acquisition. It results in a visible trail along the motion of a recorded object and can be used by photographers to convey a sense of motion. Nevertheless, it is very challenging to acquire this effect as intended and requires much experience from the photographer. To achieve actual control over the motion blur, one could be added in a post process but current solutions require complex manual intervention and can lead to artifacts that mix moving and static objects incorrectly. In this paper, we propose a novel method to add motion blur to a single image that generates the illusion of a photographed motion. Relying on a minimal user input, a filtering process is employed to produce a virtual motion effect. It carefully handles object boundaries to avoid artifacts produced by standard filtering methods. We illustrate the effectiveness of our solution with various complex examples, including multi-directional blur, reflections, multiple objects, and illustrate how several motion-related artistic effects can be achieved. Our post-processing solution is an alternative to capturing the intended real-world motion blur directly and enables fine-grained control of the motion-blur effect.