As a core task in Intelligent Transportation Systems (ITS), traffic flow prediction is essential for resource allocation and real-time route planning. Effectively capturing complex temporal correlations and dynamic spatial dependencies in traffic flow data is critical yet challenging for accurate prediction. However, existing approaches are still limited by the insufficient capability for spatial-temporal pattern decoupling and the underutilization of frequency domain information. To address these issues, we propose a novel Frequency-Enhanced Dynamic Decoupling Graph Convolutional Network (FEDDGCN), which introduces a gated decoupling mechanism integrating temporal and spatial embeddings to decouple traffic flow into prominent periodic and perturbative component. It also achieves effective pattern separation by incorporating frequency domain analysis with Fourier filters. Furthermore, a dual-branch spatial-temporal learning module, employing a divide-and-conquer strategy, is designed to achieve separate modeling for the two distinct components. Specially, the dynamic graph convolution modules are utilized to learn spatial dependencies and temporal and frequency attention mechanisms further capture complex temporal correlations for prominent periodic and perturbative components.Extensive experiments on multiple real-world datasets demonstrate that FEDDGCN achieves superior predictive performance compared with state-of-the-art methods.

Textbook reading has long dominated classical poetry education in Chinese-speaking communities. However, research has shown that extensive text-based learning can lead to learner disengagement and a less pleasant experience. This paper aims to improve the experience of classical Chinese poetry learning by introducing LivePoem—a system that generates musical storyboards (storyboards with background music) as audiovisual aids to support poetry comprehension. We employ a pre-trained diffusion model for storyboard generation and train a prosody-based poem-to-melody generator using a Transformer model, both validated by standard objective metrics to ensure generation quality. Through a within-subjects study involving 25 non-native Chinese learners, we compared learning outcomes from textbook reading and musical storyboard viewing through standardised reading comprehension tests. Additionally, the learning experience was assessed by the Self-Assessment Manikin (SAM) and an inductive thematic analysis of learners' open-ended feedback. Experimental results show that musical storyboards retained the learning outcomes of textbooks, while more effectively engaging learners and providing a more pleasant learning experience.

We present a unified framework for automatic multitrack music arrangement that enables a single pre-trained symbolic music model to handle diverse arrangement scenarios, including reinterpretation, simplification, and additive generation. At its core is a segment-level reconstruction objective operating on token-level disentangled content and style, allowing for flexible any-to-any instrumentation transformations at inference time. To support track-wise modeling, we introduce REMI-z, a structured tokenization scheme for multitrack symbolic music that enhances modeling efficiency and effectiveness for both arrangement tasks and unconditional generation. Our method outperforms task-specific state-of-the-art models on representative tasks in different arrangement scenarios---band arrangement, piano reduction, and drum arrangement, in both objective metrics and perceptual evaluations. Taken together, our framework demonstrates strong generality and suggests broader applicability in symbolic music-to-music transformation.

As a crucial representation of 3D data, a point cloud (PC) can accurately capture the geometry, structure, and color information of objects. However, various quality problems arise owing to device noise, data acquisition errors, and compression algorithms, limiting the application of PCs. Therefore, assessing PC quality to determine its suitability for applications is a challenging task. In this work, a local and global structure-guided feature extraction and attention network (LGS-Net) is introduced for no-reference PC quality assessment (PCQA). This approach incorporates cluster construction (CC), local structure-guided cluster feature extraction (LSFE), and global structure-guided attention (GSA) modules. First, owing to the heightened sensitivity of the human visual system (HVS) to structural information, a graph filter is employed to identify high-frequency clusters. Within the LSFE module, a multiscale strategy is employed to ensure that structural information effectively influences both the geometry and color information. Simultaneously, the multiscale features within the cluster are dynamically fine-tuned using feature channel weight reassignment. To account for the impact of interclusters on overall quality, a GSA module is introduced to establish global dependencies between local clusters. This approach enables the extraction of final geometry, color, and structure information, which are ultimately used for accurate quality assessment. Extensive experimental results show that the proposed method outperforms the existing state-of-the-art PCQA methods using two publicly available subjective datasets.

Sketch-based melody creation systems enable people to compose melodies by converting human-sketched melody contours into coherent melodies that fit the depicted contours. This remains one of the most intuitive approaches to interactive music creation. However, previous studies are still stagnating in limitations regarding usability and interpretability, which hinders effective interactions between people and AI. For one thing, these studies entail additional complex musical conditions as auxiliary inputs (e.g. chord progressions, contextual melodies, and predetermined rhythms), supporting only fixed-length and rule-based melody generation. This makes existing systems less usable, with generated melodies lacking diversity and coherence. Moreover, users without enough musical expertise might find it difficult to define appropriate inputs and to interpret the role of these inputs in guiding melody generation. To address these limitations, we present Drawlody, a novel sketch-based melody creation system with enhanced usability and interpretability. Specifically, Drawlody simplifies user input requirements by excluding all complex musical conditions, using only a simplified melody contour representation named Generalised Melody Contour (GMC) as input. This simplification clarifies the role of user controls, making the system more usable for people without musical training. To guide coherent melody generation from GMC, we propose FlexMIDI music representation, which simulates the tonal structure of melodies and faithfully explains how human-sketched contours guide melody generation. We employ a CNN-Transformer-based architecture as the foundation model to achieve arbitrary-length melody generation. Drawlody is evaluated by both objective and subjective music quality studies, as well as a usability and interpretability study. The results support its enhanced usability, interpretability, and high-quality melody generation capabilities. Video demos of the system are presentedhere.

Creativity Support Tools (CSTs) aid in the efficient and effective composition of creative content, such as picture books. However, many existing CSTs only allow for mono-modal creation, whereas previous studies have become theoretically and technically mature to support multi-modal innovative creations. To overcome this limitation, we introduce XCreation, a novel CST that leverages generative AI to support cross-modal storybook creation. Nevertheless, directly deploying AI models to CSTs can still be problematic as they are mostly black-box architectures that are not comprehensible to human users. Therefore, we integrate an interpretable entity-relation graph to intuitively represent picture elements and their relations, improving the usability of the underlying generative structures. Our between-subject user study demonstrates that XCreation supports continuous plot creation with increased creativity, controllability, usability, and interpretability. XCreation is applicable to various scenarios, including interactive storytelling and picture book creation, thanks to its multimodal nature.

Real-time music accompaniment generation has a wide range of applications in the music industry, such as music education and live performances. However, automatic real-time music accompaniment generation is still understudied and often faces a trade-off between logical latency and exposure bias. In this paper, we propose SongDriver, a real-time music accompaniment generation system without logical latency nor exposure bias. Specifically, SongDriver divides one accompaniment generation task into two phases: 1) The arrangement phase, where a Transformer model first arranges chords for input melodies in real-time, and caches the chords for the next phase instead of playing them out. 2) The prediction phase, where a CRF model generates playable multi-track accompaniments for the coming melodies based on previously cached chords. With this two-phase strategy, SongDriver directly generates the accompaniment for the upcoming melody, achieving zero logical latency. Furthermore, when predicting chords for a timestep, SongDriver refers to the cached chords from the first phase rather than its previous predictions, which avoids the exposure bias problem. Since the input length is often constrained under real-time conditions, another potential problem is the loss of long-term sequential information. To make up for this disadvantage, we extract four musical features from a long-term music piece before the current time step as global information. In the experiment, we train SongDriver on some open-source datasets and an original àiMusic Dataset built from Chinese-style modern pop music sheets. The results show that SongDriver outperforms existing SOTA (state-of-the-art) models on both objective and subjective metrics, meanwhile significantly reducing the physical latency.