Video-to-Music generation seeks to generate musically appropriate background music that enhances audiovisual immersion for videos. However, current approaches suffer from two critical limitations: 1) incomplete representation of video details, leading to weak alignment, and 2) inadequate temporal and rhythmic correspondence, particularly in achieving precise beat synchronization. To address the challenges, we propose Video Echoed in Music (VeM), a latent music diffusion that generates high-quality soundtracks with semantic, temporal, and rhythmic alignment for input videos. To capture video details comprehensively, VeM employs a hierarchical video parsing that acts as a music conductor, orchestrating multi-level information across modalities. Modality-specific encoders, coupled with a storyboard-guided cross-attention mechanism (SG-CAtt), integrate semantic cues while maintaining temporal coherence through position and duration encoding. For rhythmic precision, the frame-level transition-beat aligner and adapter (TB-As) dynamically synchronize visual scene transitions with music beats. We further contribute a novel video-music paired dataset sourced from e-commerce advertisements and video-sharing platforms, which imposes stricter transition-beat synchronization requirements. Meanwhile, we introduce novel metrics tailored to the task. Experimental results demonstrate superiority, particularly in semantic relevance and rhythmic precision.

Multi-layer image generation is a fundamental task that enables users to isolate, select, and edit specific image layers, thereby revolutionizing interactions with generative models. In this paper, we introduce the Anonymous Region Transformer (ART), which facilitates the direct generation of variable multi-layer transparent images based on a global text prompt and an anonymous region layout. Inspired by Schema theory1, this anonymous region layout allows the generative model to autonomously determine which set of visual tokens should align with which text tokens, which is in contrast to the previously dominant semantic layout for the image generation task. In addition, the layer-wise region crop mechanism, which only selects the visual tokens belonging to each anonymous region, significantly reduces attention computation costs and enables the efficient generation of images with numerous distinct layers (e.g., 50+). When compared to the full attention approach, our method is over 12 times faster and exhibits fewer layer conflicts. Furthermore, we propose a high-quality multi-layer transparent image autoencoder that supports the direct encoding and decoding of the transparency of variable multi-layer images in a joint manner. By enabling precise control and scalable layer generation, ART establishes a new paradigm for interactive content creation.

Although industry practices and policies related to the growth of the data factor market have been enhanced, there still remain challenges that need to be overcome, such as poor data quality, low data application, lack of basic systems (e.g. data property rights and transactions), and inadequate implementation of data security governance. Addressing these challenges requires efforts from both institutional construction and technological innovation. In addition, as the exploration of data elements continues to deepen, it is necessary to closely pay attention to the adaptability between data element systems and data technology innovation, adhere to the agile, iterative approach to continuously adjust and optimize, and maintain the balance between efficiency and fairness as well as between development and security.

Serving as the global Internet’s phonebook, the Domain Name Service (DNS) is utilized for translating human-friendly domain names into machine-readable IP addresses, which makes DNS essential to the operation of the Internet and heavily relied on by today’s almost all various online services. Note that in some cases, DNS could be exploited maliciously and involved into some unexpected scenarios such as phishing, spamming, and harmful content distributing. We refer to these kinds of exploitation as DNS Abuse. Over the past years, DNS Abuse has been suffering an explosive growth, and thus DNS Abuse mitigation has been drawing more and more researchers’ attentions. In this paper, we reexamine some current major efforts made in this area, and find out some potential concerns or limitations which could bring negative impact to their usability and reliability for worldwide adoption or deployment. In this context, we further propose an incentive and decentralized procedure by introducing a trusted notifier reporting framework based on blockchain, which could guarantee the usability and reliability of DNS Abuse mitigation more significantly for worldwide deployment.

In this paper, we propose a robust point set registration algorithm which combines correntropy and point-to-plane distance, which can register rigid point sets with noises and outliers. Firstly, as correntropy performs well in handling data with non-Gaussian noises, we introduce it to model rigid point set registration problem based on point-to-plane distance; Secondly, we propose an iterative algorithm to solve this problem, which repeats to compute correspondence and transformation parameters respectively in closed form solutions. Simulated experimental results demonstrate the high precision and robustness of the proposed algorithm. In addition, LiDAR based localization experiments on automated vehicle performs satisfactory for localization accuracy and time consumption.