media streaming services

Abstract: Cross-domain sequential recommendation (CDSR) alleviates data sparsity by transferring user behaviors across domains. Recent advances increasingly leverage large language models (LLMs) to bridge domains through global item semantics. However, this paradigm is inherently limited by two issues: I. semantic domain isolation, where item semantics remain domain-specific and hinder transferable modeling, and II. semantic-behavior inconsistency, where local semantic relations are more consistent with behavioral transitions, whereas such consistency often breaks down globally across domains. To this end, we propose PerCDR, an LLM-enhanced CDSR framework that shifts the focus from global semantic bridging to persona-guided behavioral modeling. Specifically, PerCDR leverages LLM-inferred personas to globally guide transferable cross-domain behavioral patterns, while exploiting local semantic relations to enhance intra-domain behavioral modeling, followed by domain preference adaptation for recommendation refinement. Extensive experiments demonstrate the superiority of PerCDR and validate its effectiveness.

Abstract: In decentralized storage systems, access control is often implemented by encrypting the data before upload and sharing the decryption key with authorized parties. A leaked key, however, makes the data publicly accessible, which lowers the barrier to content piracy below that of traditional systems, where piracy requires redistributing the full data. We present Physalia, an end-to-end access-control system for decentralized storage that secret-shares the data itself, instead of just the key, across multiple servers. Leaking the data then requires transmitting it in full: We formalize this intuition and introduce the redistribution bandwidth an adversary must pay to leak protected content and show that Physalia raises it to the size of the data. Sharing across untrusted servers requires robustness against corrupted shares. We develop a robustness transform that turns any computational secret sharing scheme into a robust one and which is of independent interest. In contrast to existing schemes that rely on error correction, it uses signatures with ephemeral keys and adds only constant-size metadata per share. We show that this transform is secure and evaluate Physalia end-to-end on the Walrus decentralized storage system with an on-chain access policy.

Abstract: Residual Vector Quantization (RVQ)-based neural audio codecs (NACs) enable high-fidelity audio distribution at unprecedentedly low bitrates through discrete token-based representations. However, this shift disrupts traditional forensics, as non-linear neural transcoding obscures the underlying traces of legacy compression. This study defines the forensic gap and proposes a Transformer-based framework designed to leverage the hierarchical and temporal dependencies inherent in RVQ sequences. By modeling inter-layer causal relationships and dynamic forensic significance, our model effectively disentangles superimposed artifacts from legacy-to-neural transcoding. Experimental results achieve 97%+ accuracy for codec identification and robust joint identification performance across 32-128 kbps. These results demonstrate that traditional codec traces persist even after neural transcoding, supporting the feasibility and necessity of neural-codec-aware audio forensics.