World Congress on Services [Services Society]

This study combines the technical progress of the DeepSeek large model with global digital trends. It systematically analyzes its innovative practices in technical architecture, industry applications, and ecosystem building. Using DeepSeek’s strong knowledge processing capabilities, we build a three-tier interconnected financial knowledge graph (“company - industry - macro”) and add a policy transmission path sub-graph. This helps achieve in-depth decoupling of multi-scale features. We adopt an innovative “DeepSeek-MoE + Temporal Convolutional Network (TCN)” hybrid architecture. It is specially optimized for the multi-scale features of financial data. Focusing on the financial securities field, we creatively build a multi-scale quantitative stock selection model based on DeepSeek. To address the multi-scale features of financial market data, we introduce innovations such as adaptive Hurst thresholds, policy event fusion factors, and high-frequency attention layers. These effectively overcome the shortcomings of traditional quantitative models in handling high-frequency noise and low-frequency trends. Empirical results show that this strategy achieves an annualized return of 29.3

This paper describes a new synthetic data generation pipeline called 3DGENie designed to generate 3D point clouds to train deep learning computer vision models. 3DGENie uses procedural layout generation to produce region layout trees. It then applies 3D scene construction and asset randomization to produce scenes populated with 3D assets. Synthetic sensors are placed in the virtual environment to simulate data capture from the 3D scenes as if monitored by real-world sensors. 3DGENie uses Nvidia Omniverse as its scene building platform and Pixar’s Universal Scene Description (USD) for 3D graphics representation to allow for seamless interchange across platforms. Our main application focuses on the generation of industrial car assembly lines, yet 3DGENie can be used across different applications. We conduct experiments to evaluate the generated 3D point clouds, using several deep learning semantic segmentation models. Results highlight the quality of our pipeline.

Data sharing benefits various aspects of people’s daily lives, while in most existing centralized sharing paradigms, data generators do not have the ownership or control over their data. There have been many recent works on decentralized data sharing that provide the data owners with full control over their generated data in a decentralized setting. However, besides guaranteeing the owners with more control over their data, record tracking for data requesters becomes challenging due to the decentralization attribute. To address this issue, we propose SecLog, an atomic-swap based secure logging for decentralized data sharing. We utilized the idea of atomic swap from existing works, modifying the triggered event when the access to the queried results is given to the data requester, a record will be automatically included on the blockchain. Simulation results indicate that our proposed protocol costs less gas compared to the original payment-based atomic swaps and our source code is available at: https://github.com/avocado-avery/SecLog .

This study investigates the efficacy of an immersive, constructivist-based virtual reality (VR) intervention for the preservation and transmission of intangible cultural heritage (ICH), focusing on the mythopoetic corpus of the Peirene Fountain in ancient Corinth. The Peirene Fountain embodies a nexus of tangible and intangible heritage, most notably the myth of the nymph Peirene and the taming of Pegasus by Bellerophon. Since the traditional ICH dissemination modalities (e.g., oral narration, static text) are inadequate for engaging contemporary, digitally literate audiences, a VR experience on the Virtual Education Collaborative System (VECOS) platform was designed, leveraging its no-code, 3D objects scan import, AI assisted narrative sequencing, real-time event tracking, and cross device accessibility to create a multi-user, problem-based learning environment. Guided by the Constructivist Theory of Learning, users’ prior knowledge is scaffolded and extended through interactive, puzzle-oriented tasks, anchored by 3D reconstructions of key artifacts that facilitate situated, social learning and cognitive apprenticeship. Early validation involved 37 master’s students of the “Science and Metaverse Technologies” program, whose unstructured observational data, like scale surveys (mean engagement = 4.2/5; comprehension = 3.8/5), and semi‐structured group interviews indicated high levels of immersion, narrative retention, and emergent collaborative behavior. Findings suggest that the constructivist VR framework in VECOS significantly enhanced user engagement, contextualization of mythic narratives, and sustainable ICH transmission. Moreover, the modular, scalable architecture of VECOS suggests broad applicability to diverse heritage contexts, while limitations, such as narrative pacing, multimodal feedback, and hardware accessibility, underscore avenues for iterative refinement and future research.

Digital trust management in modern times uses blockchain technology and cryptography for building trust through dependable security protocols which operate decentralization. Basic blockchain operations receive core security through cryptographic authentication methods which validate data parts and verify users before enabling mutual agreement mechanisms for unalterable data storage. An evaluation of blockchain operation cryptography needs to analyze the Ethereum network. Ethereum achieves decentralized transparent operations through cryptographic resources that consist of hash functions and elliptic curve cryptography and digital signatures as well as zero-knowledge proofs which operate as emerging protocols. Ethereum supports transaction approvals and identity safeguards because its cryptographic structure makes trustless operation of smart contracts automatic. Ethereum intends to develop Ethereum 2.0 features with zk-SNARKs along with BLS signatures as its vital technical bases for scalability and privacy system enhancements. Research on new cryptographic techniques will explore cryptographic process protection methods for Ethereum 2.0 since its cryptographic vulnerabilities remain unaddressed. The evaluation demonstrates that secure decentralized digital platforms generate equivalent worth from cryptographic and blockchain technologies.