This study will assess how effective the GAM is for solving scalability and efficiency issues in multi-chain data sharing. Using qualitative research methods, the five sub-questions answered deal with some of the shortcomings of models currently in place, potential improvements in terms of scalability and efficiency, hybrid storage options, and whether GAM really applies to the real world. Thematic analysis of blockchain transaction logs and user feedback provides the following key findings: GAM significantly enhances scalability via virtual group formation, optimizes efficiency through on-chain and off-chain processes, and demonstrates practical advantages with hybrid storage. This study concludes that GAM indeed offers robust solutions to problems in multi-chain data sharing, outperforming traditional models. However, it needs further testing across diverse blockchain environments and integration with emerging blockchain technologies for its broader adoption.
This paper studies the challenges and progressions of Legal Judgment Prediction, in particular on the grounds of improving efficiency, accuracy, and fairness in judicial systems. The study examines five sub-research questions: the limitation of existing LJP methods, the role of factual logic in judgment reasoning, the integration of external legal knowledge, the effectiveness of a chain prompt reasoning module, and the impact of contrastive knowledge fusion on long-tail cases. A qualitative research methodology is followed to design and validate an interpretable framework for LJP, featuring a chain prompt reasoning module to strengthen factual logic and a contrastive knowledge fusing module to incorporate external legal knowledge. Results indicate notable improvements in the prediction accuracy, interpretability, and handling of complex long-tail cases. Despite the specific datasets used, the proposed framework is a demonstration of its potential in wider applications and theoretical contributions to legal AI. Future work will be based on diverse data sources and methodologies to generalize and improve these findings.
This research delves into the challenges and advances of few-shot multi-hop reasoning for Temporal Knowledge Graphs (TKGs), particularly in the combination of reinforcement learning and path search strategies. The central research question investigates the efficiency of a new few-shot multi-hop reasoning model called TFSM, which employs reinforcement learning for TKGs. The study addresses five sub-research questions on the issues of model interpretability, entity representation, path search strategy, comparative performance, and the contribution of individual model components. A quantitative methodology has been used in this work, using datasets such as ICEWS18-few, ICEWS14-few, and GDELT-few to analyze the performance of the TFSM model. Results. It shows that reinforcement learning considerably enhances interpretability, one-hop neighbors improve the entity representation, path search strategies decrease node complexity, and TFSM outperforms baseline methods in few-shot scenarios. This work contributes to the advancement of knowledge on few-shot reasoning in TKGs and presents further research directions for improving model components and broadening application.
This paper introduces a new approach in speech compression through advanced attention mechanisms, integration of LSTM, and dual-branch conformer structures for optimizing codec efficiency. The study focuses on five research questions, which are: intra-inter broad attention, multi-head attention networks, LSTM for sequence modeling, redundancy elimination, and comparative performance of IBACodec against traditional codecs. The study uses a quantitative methodology with performance metrics that include bitrate efficiency and quality evaluation. Results confirm that IBACodec significantly enhances context awareness, compression efficiency, sequence modeling, and redundancy elimination compared to existing solutions. These findings position IBACodec as a leading solution for speech compression. Further research is needed to explore real-time applications and broader datasets.
This study looks into the role blockchain technology can play in improving the safe sharing of data between various parties in emergency management. It seeks to explore how blockchain, coupled with attribute-based access control, can address the issues in centralized data platforms and transform the nature of decentralized data governance. The five core research questions focus on improving data security and privacy, the role of ABAC in dynamic policy adaptation, data sharing efficiencies and reliability, minimizing risks of single-point failures and unauthorized access, and the feasibility of using Hyperledger Fabric for real-world applications. By applying a quantitative research approach, the study assesses how blockchain and ABAC can influence data security, policy adaptation, and system reliability in emergency management. The results are confirmed in terms of significant improvements in data security due to blockchain; the ease of dynamic policy adaptation; better efficiency in sharing data; risk mitigation; and validation of the feasibility of using Hyperledger Fabric for applications in emergency management. This research contributes to the understanding of decentralized data governance and underscores the importance of blockchain in enhancing emergency response systems.