GATCF: Graph Attention Collaborative Filtering for Reliable Blockchain Services Selection in BaaS

Blockchain technology is a decentralized ledger that allows the development of applications without the need for a trusted third party. As service-oriented computing continues to evolve, the concept of Blockchain as a Service (BaaS) has emerged, providing a simplified approach to building blockchain-based applications. The growing demand for blockchain services has resulted in numerous options with overlapping functionalities, making it difficult to select the most reliable ones for users. Choosing the best-trusted blockchain peers is a challenging task due to the sparsity of data caused by the multitude of available options. To address the aforementioned issues, we propose a novel collaborative filtering-based matrix completion model called Graph Attention Collaborative Filtering (GATCF), which leverages both graph attention and collaborative filtering techniques to recover the missing values in the data matrix effectively. By incorporating graph attention into the matrix completion process, GATCF can effectively capture the underlying dependencies and interactions between users or peers, and thus mitigate the data sparsity scenarios. We conduct extensive experiments on a large-scale dataset to assess our performance. Results show that our proposed method achieves higher recovery accuracy.

Microchip Health Monitoring System Using the FLL Circuit

Here a solution for a Microchip Health Monitoring (MHM) system using MTOL (Multi-Temperature Operational Life) reliability testing assessment data is proposed. The module monitors frequency degradation over time compared to lab tested data. Since trends in performance degradation in recently developed devices have transitioned from multiple failure mechanisms to a single dominant failure mechanism, development of the monitor is greatly simplified. The monitor uses a novel circuit customized to deliver optimum accuracy by combining the concepts of ring oscillator (RO) and phase locked loop (PLL) circuits. The modified circuit proposed is a new form of the frequency locked loop (FLL) circuit. We demonstrate that the collection of frequency degradation data from the ring circuits of each test produces Weibull distributions with steep slopes. This implies that the monitor can predict accurate end-of-life (EOL) predictions at early stages of chip degradations. The design of the microchip health monitoring system projected in this work can have great benefit in all systems using FPGA and ASIC devices.