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Bike-sharing is a powerful solution to urban challenges (e.g., expanding bike communities, lowering transportation costs, alleviating traffic congestion, reducing emissions, and enhancing health). Accurately predicting bike-sharing demand not only ensures the system meets community needs but also optimizes resource allocation, reduces operational costs, and enhances the user experience, thereby increasing the system's sustainability and city-wide benefits. However, prediction is complicated in low-computing environments with insufficient data due to privacy regulations or policy constraints. This study proposes an online learning-based two-stage forecasting model based on a low-computing environment with insufficient data for robust, fast multistep-ahead prediction for bike-sharing demand in Seoul. The model was applied with exploratory data analysis (EDA) to the Seoul Bike-sharing Demand dataset, split into insufficient training and sufficient testing sets. First, we generated prediction values for the random forest, extreme gradient boosting, and Cubist methods in training and testing. Second, we used the Ranger package trained with external factors and prediction values using time-series cross-validation for multistep-ahead prediction 1 h to 1 day later. We compared the model performance with that of 23 machine and deep learning models to verify its superiority. Using interpretability methods and EDA, we reported the relationships between external factors and bike-sharing demand. [ABSTRACT FROM AUTHOR]

Blockchain is a data storage technology characterized by stability, security, transparency and decentralization. It is classified into public and private according to the nature and scope of participating members and the right to participate in proof of work. In addition, the data stored in the block cannot be arbitrarily modified, and it is recognized for its security and convenience as a public ledger that anyone can read. Smart Contract also applies next-generation trading technology in a variety of areas including electronic voting, logistics management and currency trading. In this paper, we propose data management to improve security based on blockchain. [ABSTRACT FROM AUTHOR]

High performance computers should be performing the high-level computations for various research fields. Then, it is important to enhancing the system performance with statistical approaches and data mining technologies. This paper should analyze predictable correlations from system profiling data. Our approach is based on PCA algorithm, it is determined proper clusters for the performance indices. Experimental analysis suggests the correlation between performance indices, they could suggest decision supports for scale-out architecture in HPC environments. [ABSTRACT FROM AUTHOR]

High-performance computing (HPC) systems provide huge computational resources and large memories. The hybrid memory is a promising memory technology that contains different types of memory devices, which have different characteristics regarding access time, retention time, and capacity. However, the increasing performance and employing hybrid memories induce more complexity as well. In this paper, we propose a data migration methodology called HyDM to effectively use hybrid memories targeting at Intel Knight Landing (KNL) processor. HyDM monitors status of applications running on a system and migrates pages of selected applications to the High Bandwidth Memory (HBM). To select appropriate applications on system runtime, we adopt the roofline performance model, a visually intuitive method. HyDM also employs a feedback mechanism to change the target application dynamically. Experimental results show that our HyDM improves over the baseline execution the execution time by up to 22%. [ABSTRACT FROM AUTHOR]