The Fermatean fuzzy set (FFS) is a momentous generalization of a intuitionistic fuzzy set and a Pythagorean fuzzy set that can more accurately portray the complex vague information of elements and has stronger expert flexibility during decision analysis. The Combined Compromise Solution (CoCoSo) approach is a powerful decision-making technique to choose the ideal objective by fusing three aggregation strategies. In this paper, an integrated, multi-criteria group-decision-making (MCGDM) approach based on CoCoSo and FFS is used to assess green suppliers. To begin, several innovative operations of Fermatean fuzzy numbers based on Schweizer–Sklar norms are presented, and four aggregation operators utilizing the proposed operations are also developed. Several worthwhile properties of the advanced operations and operators are explored in detail. Next, a new Fermatean fuzzy entropy measure is propounded to determine the combined weight of criteria, in which the subjective and objective weights are computed by an improved best-and-worst method (BWM) and entropy weight approach, respectively. Furthermore, MCGDM based on CoCoSo and BWM-Entropy is brought forward and employed to sort diverse green suppliers. Lastly, the usefulness and effectiveness of the presented methodology is validated by comparison, and the stability of the developed MCGDM approach is shown by sensitivity analysis. The results shows that the introduced method is more stable during ranking of green suppliers, and the comparative results expound that the proposed method has higher universality and credibility than prior Fermatean fuzzy approaches.

As the scale is expanding, node failure becomes a commonplace feature of large-scale cluster systems. As an important part of cluster operating system software, job scheduling takes charge with high efficient resource management and reasonable job scheduling. The function of job scheduling in cluster is divided into two sub-parts: job selection and node allocation. In this paper, we introduce a failure-aware scheduling strategy named LUNF (Longest Uptime Node First) node allocation policy using characterization of nodes' failure. Simulation results show that LUNF policy do better than random node allocation policy for the system performance.

In this paper, a neural network model is developed to predict the first-day closing price of Chinese SME stock market IPOs. We try to predict the extraordinary Chinese IPOs first-day return by using a set of financial variables which is different from these existed research literatures considering the particularity of Chinese SME stock market issue mechanism and pricing method. We first pre-process the original data in order to eliminate the influence of different scale. Then we use principal component analysis to perform data reduction among all the post-processed data. Then we construct a neural network to predict the underpricing based on publicly available data in prospectus. The prediction result shows that it will be helpful to the investors' decision making.

Nowadays Hadoop has become a leading architecture for large-scale data processing. One of the efficient ways to accelerate data processing is column-oriented storage technique which has been integrated into Hadoop family recently. However, how to design an appropriate attribute clustering algorithm to achieve optimal data processing performance for column-oriented hadoop system is still a big problem. In this paper, we propose a novel algorithm called CHAC to solve this problem. Both cases of overlapping attribute cluster and non-overlapping attribute cluster are considered in CHAC. In addition, an adjustable parameter is also taken into account to prohibit excessive attribute redundancy via limiting space overhead. The experimental results on TPC-H Benchmark demonstrate the efficiency and effectiveness of the proposed algorithm.

Cache side channel attacks obtain victim cache line access footprint to infer security-critical information. Among them, cross-core attacks exploiting the shared last level cache are more threatening as their simplicity to set up and high capacity. Stateful approaches of detection-based mitigation observe precise cache behaviors and protect specific cache lines that are suspected of being attacked. However, their recording structures incur large storage overhead and are vulnerable to reverse engineering attacks. Exploring the intrinsic non-determinate layout of a traditional Cuckoo filter, this paper proposes a space efficient Auto-Cuckoo filter to record access footprints, which succeed to decrease storage overhead and resist reverse engineering attacks at the same time. With Auto-Cuckoo filter, we propose PiPoMonitor to detect Ping-Pong patterns and prefetch specific cache line to interfere with adversaries' cache probes. Security analysis shows the PiPoMonitor can effectively mitigate cross-core attacks and the Auto-Cuckoo filter is immune to reverse engineering attacks. Evaluation results indicate PiPoMonitor has negligible impact on performance and the storage overhead is only 0.37%, an order of magnitude lower than previous stateful approaches.