Disentangled self-attention neural network based on information sharing for click-through rate prediction.

With the exponential growth of network resources, recommendation systems have become successful at combating information overload. In intelligent recommendation systems, the prediction of click-through rates (CTR) plays a crucial role. Most CTR models employ a parallel network architecture to successfully capture explicit and implicit feature interactions. However, the existing models ignore two aspects. One limitation observed in most models is that they focus only on the interaction of paired term features, with no emphasis on modeling unary terms. The second issue is that most models input characteristics indiscriminately into parallel networks, resulting in network input oversharing. We propose a disentangled self-attention neural network based on information sharing (DSAN) for CTR prediction to simulate complex feature interactions. Firstly, an embedding layer transforms high-dimensional sparse features into low-dimensional dense matrices. Then, the disentangled multi-head self-attention learns the relationship between different features and is fed into a parallel network architecture. Finally, we set up a shared interaction layer to solve the problem of insufficient information sharing in parallel networks. Results from experiments conducted on two real-world datasets demonstrate that our proposed method surpasses existing methods in predictive accuracy.

Towards virtual machine scheduling research based on multi-decision AHP method in the cloud computing platform.

Virtual machine scheduling and resource allocation mechanism in the process of dynamic virtual machine consolidation is a promising access to alleviate the cloud data centers of prominent energy consumption and service level agreement violations with improvement in quality of service (QoS). In this article, we propose an efficient algorithm (AESVMP) based on the Analytic Hierarchy Process (AHP) for the virtual machine scheduling in accordance with the measure. Firstly, we take into consideration three key criteria including the host of power consumption, available resource and resource allocation balance ratio, in which the ratio can be calculated by the balance value between overall three-dimensional resource (CPU, RAM, BW) flat surface and resource allocation flat surface (when new migrated virtual machine (VM) consumed the targeted host's resource). Then, virtual machine placement decision is determined by the application of multi-criteria decision making techniques AHP embedded with the above-mentioned three criteria. Extensive experimental results based on the CloudSim emulator using 10 PlanetLab workloads demonstrate that the proposed approach can reduce the cloud data center of number of migration, service level agreement violation (SLAV), aggregate indicators of energy comsumption (ESV) by an average of 51.76%, 67.4%, 67.6% compared with the cutting-edge method LBVMP, which validates the effectiveness.

Activation and Role of NACHT, LRR, and PYD Domains-Containing Protein 3 Inflammasome in RNA Viral Infection.

NACHT, LRR, and PYD domains-containing protein 3 (NLRP3) inflammasome activation and effects during ribonucleic acid (RNA) viral infection are the focus of a wide range of research currently. Both the pathogen-associated molecule pattern derived from virions and intracellular stress molecules involved in the process of viral infection lead to activation of the NLRP3 inflammasome, which in turn triggers inflammatory responses for antiviral defense and tissue healing. However, aberrant activation of the NLRP3 inflammasome can instead support viral pathogenesis and promote disease progression. Here, we summarize and expound upon the recent literature describing the molecular mechanisms underlying the activation and effects of the NLRP3 inflammasome in RNA viral infection to highlight how it provides protection against RNA viral infection.