Information about the concordance between dynamic emotional experiences and objective signals is practically useful. Previous studies have shown that valence dynamics can be estimated by recording electrical activity from muscles in brows cheeks. However, whether facial actions based on video data analyzed without electrodes used for sensing emotion remains unknown. We investigated this issue of participants' faces obtaining arousal ratings while they observed films. Action units (AUs) 04...

In this paper, we study parallel data access on distributed file systems, e.g, the Hadoop system. Our experiments show that read requests are often served remotely and in an imbalanced fashion. This results a serious disk transfer contention certain cluster/storage nodes. We conduct complete analysis how remote patterns occur they affected by size of cluster. then propose novel method to Optimize Parallel Data Access Distributed File Systems referred as Opass. The goal Opass is reduce...

Data-driven machine learning approaches with precise predictive capabilities are proposed to address the long-standing challenges in calculation of complex many-electron atomic systems, including high computational costs and limited accuracy. In this work, we develop a general workflow for learning-assisted structure calculations based on Cowan code's Hartree-Fock relativistic corrections (HFR) theory. The incorporates enhanced ElasticNet XGBoost algorithms, refined using entropy weight...

We propose using multiple observed features of network traffic to identify new high-distributed low-rate quality services (QoS) violation so that detection accuracy may be further improved. For the features, we choose F feature in TCP packet header as a microscopic and, P and D macroscopic features. Based on these establish multistream fused hidden Markov model (MF-HMM) detect stealthy denial service (LDoS) attacks legitimate background traffic. In addition, threshold value is dynamically...

Whereas traditional scientific applications are computationally intensive, recent require more data-intensive analysis and visualization. As the computational power size of compute clusters continue to increase, I/O read rates associated network cost for these create a serious performance bottleneck when faced with massive data sets today's "big data" era.

Recent years have witnessed an increasing demand for super data clusters. The clusters reached the petabyte-scale that can consist of thousands or tens storage nodes at a single site. For this architecture, reliability is becoming great concern. In order to achieve high reliability, recovery and node reconstruction must. Although extensive research works investigated how sustain performance in case failures large scale, reverse lookup problem, namely finding objects list failed remains open....

Emerging data-intensive applications are creating non-uniform CPU and I/O workloads which impose the requirement to consider both effects in power management strategies. Current approaches focus on scaling down frequency based busy/idle ratio without taking into consideration. Therefore, they do not fully exploit opportunities conservation. In this paper, we propose a novel scheme called model-free, adaptive, rule-based (MAR) multiprocessor systems minimize consumption subject performance...

Noisy Intermediate-Scale Quantum (NISQ) computers face a critical limitation in qubit numbers, hindering their progression towards large-scale and fault-tolerant quantum computing. A significant challenge impeding scaling is crosstalk, characterized by unwanted interactions among neighboring components on chips, including qubits, resonators, substrate. We motivate general approach to systematically resolving multifaceted crosstalks limited substrate area. propose Qplacer, frequency-aware...

In the noisy intermediate scale quantum (NISQ) era, Variational Quantum Algorithm (VQA) has emerged as one of most promising approaches to harness power computers. VQA, a classical optimizer iteratively updates parameters variational circuit minimize cost objective obtained by executing on real hardware. However, deployment VQA applications NISQ devices encounters substantial noise, which degrades training stability. Moreover, drift noise is particularly intractable due its dynamic nature in...

Named entity recognition is a fundamental and important task in the field of natural language processing. The effect existing solutions practical applications do not meet need people. Firstly, this paper studies scheme recurrent neural networks with conditional random fields. Further, series experiments were performed on input word vector dimension, number nodes within network, variant structure networks. Finally, method combining network attention mechanism proposed. An layer built over...

With the increasing size of clusters as well capacity each storage node, current systems are spending more time on recovery. When node failure happens, system enters degradation mode in which reconstruction/block recovery is initiated. This very process needs to wake up a number disks and takes substantial amount I/O bandwidth will not only compromise energy efficiency but also performance. raises natural problem: how balance performance, energy, for an efficient system? Without considering...

With the increasing popularity of cloud computing, current data centers contain petabytes in their datacenters. This requires thousands or tens storage nodes at a single site. Node failure these datacenters is normal instead rare situation. As result, reliability great concern. In order to achieve high reliability, recovery node reconstruction must. Although extensive research works have investigated how sustain performance and case large scale, reverse lookup problem, namely finding list...

To run search tasks in a parallel and load-balanced fashion, existing BLAST schemes such as mpiBLAST introduce data initialization preparation stage to move database fragments from the shared storage local cluster nodes. Unfortunately, quickly growing sequence becomes too heavy network today's big era.

State Grid Corporation proposed to build a "three-type two-network, world-class" strategic goal, of which the construction ubiquitous electric Internet Things (E-IoT) is an important content and key link future smart improvement. This paper builds industry IoT system using 230MHz band. The frequency band used by described in detail. 4.5G wireless access network plan test interference, multi-base station networking scenario, security, service docking stability projects near Nanrui. results...

Power management is becoming very important in data centers. Cloud computing also one of the newest promising center techniques which appealing to many big companies. As cloud different from current centers terms power due a dynamic structure and property for its online service. budgeting, role management, provides powerful solutions with capabilities. To be specific, existing methods are based on distribution units (PDU) divided by fixed locations physical levels. However, it not suitable...