基于灰色模糊层次分析法的企业云服务安全评价模型

从企业实施云服务项目角度,总结得出影响云服务安全的四大因素,即平台设施、运行安全、运营管理以及法律法规因素。在此基础上构建企业云服务安全评价模型,并利用灰色模糊层次分析法（ GFAHP）对模型进行综合评价。最后通过一个实例进行具体阐明与评价。     

车联网中基于雾计算和多TA的条件隐私保护认证方案

车联网在生活中扮演着越来越重要的角色,它可以有效地防止交通拥堵从而减少交通事故。然而,在车联网中总是有非法车辆试图接入车联网并发布虚假消息。此外,现有方案多数存在计算效率低下的问题。针对上述存在的问题进行了研究,提出了一种车联网中基于雾计算和多TA的条件隐私保护认证方案。在保护车辆用户身份的条件下实现了车辆、雾节点、TA三者之间的身份认证,且在车辆追踪阶段可以还原车辆用户的真实身份,从而实现条件隐私保护。雾计算的使用降低了方案的计算和通信开销,同时多TA模型的使用也解决了单TA单点故障的问题。安全性证明和性能分析的结果表明该方案是安全且高效的。最后对当前方案进行了总结以及对未来研究作出了展望。     

Unikernel的研究及其进展

Unikernel是一种专门定制、优化的轻量级虚拟机,在云环境中,它能够解决由传统虚拟机带来的资源消耗大、低效率、低安全性的问题,有望成为超越Docker的下一代轻量级微服务系统架构。目前,Unikernel开源项目较多,技术呈现复杂性和多样性,使得研究人员和开发人员难以准确理解和把握。为此,首先介绍了Unikernel的基本概念和基本特点;然后总结出了Unikernel的系统架构,并详细阐述了Unikernel关键技术的研究工作进展,包括应用层、库操作系统层和平台适配层;最后结合已有的研究成果,探讨了Unikernel的研究方向及其面临的机遇和挑战。     

Fast fingerprint identification for large databases

Fingerprint matching has emerged as an effective tool for human recognition due to the uniqueness, universality and invariability of fingerprints. Many different approaches have been proposed in the literature to determine faithfully if two fingerprint images belong to the same person. Among them, minutiae-based matchers highlight as the most relevant techniques because of their discriminative capabilities, providing precise results. However, performing a fingerprint identification over a large database can be an inefficient task due to the lack of scalability and high computing times of fingerprint matching algorithms.In this paper, we propose a distributed framework for fingerprint matching to tackle large databases in a reasonable time. It provides a general scheme for any kind of matcher, so that its precision is preserved and its time of response can be reduced.To test the proposed system, we conduct an extensive study that involves both synthetic and captured fingerprint databases, which have different characteristics, analyzing the performance of three well-known minutiae-based matchers within the designed framework. With the available hardware resources, our distributed model is able to address up to 400 000 fingerprints in approximately half a second. Additional details are provided at http://sci2s.ugr.es/ParallelMatching.     

Towards adaptive learning with improved convergence of deep belief networks on graphics processing units

In this paper we focus on two complementary approaches to significantly decrease pre-training time of a deep belief network (DBN). First, we propose an adaptive step size technique to enhance the convergence of the contrastive divergence (CD) algorithm, thereby reducing the number of epochs to train the restricted Boltzmann machine (RBM) that supports the DBN infrastructure. Second, we present a highly scalable graphics processing unit (GPU) parallel implementation of the CD-k algorithm, which boosts notably the training speed. Additionally, extensive experiments are conducted on the MNIST and the HHreco databases. The results suggest that the maximum useful depth of a DBN is related to the number and quality of the training samples. Moreover, it was found that the lower-level layer plays a fundamental role for building successful DBN models. Furthermore, the results contradict the pre-conceived idea that all the layers should be pre-trained. Finally, it is shown that by incorporating multiple back-propagation (MBP) layers, the DBNs generalization capability is remarkably improved.     

Affective computing vs. affective placebo: Study of a biofeedback-controlled game for relaxation training

Relaxation training is an application of affective computing with important implications for health and wellness. After detecting user׳s affective state through physiological sensors, a relaxation training application can provide the user with explicit feedback about his/her detected affective state. This process (biofeedback) can enable an individual to learn over time how to change his/her physiological activity for the purposes of improving health and performance. In this paper, we provide three contributions to the field of affective computing for health and wellness. First, we propose a novel application for relaxation training that combines ideas from affective computing and games. The game detects user׳s level of stress and uses it to influence the affective state and the behavior of a 3D virtual character as a form of embodied feedback. Second, we compare two algorithms for stress detection which follow two different approaches in the affective computing literature: a more practical and less costly approach that uses a single physiological sensor (skin conductance), and a potentially more accurate approach that uses four sensors (skin conductance, heart rate, muscle activity of corrugator supercilii and zygomaticus major). Third, as the central motivation of our research, we aim to improve the traditional methodology employed for comparisons in affective computing studies. To do so, we add to the study a placebo condition in which user׳s stress level, unbeknown to him/her, is determined pseudo-randomly instead of taking into account his/her physiological sensor readings. The obtained results show that only the feedback presented by the single-sensor algorithm was perceived as significantly more accurate than the placebo. If the placebo condition was not included in the study, the effectiveness of the two algorithms would have instead appeared similar. This outcome highlights the importance of using more thorough methodologies in future affective computing studies.     

Pattern synthesis of time‐modulated conformal arrays by an improved differential evolution algorithm

In this article, an improved differential evolution algorithm (IDE) based on two different colonies is proposed and applied to time‐modulated conformal arrays syntheses. The whole population of IDE is divided into two parts. The one part searches the solution globally while the other searches the neighborhood of the solution provided by the previous one. Benchmark functions are provided to testify IDE. Furthermore, IDE is applied to synthetize sum‐difference patterns with a 1 × 16 elements time‐modulated circular array and low sidelobe level (SLL) patterns with an 8 × 12 elements time‐modulated cone array. After optimization, the sideband level (SBL) of the circular array at the first sideband frequency is ?1.00 dB. The SLL and SBL at the first sideband frequency of the cone array are lower than ?30.00 and ?20.00 dB, respectively. Experiment results verify the superior performance of IDE. Moreover, to accelerate the computation speed, graphics processing unit parallel computing technique is introduced into pattern synthesis and the acceleration ratios of more than 23 times can be achieved. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:697–705, 2014.     

Challenges in real-time virtualization and predictable cloud computing

Cloud computing and virtualization technology have revolutionized general-purpose computing applications in the past decade. The cloud paradigm offers advantages through reduction of operation costs, server consolidation, flexible system configuration and elastic resource provisioning. However, despite the success of cloud computing for general-purpose computing, existing cloud computing and virtualization technology face tremendous challenges in supporting emerging soft real-time applications such as online video streaming, cloud-based gaming, and telecommunication management. These applications demand real-time performance in open, shared and virtualized computing environments. This paper identifies the technical challenges in supporting real-time applications in the cloud, surveys recent advancement in real-time virtualization and cloud computing technology, and offers research directions to enable cloud-based real-time applications in the future.     

Strategies and methods for cloud migration

Legacy system migration to the cloud brings both great challenges and benefits, so there exist various academic research and industrial applications on legacy system migration to the cloud. By analyzing the research achievements and application status,we divide the existing migration methods into three strategies according to the cloud service models integrally. Different processes need to be considered for different migration strategies, and different tasks will be involved accordingly. The similarities and differences between the migration strategies are discussed, and the challenges and future work about legacy system migration to the cloud are proposed. The aim of this paper is to provide an overall presentation for legacy system migration to the cloud and identify important challenges and future research directions.     

A New Risk Assessment Approach for Cloud Consumer

The cloud computing introduces several changes in technology that have resulted a new ways for cloud providers to deliver their services to cloud consumers mainly in term of security risk assessment, thus, adapting a current risk assessment tools to cloud computing is a very difficult task due to its several characteristics that challenge the effectiveness of risk assessment approaches. Consequently, there is a need of risk assessment approach adapted to cloud computing. With such an approach, the cloud consumers can be guaranteed the effectiveness of data security and the cloud providers can win the trust of their cloud consumers. This paper requires the formalization of risk assessment method for conventional system as fundamental steps towards the development of flexible risk assessment approach regarding cloud consumers.