基于CiteSpace的旅游服务设计研究可视化分析

目的 旅游服务设计发展态势良好，从旅游服务设计的角度进行文献可视化分析，可从中获得当前领域的知识结构、研究热点和发展态势，为未来提供旅游服务设计研究和旅游产业的服务设计参考。方法 从CNKI和Web of Science数据库收集1990—2020年国内外的相关数据，运用CiteSpace对旅游服务设计的文献发表时间、高度被引用的文献、研究热点和研究前沿进行可视化分析，比较国内外研究差异。结论 在1990—2020年国内外旅游服务设计研究皆呈上升趋势。国外的研究热度、被引频次高于国内，以跨学科领域研究尤为突出，研究重点在建筑环境和交通相关策略;而国内研究以产品服务系统为主，偏向于技术的实现方式。综观研究现状和趋势，本文从智慧科技旅游、可持续发展和全域旅游进行深入探讨，提出旅游服务设计新发展趋势。     

基于形态审美的产品基因网络造型研究

目的 利用产品形态审美基因网络，对台灯外轮廓曲线的相关性进行综合分析，析出产品优化与创新设计的关键要素。方法 以三次贝塞尔曲线对台灯的外轮廓曲线进行基因编码，规律性调整控制点的坐标生成新曲线形态，进行曲线视觉力感知识别，挖掘曲线间的相关性;以曲线基因为节点，相关性为边构建产品形态审美基因网络，并对网络进行拓扑分析，析出关键设计节点辅助设计者进行产品设计。结果 运用产品形态基因网络的理论找到原型设计的关键性节点，并进行设计优化，使产品造型设计更符合消费者的审美习惯和偏好。结论 通过对台灯外轮廓线的案例实践，证实了产品形态基因网络在产品细节优化和造型创新设计方面具有实用性和可靠性，能够辅助设计者找到关键设计节点并协调设计的基因集团。     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

一种无证书签名方案的分析和改进

本文介绍了无证书签名的基本工作原理,介绍并分析了Hashimoto等人提出的一种无证书签名方案,对该方案进行了Ⅰ类型攻击,证明了该方案是不安全的,不能抵御超Ⅰ类型对手的攻击,对该方案提出了改进措施。     

Low area field-programmable gate array implementation of PRESENT image encryption with key rotation and substitution

Lightweight ciphers are increasingly employed in cryptography because of the high demand for secure data transmission in wireless sensor network, embedded devices, and Internet of Things. The PRESENT algorithm as an ultra-lightweight block cipher provides better solution for secure hardware cryptography with low power consumption and minimum resource. This study generates the key using key rotation and substitution method, which contains key rotation, key switching, and binary-coded decimal-based key generation used in image encryption. The key rotation and substitution-based PRESENT architecture is proposed to increase security level for data stream and randomness in cipher through providing high resistance to attacks. Lookup table is used to design the key scheduling module, thus reducing the area of architecture. Field-programmable gate array (FPGA) performances are evaluated for the proposed and conventional methods. In Virtex 6 device, the proposed key rotation and substitution PRESENT architecture occupied 72 lookup tables, 65 flip flops, and 35 slices which are comparably less to the existing architecture.     

一种X射线环焊缝图像质量评价方法

为了缩小增强后环焊缝图像质量主客观评价的差距,提出了一种基于人眼视觉的无参考质量评价指标。通过分析人眼的视觉特性,建立图像模糊评价模型,设定主观评价方法;主客观评价值进行曲线拟合,经验证,评价结果与主观评价结果具有线性拟合关系,评价结果接近人眼最佳视觉,验证了评价指标的可行性。     

一种标准模型下无证书签名方案的安全性分析与改进

无证书签名具有基于身份密码体制和传统公钥密码体制的优点，可解决复杂的公钥证书管理和密钥托管问题.Wu和Jing提出了一种强不可伪造的无证书签名方案，其安全性不依赖于理想的随机预言机.针对该方案的安全性，提出了两类伪造攻击.分析结果表明，该方案无法实现强不可伪造性，并在"malicious-but-passive"的密钥生成中心攻击下也是不安全的.为了提升该方案的安全性，设计了一个改进的无证书签名方案.在标准模型中证明了改进的方案对于适应性选择消息攻击是强不可伪造的，还能抵抗恶意的密钥生成中心攻击.此外，改进的方案具有较低的计算开销和较短的私钥长度，可应用于区块链、车联网、无线体域网等领域.     

Training visual attention in a naturalistic visual environment

The efficiency of training visual attention in the central and peripheral visual field was investigated by means of a visual detection task that was performed in a naturalistic visual environment including numerous, time-varying visual distractors. We investigated the minimum number of repetitions of the training required to obtain the top performance and whether intra-day training improved performance as efficiently as inter-day training. Additionally, our research aimed to find out whether exposure to a demanding task such as a microsurgical intervention may cancel out the effects of training.

Results showed that performance in visual attention peaked within three (for tasks in the central visual field) to seven (for tasks in the periphery) days subsequent to training. Intra-day training had no significant effect on performance. When attention training was administered after exposure to stress, improvement of attentional performance was more pronounced than when training was completed before the exposure. Our findings support the implementation of training in situ at work for more efficient results.

Practitioner Summary: Visual attention is important in an increasing number of workplaces, such as with surveillance, inspection, or driving. This study shows that it is possible to train visual attention efficiently within three to seven days. Because our study was executed in a naturalistic environment, training results are more likely to reflect the effects in the real workplace.     

基于深度卷积神经网络的视觉里程计研究

视觉里程计利用视频信息来估计相机运动的位姿参数，实现对智能体的定位。传统视觉里程计方法需要特征提取、特征匹配/跟踪、外点剔除、运动估计、优化等流程，解算非常复杂，因此，提出了基于卷积神经网络的方法来实现端对端的单目视觉里程计。借助卷积神经网络对彩色图片自动学习提取图像帧间变化的全局特征，将用于分类的卷积神经网络转化为帧间时序特征网络，通过三层全连接层输出相机的帧间相对位姿参数。在KITTI数据集上的实验结果表明，提出的Deep-CNN-VO模型可以较准确地估计车辆的运动轨迹，证明了方法的可行性。在简化了复杂模型的基础上，与传统的视觉里程计系统相比，该模型的精度也有所提高。