Effects of using head-up display in automobile context on attention demand and driving performance

This study aimed to investigate the difference in driving performance between drivers’ attention on the head-up display (HUD)/road under low/high road conditions via a driving simulator experiment. Experimental driving included four driving scenarios with attention-on-the-HUD followed by attention-on-the-road or vice versa under high or low driving load conditions. Each scenario took about a 30-min driving consisting of two 15-min sections for each attention location. Forty-eight participants, divided into four groups, drove one of the four scenarios once. Besides driving safely within speed limit, participants were also required to perform detection task and speed limit sign response task. Results revealed that drivers paying attention to the HUD, under both low and high driving load conditions, reacted faster to speed limit sign changes than when paying attention to the road. In addition, attention-to-the-HUD under low driving load condition caused the smallest variation in steering wheel angle and lateral acceleration. These differences can be attributed to the driver's enhanced awareness and the cognitive capture effect, and tended to diminish with increasing driving workload. Finally, attention shift of drivers and the so-called novelty effect for using new technology product were also found.     

基于元路径注意力机制的MOOC视频推荐方法

MOOC平台上,一个课程可能存在多个版本的视频,为向学生推荐一个满足学习兴趣的MOOC视频就需要分析学生兴趣与视频内容的关系,为此,提出一种基于元路径注意力机制的视频推荐方法Mrec。一方面,利用异构信息网（HIN）描述学习者和MOOC资源之间的关系,进而使用元路径表达学生和视频之间的交互关系;另一方面,利用注意力机制捕捉学生、视频、元路径的特征对学习兴趣的影响情况。具体来说,Mrec方法包括两层注意力机制：第一层是节点注意力层,通过邻居的特征加权联合节点自身的特征,利用多头注意力得到实体在不同元路径下的特征表示;第二层是路径注意力层,通过融合在不同元路径的指导下学习到的实体的特征表示来捕捉实体在不同兴趣下的特征表示,并将学习到的用户与视频实体输入到多层感知机（MLP）中得到预测分数来进行top-K推荐。在MOOCCube和MOOCdata数据集上进行实验的结果表明,Mrec的点击率、归一化折损累积收益（NDCG）、平均倒数排名（MRR）与受试者工作特征曲线下面积（AUC）均优于对比方法。     

IEEE 802.11a移动应用性能仿真研究与优化

IEEE 802.11a移动应用是当前的研究热点问题之一。通过系统仿真,定量分析了802.11a在视距/非视距、不同多径传输、不同移动速度、不同信噪比条件下的系统性能,总结了802.11a移动应用的受限条件;在通过分析不同数据帧长、不同发送速率对系统性能影响的基础上,提出了802.11a移动应用的4种优化策略。研究还发现,经优化后的802.11a移动应用系统在受限条件下可以为120 km/h的快速移动用户提供约10 Mb/s的MAC层实际传输速率。     

Decision field theory extensions for behavior modeling in dynamic environment using Bayesian belief network

Decision field theory (DFT), widely known in the field of mathematical psychology, provides a mathematical model for the evolution of the preferences among options of a human decision-maker. The evolution is based on the subjective evaluation for the options and his/her attention on an attribute (interest). In this paper, we extend DFT to cope with the dynamically changing environment. The proposed extended DFT (EDFT) updates the subjective evaluation for the options and the attention on the attribute, where Bayesian belief network (BBN) is employed to infer these updates under the dynamic environment. Four important theorems are derived regarding the extension, which enhance the usability of EDFT by providing the minimum time steps required to obtain the stabilized results before running the simulation (under certain assumptions). A human-in-the-loop experiment is conducted for the virtual stock market to illustrate and validate the proposed EDFT. The preliminary result is quite promising.     

基于注意力机制学习的变电设备缺陷检测方法

针对现有的变电站缺陷图像检测识别算法鲁棒性弱问题,提出一种基于注意力机制学习的变电设备缺陷图像检测识别方法。所提方法以卷积神经网络作为缺陷图像特征提取的骨架网络,融合注意力机制原理,进一步提升缺陷图像特征的可辨识性。首先,构建注意力机制的卷积神经网络特征提取模型,提取不同注意力机制下变电站缺陷图像特征;其次,设计一种自适应特征学习函数,将不同注意力机制下的特征融合成为新的高质量变电缺陷图像特征;最后,将不同注意力机制下的缺陷图像特征输入到分类模型,实现变电站缺陷图像检测。所提方法增强了变电设备缺陷图像检测的准确性与鲁棒性,实验结果显示,所提方法的mAP达到了70.4%。     

视觉注意机制下结合语义特征的行人检测

目的 为研究多场景下的行人检测,提出一种视觉注意机制下基于语义特征的行人检测方法。方法 首先,在初级视觉特征基础上,结合行人肤色的语义特征,通过将自下而上的数据驱动型视觉注意与自上而下的任务驱动型视觉注意有机结合,建立空域静态视觉注意模型;然后,结合运动信息的语义特征,采用运动矢量熵值计算运动显著性,建立时域动态视觉注意模型;在此基础上,以特征权重融合的方式,构建时空域融合的视觉注意模型,由此得到视觉显著图,并通过视觉注意焦点的选择完成行人检测。结果 选用标准库和实拍视频,在Matlab R2012a平台上,进行实验验证。与其他视觉注意模型进行对比仿真,本文方法具有良好的行人检测效果,在实验视频上的行人检测正确率达93%。结论 本文方法在不同的场景下具有良好的鲁棒性能,能够用于提高现有视频监控系统的智能化性能。     

Measuring working memory load effects on electrophysiological markers of attention orienting during a simulated drive

Intersection accidents result in a significant proportion of road fatalities, and attention allocation likely plays a role. Attention allocation may depend on (limited) working memory (WM) capacity. Driving is often combined with tasks increasing WM load, consequently impairing attention orienting. This study (n = 22) investigated WM load effects on event-related potentials (ERPs) related to attention orienting. A simulated driving environment allowed continuous lane-keeping measurement. Participants were asked to orient attention covertly towards the side indicated by an arrow, and to respond only to moving cars appearing on the attended side by pressing a button. WM load was manipulated using a concurrent memory task. ERPs showed typical attentional modulation (cue: contralateral negativity, LDAP; car: N1, P1, SN and P3) under low and high load conditions. With increased WM load, lane-keeping performance improved, while dual task performance degraded (memory task: increased error rate; orienting task: increased false alarms, smaller P3).

Practitioner Summary: Intersection driver-support systems aim to improve traffic safety and flow. However, in-vehicle systems induce WM load, increasing the tendency to yield. Traffic flow reduces if drivers stop at inappropriate times, reducing the effectiveness of systems. Consequently, driver-support systems could include WM load measurement during driving in the development phase.     

Visual Focus of Attention in Non-calibrated Environments using Gaze Estimation

Estimating the focus of attention of a person highly depends on her/his gaze directionality. Here, we propose a new method for estimating visual focus of attention using head rotation, as well as fuzzy fusion of head rotation and eye gaze estimates, in a fully automatic manner, without the need for any special hardware or a priori knowledge regarding the user, the environment or the setup. Instead, we propose a system aimed at functioning under unpretending conditions, only with the usage of simple hardware, like a normal web-camera. Our system is aimed at functioning in a human-computer interaction environment, considering a person is facing a monitor with a camera adjusted on top. To this aim, we propose in this paper two novel techniques, based on local and appearance information, estimating head rotation, and we adaptively fuse them in a common framework. The system is able to recognize head rotational movement, under translational movements of the user towards any direction, without any knowledge or a-priori estimate of the user’s distance from the camera or camera intrinsic parameters.     

基于出行模式的注意力机制可解释性探索

为了探索深度注意力模型在地铁出行预测任务中的可解释性,提出基于出行模式的注意力权重擦除方法和可解释性评估框架。利用提出的地铁出行深度注意力框架搭建预测模型,使用广州地铁羊城通数据构造三种不同长度出行序列数据集进行模型训练和验证,达到70%以上准确率;通过单一出行模式的注意力权重擦除实验发现,擦除最大注意力权重的出行模式比随机模式更能显著地影响模型预测结果,但大多数样本不发生预测结果的变化。即注意力机制在该条件下提供的可解释性信息是有限的,且该信息量随着序列长度增加而减小;通过一组出行模式注意力权重擦除实验结果表明,按注意力权重降序擦除能最快使模型预测结果发生变化,并且模型能稳定地对重要的出行模式的出行记录分配注意力权重,即注意力机制在该条件下较好地提供了可解释性信息,且该信息量随着序列长度增加而增大。     

An Improved Attention Parameter Setting Algorithm Based on Award Learning Mechanism

The setting of attention parameters plays a role in the performance of synergetic neural network based on PFAP model. This paper first analyzes the attention parameter setting algorithm based on award-penalty learning mechanism. Then, it presents an impro     

基于网络环境下的个人计算机安全与防护措施

因特网的普及对人们的生活和工作方式产生巨大的影响,人们在享受网络技术带来的便利的同时,也深受病毒破坏带来的苦恼。TCP/IP协议的局限,操作系统的漏洞,以及病毒技术的快速发展,要求用户重视网络环境下的个人计算机安全与防护措施。     

基于网络环境下的个人计算机安全与防护措施

因特网的普及对人们的生活和工作方式产生巨大的影响,人们在享受网络技术带来的便利的同时,也深受病毒破坏带来的苦恼。TCP／IP协议的局限,操作系统的漏洞,以及病毒技术的快速发展,要求用户重视网络环境下的个人计算机安全与防护措施。     

Effect of correlated color temperature and illuminance on sustained attention collected via electroencephalography signals

This study aimed to explore the differences in the subjects' sustained attention under the impact of nine lighting conditions consisting of the combination of three commonly used correlated color temperature (CCT) (3300 K, 4300 K, and 5300 K) and illuminance levels (300 lx, 500 lx, and 750 lx) to provide guidance on the adjustment of CCT and illuminance level parameters for indoor lighting. We selected 24 physically and mentally healthy university students (12 male and 12 female) as the experimental subjects. The subjects were required to perform sustained attention to response task (SART) activities under the nine different lighting conditions and collected the alpha (α) waves (8–12 Hz) from the electroencephalography signals. Subsequently, the mean power spectral density of the α waves and various SART parameters were analyzed and tested. Finally, the effects of different CCT and illuminance levels on the subjects' attention were compared. With the increase in CCT, the attention level tended to increase linearly, whereas the attention level was the lowest at 300 lx and the highest at 500 lx and appeared as an inverted “U” shape. The subjects' attention level was the highest at the combination of CCT of 5300 K and illuminance level of 500 lx and the lowest at the combination of CCT of 3300 K and illuminance level of 500 lx. These results provide important data to elucidate the impact of lighting condition on attention.     

注意力分配策略对界面任务绩效和视觉行为的影响机制

在工程领域,作业人员通常需要面对刺激分布不均的复杂信息界面,并执行相关的交互任务.作业人员的视觉注意力分配已被证明与任务绩效密切相关,但对于复杂界面中基于不同信息分配策略的多优先级刺激对作业人员的视觉注意力分配及任务绩效间的潜在联系仍亟待研究.对此,本文基于多优先级注意力分配策略实验对作业人员在不同负荷条件下的任务绩效和视觉行为的影响机制展开研究.实验结果表明,差异性的分配策略和信息优先级划分提升了任务绩效表现,不同分配策略和优先级划分条件下的视觉行为存在显著差异,并受脑力负荷的影响.该结论能够为人机交互界面的设计和优化提供参考,从而提高作业人员在任务中的绩效表现.     

基于双尺度特征融合的单幅图像去雾网络

基于深度学习的图像去雾方法在合成数据集上表现良好,但在真实场景中应用时存在去雾不彻底、颜色失真等问题。提出一种新的单幅图像去雾网络,该网络包含特征提取、特征融合2个模块。在特征提取模块中,通过残差密集块和具有空间注意机制的特征提取块分别提取图像的局部特征和全局特征。在特征融合模块中,利用通道注意力机制对局部特征图和全局特征图进行通道加权,并通过卷积操作融合加权后的局部特征图与全局特征图。最后,采用门控网络自适应结合3个不同深度的融合特征图,以恢复高质量的去雾图像。实验结果表明,所提网络在室内数据集下的峰值信噪比(PSNR)和结构相似度（SSIM）分别为33.04 dB、0.983,在HAZERD数据集下的PSNR和SSIM分别比GridDehazeNet网络高出1.33 dB和0.041。同时,该网络的模型参数量和浮点运算数分别为0.34M和16.06×109frame/s,具有较低复杂度,对合成图像和真实图像均可取得理想的去雾效果。     

《ASP网页制作》教学方法探索与实践

ASP是微软公司推出的一种基于浏览器的网络数据库编程技术,是Windows环境下B/S结构应用的主要编程工具。通过理论教学和实验环节,结合学生的实际情况,改进考核方法,可使学生注重提高编程能力和实际应用能力,并通过开发项目的方式,达到学以致用的效果。     

Attention modulation of neural tuning through peak and base rate

This study investigates the influence of attention modulation on neural tuning functions. It has been shown in experiments that attention modulation alters neural tuning curves. Attention has been considered at least to serve to resolve limiting capacities and to increase the sensitivity to attended stimulus, while the exact functions of attention are still under debate. Inspired by recent experimental results on attention modulation, we investigate the influence of changes in the height and base rate of the tuning curve on the encoding accuracy, using the Fisher information. Under an assumption of stimulus-conditional independence of neural responses, we derive explicit conditions that determine when the height and base rate should be increased or decreased to improve encoding accuracy. Notably, a decrease in the tuning height and base rate can improve the encoding accuracy in some cases. Our theoretical results can predict the effective size of attention modulation on the neural population with respect to encoding accuracy. We discuss how our method can be used quantitatively to evaluate different aspects of attention function.     

脑波信号控制轨道小车系统的研制

脑波信号识别是脑机接口（BCI）技术需要解决的关键问题之一。针对脑波信号的数据处理以及特征提取,提出了采用小波变换系数计算“专心度”的特征来描述精神力集中强烈程度以及脑电波活跃状况,并结合实际测试得出不同精神状态下的检测算法。设计了一款基于ARM处理器的脑波控制轨道小车系统,将“专心度”值发送给小车系统,实现了不同精神状态下对小车车速的控制。实验结果表明,采用专心度的特征在识别效果方面要优于FFT的单一频率特征。并且该系统能够快速提取出脑波特征并实时地控制轨道小车行驶,系统性能稳定具有一定的实用价值。     

基于B/S软件体系结构的研究

在软件开发过程中,软件体系结构的设计起着非常重要的作用,用它来指导整个软件的开发,可以准确地满足用户的需要并降低软件的开发成本。将软件体系结构与分层思想相结合,给出了当前广泛应用的基于B／S的软件体系结构的框架和框架的具体实现,指出了设计及应用过程中应该注意的问题。