User-friendly search interface for older adults: supporting search goal refreshing in working memory to improve information search strategies

ABSTRACT

This study investigates how a search interface that displays users’ ultimate query (i.e. users’ current search goal) can cope with the age-related decrease of fluid abilities and support older users’ search behaviours. 30 young and 18 older adults completed 9 search problems with a regular web browser or with the experimental search interface. Results showed that older adults spent longer time on the search engine result pages, they needed more time to reformulate, and they had more difficulties exploring the search paths elaborated. Age-differences also appeared as soon as the beginning of the search. The support tool helped older users reformulate their queries more rapidly and elaborate more flexible search strategies at the beginning of the activity. Indeed, older adults who interacted with the support tool switched to the processing of a new search path more rapidly instead of exploiting their initial query (i.e. they visited fewer websites for the initial query produced and reformulated a query instead of keeping on conducting a deeper investigation of the search results provided in the search engine result page). Implications of these findings for the design of effective support tools for older users are discussed.     

Ex post demand information sharing between differentiated suppliers and a common retailer

We consider ex post demand information sharing in a two-echelon supply chain in which two suppliers sell differentiated products through a common retailer. We model three scenarios of information sharing to characterise the conditions under which information sharing may benefit or hurt different players: (1) no supplier is informed; (2) only one supplier is informed; and (3) both suppliers are informed. We find that the retailer may voluntarily share the low demand information when the product differentiation and demand magnitude satisfy certain conditions. In contrast, the retailer has no incentive to share the high demand information, even though this information benefits the suppliers. To achieve information sharing, we propose a side payment mechanism, which allows benefitted players to subsidise hurtful players. Finally, we extend our model by considering a situation where the suppliers have limited capacity and may invest to ramp up production capacity to satisfy a high demand.     

基于深度学习的秀丽隐杆线虫显微图像分割方法

利用计算机实现自动、准确的秀丽隐杆线虫(C.elegans)的各项形态学参数分析,至关重要的是从显微图像上分割出线虫体态,但由于显微镜下的图像噪声较多,线虫边缘像素与周围环境相似,而且线虫的体态具有鞭毛和其他附着物需要分离,多方面因素导致设计一个鲁棒性的C.elegans分割算法仍然面临着挑战。针对这些问题,提出了一种基于深度学习的线虫分割方法,通过训练掩模区域卷积神经网络(Mask R-CNN)学习线虫形态特征实现自动分割。首先,通过改进多级特征池化将高级语义特征与低级边缘特征融合,结合大幅度软最大损失(LMSL)损失算法改进损失计算;然后,改进非极大值抑制;最后,引入全连接融合分支等方法对分割结果进行进一步优化。实验结果表明,相比原始的Mask R-CNN,该方法平均精确率(AP)提升了4.3个百分点,平均交并比(mIOU)提升了4个百分点。表明所提出的深度学习分割方法能够有效提高分割准确率,在显微图像中更加精确地分割出线虫体。     

小尺度交通信号灯的检测与状态识别

交通信号灯识别包括检测和状态识别，在智能交通系统中发挥重要作用。基于YOLOv3算法提出了一种交通信号灯检测与状态识别模型。针对交通信号灯相较于交通场景中其他目标具有尺度小的特性进行了算法的设计：降低骨干网络的下采样倍率以增加小尺度目标的特征描述能力；通过增大特征图的尺度来改进多尺度特征融合；引入广义交并比作为检测任务的损失函数来改进目标边界框的回归效果。同时，根据交通信号灯本身的特性，使用颜色和形状约束的方法对信号灯进行状态识别和类别验证。最后在公开的Bosch交通信号灯数据集上和实际的城区道路进行了实验验证。实验结果表明，所提出的算法能够提升交通信号灯识别的精度和召回率，识别准确率可以达到90%左右。     

石油化工项目建设管理数据平台的探讨

通过建立项目管理数据平台,让石油化工项目建设管理经验得到更好的传承和利用,数据平台可以实现管理经验数据化、建设数据共享化、数据结构模板化、数据编码标准化、收集数据流程化、数据操作平台化、数据计算集成化、数据更新动态化功能目标。结合石油化工项目建设特点,归纳了5类31种管理数据,介绍了每种数据的内容和用途,并对各种数据的字段设计、平台开发技术选择提出了建议,并对数据平台的未来价值进行了展望。     

Relevance- and interface-driven clustering for visual information retrieval

Search results of spatio-temporal data are often displayed on a map, but when the number of matching search results is large, it can be time-consuming to individually examine all results, even when using methods such as filtered search to narrow the content focus. This suggests the need to aggregate results via a clustering method. However, standard unsupervised clustering algorithms like $K$-means (i) ignore relevance scores that can help with the extraction of highly relevant clusters, and (ii) do not necessarily optimize search results for purposes of visual presentation. In this article, we address both deficiencies by framing the clustering problem for search-driven user interfaces in a novel optimization framework that (i) aims to maximize the relevance of aggregated content according to cluster-based extensions of standard information retrieval metrics and (ii) defines clusters via constraints that naturally reflect interface-driven desiderata of spatial, temporal, and keyword coherence that do not require complex ad-hoc distance metric specifications as in $K$-means. After comparatively benchmarking algorithmic variants of our proposed approach – RadiCAL – in offline experiments, we undertake a user study with 24 subjects to evaluate whether RadiCAL improves human performance on visual search tasks in comparison to $K$-means clustering and a filtered search baseline. Our results show that (a) our binary partitioning search (BPS) variant of RadiCAL is fast, near-optimal, and extracts higher-relevance clusters than $K$-means, and (b) clusters optimized via RadiCAL result in faster search task completion with higher accuracy while requiring a minimum workload leading to high effectiveness, efficiency, and user satisfaction among alternatives.     

基于GFU和分层LSTM的组群行为识别研究方法

提出一种以"关键人物"为核心，使用门控融合单元（GFU，Gated Fusion Unit）进行特征融合的组群行为识别框架，旨在解决两个问题：①组群行为信息冗余，重点关注关键人物行为特征，忽略无关人员对组群行为的影响；②组群内部交互行为复杂，使用GFU有效融合以关键人物为核心的交互特征，再通过LSTM时序建模成为表征能力更强的组群特征.最终，通过softmax分类器进行组群行为类别分类.该算法在排球数据集上取得了86.7%的平均识别率.     

生产信息管理系统关键技术研究

根据公司生产控制系统现状和发展需要,分析和探讨建立公司生产信息管理系统(PIMS)的软硬件基本构成和主要方法,并对以隔离网关、实时数据库、能源计量管理等为基础的生产信息管理系统关键技术进行研究和测试。     

食品生物防腐剂产生菌门间远缘细胞融合研究

乳酸链球菌素(Nisin)和ε-聚赖氨酸是由微生物产生的安全的天然防腐剂,分别由乳酸链球菌和白色链霉菌产生。这两种生物防腐剂的抑菌效果不同,存在互补性。为了获得具有更广谱抑菌作用的优良性状的菌株,探索远缘细胞融合的可行性,以自主分离的ε-聚赖氨酸产生菌白色链霉菌YB12和Nisin产生菌NZ9700作为亲本菌株,通过原生质体融合技术将两个菌株进行门间的细胞融合;通过抗药性筛选、形态学观察、SRAP-PCR分子鉴定,成功获得两株稳定遗传的融合菌株。同时对融合菌株的抑菌活性进行了检测。     

一种高误码(n,k,m)非系统卷积码盲识别算法

针对信息截获等领域中的卷积码盲识别问题，提出一种高误码 (n,k,m)非系统卷积码盲识别算法。首先建立可变的数据矩阵模型，对侦收到的数据进行相关预处理，以减小误码对识别的影响，提高算法的容错性能；再对预处理后的数据矩阵进行统计分析，识别出卷积码的各项参数，并提取各个数据矩阵的校验序列；进而利用校验序列构建线性方程组求解生成多项式矩阵组，通过设定筛选生成多项式矩阵的条件，筛选出非系统卷积码的生成多项式矩阵，最终完成对非系统卷积码的识别。仿真实验表明，该算法可以对高误码(n,k,m)非系统卷积码实现有效的盲识别。