The improvement of museum information flow based on paste functional mapping method

As a subsidiary institution of collecting and preserving cultural relics, human cultural heritages and developing information resources, museums are important places to display, disseminate and study excellent national cultures. With the development of the economy and the improvement of people's living standards, people's demand for spiritual culture is getting higher and higher, and museums are getting more and more attention from people. Thanks to the rapid development of computer technology, more and more museums are focusing on informatisation. This article will use the information system's functional mapping method based on the museum's improvement and test analysis, which can make the museum's business unit with the use of the unified system platform, under a unified, homogeneous standard data for the orderly organisation of information resources sharing and efficient, rapid building of information resources of lateral connection, make the administrative, business interaction and interrelated information collection with Word Perfect data integration, and create a museum management platform more conducive to innovative thinking. The innovation system mechanism, to improve the way of ZhanChen for the museum, to speed up the informatisation construction museum, make a museum of play to better spread knowledge, transfer civilisation, materialisation of education function and the important window displaying the world the outstanding civilisation achievement effect, and promote the development of cultural undertakings, where science has a very important realistic and far-reaching historical significance.

Evaluating Effective Connectivity of Trust in Human–Automation Interaction: A Dynamic Causal Modeling (DCM) Study

Objective Using dynamic causal modeling (DCM), we examined how credibility and reliability affected the way brain regions exert causal influence over each other—effective connectivity (EC)—in the context of trust in automation. Background Multiple brain regions of the central executive network (CEN) and default mode network (DMN) have been implicated in trust judgment. However, the neural correlates of trust judgment are still relatively unexplored in terms of the directed information flow between brain regions. Method Sixteen participants observed the performance of four computer algorithms, which differed in credibility and reliability, of the system monitoring subtask of the Air Force Multi-Attribute Task Battery (AF-MATB). Using six brain regions of the CEN and DMN commonly identified to be activated in human trust, a total of 30 (forward, backward, and lateral) connection models were developed. Bayesian model averaging (BMA) was used to quantify the connectivity strength among the brain regions. Results Relative to the high trust condition, low trust showed unique presence of specific connections, greater connectivity strengths from the prefrontal cortex, and greater network complexity. High trust condition showed no backward connections. Conclusion Results indicated that trust and distrust can be two distinctive neural processes in human–automation interaction—distrust being a more complex network than trust, possibly due to the increased cognitive load. Application The causal architecture of distributed brain regions inferred using DCM can help not only in the design of a balanced human–automation interface design but also in the proper use of automation in real-life situations.

Neo-Surrealism within the Prose Poetry Tradition

This chapter assesses the American neo-surreal as an influential strand of prose poetry, adapting ideas that originated with the surrealists to challenge assumptions about how the world should be understood, and prose-poetic narratives ought to be read. The term “neo-surrealism” does not have to be restrictive but may be used as a way of opening up an understanding of certain key features of prose poetry internationally. And while American prose poets are certainly not the first to experiment with surrealism, many contemporary American prose poets demonstrate a particular interest in absurdism and neo-surrealism. As a result, neo-surrealism is arguably best exemplified by American prose poets — in terms of the number of writers employing such techniques and the quality of neo-surrealistic works being written. Notwithstanding its contemporaneity, the neo-surrealistic strand of prose poetry maintains a clear — if sometimes lateral — connection to the strange and often dreamlike works produced by nineteenth-century French prose poets such as Charles Baudelaire and Arthur Rimbaud.

Mask OBB: A Semantic Attention-Based Mask Oriented Bounding Box Representation for Multi-Category Object Detection in Aerial Images

Object detection in aerial images is a fundamental yet challenging task in remote sensing field. As most objects in aerial images are in arbitrary orientations, oriented bounding boxes (OBBs) have a great superiority compared with traditional horizontal bounding boxes (HBBs). However, the regression-based OBB detection methods always suffer from ambiguity in the definition of learning targets, which will decrease the detection accuracy. In this paper, we provide a comprehensive analysis of OBB representations and cast the OBB regression as a pixel-level classification problem, which can largely eliminate the ambiguity. The predicted masks are subsequently used to generate OBBs. To handle huge scale changes of objects in aerial images, an Inception Lateral Connection Network (ILCN) is utilized to enhance the Feature Pyramid Network (FPN). Furthermore, a Semantic Attention Network (SAN) is adopted to provide the semantic feature, which can help distinguish the object of interest from the cluttered background effectively. Empirical studies show that the entire method is simple yet efficient. Experimental results on two widely used datasets, i.e., DOTA and HRSC2016, demonstrate that the proposed method outperforms state-of-the-art methods.

EnsNet: Ensconce Text in the Wild

A new method is proposed for removing text from natural images. The challenge is to first accurately localize text on the stroke-level and then replace it with a visually plausible background. Unlike previous methods that require image patches to erase scene text, our method, namely ensconce network (EnsNet), can operate end-to-end on a single image without any prior knowledge. The overall structure is an end-to-end trainable FCN-ResNet-18 network with a conditional generative adversarial network (cGAN). The feature of the former is first enhanced by a novel lateral connection structure and then refined by four carefully designed losses: multiscale regression loss and content loss, which capture the global discrepancy of different level features; texture loss and total variation loss, which primarily target filling the text region and preserving the reality of the background. The latter is a novel local-sensitive GAN, which attentively assesses the local consistency of the text erased regions. Both qualitative and quantitative sensitivity experiments on synthetic images and the ICDAR 2013 dataset demonstrate that each component of the EnsNet is essential to achieve a good performance. Moreover, our EnsNet can significantly outperform previous state-of-the-art methods in terms of all metrics. In addition, a qualitative experiment conducted on the SBMNet dataset further demonstrates that the proposed method can also preform well on general object (such as pedestrians) removal tasks. EnsNet is extremely fast, which can preform at 333 fps on an i5-8600 CPU device.

Conceptual hydrogeological model of the Yonge Street Aquifer, south-central Ontario: a glaciofluvial channel–fan setting

The Yonge Street Aquifer (YSA) in the Greater Toronto Area of south-central Ontario is a prolific municipal supply aquifer. It has been considered to be channelized sand and gravel linked to a bedrock valley. Despite considerable work, the fundamental conceptual model for the YSA is not well developed and documented. Based on high-quality data, a revised conceptual model of the aquifer is presented. Seismic profiles define the geometry of the regional stratigraphy with four distinct units: bedrock, Lower sediments, Newmarket Till, and Oak Ridges Moraine (ORM) sediment. Seismic data reveal two generations of roughly north–south channels: older sub-Newmarket Till channels within Lower sediments (termed Thorncliffe channel) and ORM-related channels (termed ORM channel) that incise both Newmarket Till and Lower sediments. The YSA is interpreted to occur within a Thorncliffe channel, with possible vertical connection to younger ORM channels and lateral connection to inter-channel Lower sediments. Thorncliffe channel deposits consist of fining-upward transitions from coarse gravel, to sand, to rhythmically bedded mud interpreted to be deposited within a channel – esker – subaqueous fan complex. Upper Thorncliffe channel mud facies and overlying Newmarket Till provide a capping aquitard. The YSA conceptual model benefits from a strong understanding of facies changes in the Thorncliffe Formation. The deposits with highest permeability occur within up to 80 m thick gravel and sand sequences at the base of the Thorncliffe channel, with transmissivity ranging from 1500 to 4500 m2/day. Groundwater level response to municipal pumping confirms connection along the channel with muted hydraulic response laterally. Thorncliffe channels are interpreted to be up to 20 km long and approximately 2 km wide.