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Pixel-based semantic segmentation models fail to effectively express geographic objects and their topological relationships. Therefore, in semantic segmentation of remote sensing images, these models fail to avoid salt-and-pepper effects and cannot achieve high accuracy either. To solve these problems, object-based models such as graph neural networks (GNNs) are considered. However, traditional GNNs directly use similarity or spatial correlations between nodes to aggregate nodes’ information, which rely too much on the contextual information of the sample. The contextual information of the sample is often distorted, which results in a reduction in the node classification accuracy. To solve this problem, a knowledge and geo-object-based graph convolutional network (KGGCN) is proposed. The KGGCN uses superpixel blocks as nodes of the graph network and combines prior knowledge with spatial correlations during information aggregation. By incorporating the prior knowledge obtained from all samples of the study area, the receptive field of the node is extended from its sample context to the study area. Thus, the distortion of the sample context is overcome effectively. Experiments demonstrate that our model is improved by 3.7% compared with the baseline model named Cluster GCN and 4.1% compared with U-Net.

Deep neural networks achieve state-of-the-art results in many different problem settings by exploiting vast amounts of training data. However, collecting, storing and—in the case of supervised learning—labelling the data is expensive and time-consuming. Additionally, assessing the networks’ generalization abilities or predicting how the inferred output changes under input transformations is complicated since the networks are usually treated as a black box. Both of these problems can be mitigated by incorporating prior knowledge into the neural network. One promising approach, inspired by the success of convolutional neural networks in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations of the problem to solve that affect the output in a predictable way. This promises an increased data efficiency and more interpretable network outputs. In this survey, we try to give a concise overview about different approaches that incorporate geometrical prior knowledge into neural networks. Additionally, we connect those methods to 3D object detection for autonomous driving, where we expect promising results when applying those methods.

Three-dimensional semantic segmentation is the foundation for automatically creating enriched Digital Twin Cities (DTCs) and their updates. For this task, prior-level fusion approaches show more promising results than other fusion levels. This article proposes a new approach by developing and benchmarking three prior-level fusion scenarios to enhance the outcomes of point cloud-enriched semantic segmentation. The latter were compared with a baseline approach that used the point cloud only. In each scenario, specific prior knowledge (geometric features, classified images, or classified geometric information) and aerial images were fused into the neural network’s learning pipeline with the point cloud data. The goal was to identify the one that most profoundly enhanced the neural network’s knowledge. Two deep learning techniques, “RandLaNet” and “KPConv”, were adopted, and their parameters were modified for different scenarios. Efficient feature engineering and selection for the fusion step facilitated the learning process and improved the semantic segmentation results. Our contribution provides a good solution for addressing some challenges, particularly for more accurate extraction of semantically rich objects from the urban environment. The experimental results have demonstrated that Scenario 1 has higher precision (88%) on the SensatUrban dataset compared to the baseline approach (71%), the Scenario 2 approach (85%), and the Scenario 3 approach (84%). Furthermore, the qualitative results obtained by the first scenario are close to the ground truth. Therefore, it was identified as the efficient fusion approach for point cloud-enriched semantic segmentation, which we have named the efficient prior-level fusion (Efficient-PLF) approach.

The purpose of the current study was to: (a) examine the frequency of prior knowledge (PK) activation in elementary classrooms while students were engaged with text, (b) investigate the relevance of students’ responses to teacher prompts, (c) explore the nature of teachers’ and students’ prior knowledge activation utterances, and (d) investigate whether there were discernible routines in the interactions between teachers and students when activating PK. Participants were 6 teachers and 99 students from a private elementary school in the mid-Atlantic. An analysis of classroom discourse suggested that teachers infrequently prompted students to activate their prior knowledge during reading. Yet, when teachers did prompt PK, they asked about a prior lesson most often, or about a specific text, students’ world knowledge, or their personal experiences. Students then responded to their teachers according to the prompted referential frame. Additionally, four routines of classroom discourse were identified in the data including nonresponsive, question–answer, simple feedback, and interaction routines, with less elaborate routines being most common and primarily occurring at the beginning of lessons.

We investigated in an experiment with 180 university students the joint role of prior knowledge, alleged model competence, and social comparison orientation regarding the effectiveness of Eye Movement Modeling Examples (EMME) for supporting multimedia learning. EMME consisted of short videos with gaze replays of an instructed model demonstrating effective multimedia processing strategies. Participants were either instructed that the model in the EMME-videos was a successful learner (competent model) or another participant (peer model). Participants in a control condition received no EMME. Furthermore, we activated domain-relevant prior knowledge in half of the participants before watching the EMME. Against our expectations, we found no influence of either prior knowledge activation or model-observer similarity. As expected, our results indicated that EMME fostered multimedia learning. This was also supported by findings from small-scale meta-analyses that were conducted with the focus on the effect of EMME for multimedia learning and potential moderators of the effect. Moreover, results showed first evidence that social comparison orientation interacts with (alleged) model competence regarding the effectiveness of EMME. Further research is needed to follow up on the influence of individual factors as well as social cues on the effectiveness of EMME.

Purpose This paper aims to enrich absorptive capacity literature by specifically highlighting and adding environmental conditions and internationalisation process to the original conceptualisation. Design/methodology/approach The authors undertake a conceptual analysis and present an enhanced framework of absorptive capacity by integrating multiple literature streams. The authors have analysed the most relevant literature to provide underlying justifications for the proposed conceptual model. Findings Absorptive capacity ensures the long-term survival and success of a business. To develop absorptive capacity successfully, firms should focus on its various dimensions and existing intangible assets and external environment. The multidimensionality and richness of absorptive capacity is an under-explored area in the existing literature. The authors revisit the conceptualisation of absorptive capacity and add environmental conditions and the internationalisation process to the original conceptualisation. Absorptive capacity does not lead to a competitive advantage independent of its environment. To successfully develop it, firms have to adopt a holistic approach by considering the multi-dimensions, drivers and contextual conditions of absorptive capacity. Originality/value This study contributes by conceptualising absorptive capacity as a dynamic capability. It is one of the first studies to specifically propose a framework that combines antecedents (prior knowledge, combinative capabilities and IT capabilities), moderators (environmental conditions, namely, market and technological turbulence, competitiveness and the internationalisation process) and consequences (competitive advantage). The study offers a unique conceptualisation with implications for researchers and managers. As a result, managers will have a well-defined blueprint to create value by using firm capabilities.

This article reflects a metaview of the work of the three research projects funded through the Institute for Education Sciences under the Reading for Understanding competition that addressed middle‐grade through high school readers (grades 4–12). All three projects shared the assumption that instruction is necessary for successful reading to learn just as it is for learning to read. Through multiple studies conducted independently, the three projects arrived at common themes and features of productive instruction for reading for understanding with adolescent readers. These common themes are elaborated with instructional examples and include the following: (a) Students purposefully engage with multiple forms of texts and actively process them, (b) instructional routines incorporate social support for reading through a variety of participation structures, and (c) instruction supports new content learning by leveraging prior knowledge and emphasizing key constructs and vocabulary.

In training-based Machine Learning applications, the training data are frequently labeled by non-experts and expose substantial label noise which greatly alters the training models. In this work, a novel method for reducing the effect of label noise is introduced. The rules are created from expert knowledge to identify the incorrect non-expert training data. Using the gradient descent algorithm, the violating data samples are weighted less to mitigate their effects during model training. The proposed method is applied to the image classification problem using Manga109 and CIFAR-10 dataset. The experiments show that when the noise level is up to 50% our proposed method significantly increases the accuracy of the model compared to conventional learning methods.

Two experiments investigated the effects of characteristic features of concept mapping used for prior knowledge activation. Characteristic demands of concept mapping include connecting lines representing the relationships between concepts and labeling these lines, specifying the type of the semantic relationships. In the first experiment, employing a within-subjects design, 20 psychology students completed a label-provided-lines economics mapping task and then a create-and-label-lines meteorology mapping task or vice versa. The analysis of 40 think-aloud protocols indicated more elaboration processes for the label-provided-lines task than for the create-and-label-lines task. On the other hand, the protocols indicated more model-construction and organization processes in the create-and-label-lines task. The second experiment used the same variation but focused on learning outcomes and perceived self-efficacy as dependent measures. Forty-two psychology students were randomly assigned to either a label-provided-lines mapping task or a create-and-label-lines mapping task. Subsequently, both groups completed a learning phase in a hypertext environment and a posttest. Results showed substantial differences in learning outcomes and perceived self-efficacy in favor of the label-provided-lines prior knowledge activation task. The findings are congruent with coherence effects found in text-comprehension research and support the position that concept mapping should not be seen as a unitary method but be differentiated according to the specific tasks to be completed.

The importance of teacher read‐alouds in language teaching and learning has been known for years, but we know very little about how older English learners with limited English skills can benefit from this practice. The author shares how interactive read‐alouds with picture books can be designed to help emerging college learners of English as a foreign language develop word inference ability and become more independent word learners. The processes of selecting a picture book and target words for the practice and implementation of the four phases of a read‐aloud in a beginning‐level freshman English class in Taiwan are described. Findings suggest that interactive picture book read‐alouds can be a pleasurable and effective instructional practice for giving older emerging English learners tools that enable them to internalize the use of context and prior knowledge to draw inferences and further apply them independently when they confront unknown vocabulary as they read on their own.