Deep Learning for Pipeline Damage Detection: an Overview of the Concepts and a Survey of the State-of-the-Art

Pipelines have been extensively implemented to transfer oil as well as gas products at wide distances as they are safe, and suitable. However, numerous sorts of damages may happen to the pipeline, for instance erosion, cracks, and dent. Hence, if these faults are not properly refit will result in the pipeline demolitions having leak or segregation which leads to tremendously environment risks. Deep learning methods aid operators to recognize the earliest phases of threats to the pipeline, supplying them time and information in order to handle the problem efficiently. This paper illustrates fundamental implications of deep learning comprising convolutional neural networks. Furthermore the usages of deep learning approaches for hampering pipeline detriment through the earliest diagnosis of threats are introduced

Individual Differences in Function Learning as They Relate to the Learning of Conceptual Information

Individual differences have not often been considered within the problem-solving or concept-learning literatures despite the indication that some individuals are better able to transfer to novel problems and that manipulations in strategy can effectively increase the ability to transfer: Gick & Holyoak, 1983). Research in the function-learning domain indicates that there may be two qualitatively different types of learners: those who remember distinct example associations: exemplar learners) and others who abstract rules that govern each association: rule learners; DeLosh, Busemeyer, & McDaniel, 1997). Data from two unpublished studies: McDaniel, Cahill, Robbins, & Trumpower, 2012; Fadler, Lee, Scullin, Shelton, & McDaniel, 2012) have demonstrated the stability of these two types of learning across a variety of different higher order problem-solving, concept-learning, and cognitive tasks. However, it remains to be seen whether these differences between learners have implications for the type of conceptual material often used in classrooms. In the current project, this issue was addressed through two experiments. During Experiment 1, participants were first identified as exemplar or rule-based learners on the basis of function learning transfer performance. Each group then read several passages and answered questions about the passages that ranged in their degree of transfer. Rule learners performed better than exemplar learners on each question type and the two types of learners also demonstrated qualitatively different processing during function learning training and on a test of analogical transfer. The data from Experiment 2 showed that rule learners behaved qualitatively differently from exemplar learners during function learning training but failed to replicate the passage data from Experiment 1. However, a benefit was found on recognition memory for exemplar learners on a concept-learning task. The current study is the first to show differential benefits for exemplar and rule-based processing. It also provides evidence that function-learning tendency can be used to predict differences on concept-learning tasks and that only rule learning is associated with abstraction ability. The findings suggest that individual differences should be considered both in current hybrid models of categorization, but also potentially in classrooms that might rely heavily on problem solving, where the differences in types of learners may have an impact on student performance and understanding

Subgraph Networks Based Contrastive Learning

Graph contrastive learning (GCL), as a self-supervised learning method, can solve the problem of annotated data scarcity. It mines explicit features in unannotated graphs to generate favorable graph representations for downstream tasks. Most existing GCL methods focus on the design of graph augmentation strategies and mutual information estimation operations. Graph augmentation produces augmented views by graph perturbations. These views preserve a locally similar structure and exploit explicit features. However, these methods have not considered the interaction existing in subgraphs. To explore the impact of substructure interactions on graph representations, we propose a novel framework called subgraph network-based contrastive learning (SGNCL). SGNCL applies a subgraph network generation strategy to produce augmented views. This strategy converts the original graph into an Edge-to-Node mapping network with both topological and attribute features. The single-shot augmented view is a first-order subgraph network that mines the interaction between nodes, node-edge, and edges. In addition, we also investigate the impact of the second-order subgraph augmentation on mining graph structure interactions, and further, propose a contrastive objective that fuses the first-order and second-order subgraph information. We compare SGNCL with classical and state-of-the-art graph contrastive learning methods on multiple benchmark datasets of different domains. Extensive experiments show that SGNCL achieves competitive or better performance (top three) on all datasets in unsupervised learning settings. Furthermore, SGNCL achieves the best average gain of 6.9\% in transfer learning compared to the best method. Finally, experiments also demonstrate that mining substructure interactions have positive implications for graph contrastive learning.Comment: 12 pages, 6 figure

Meaningful vs. rote learning in arithmetic the decomposition vs. the equal additions method of subtraction

Thesis (Ed.M.)--Boston Universit

Removing opportunities to calculate improves students' performance on subsequent word problems.

BackgroundIn two studies we investigated whether removing opportunities to calculate could improve students' subsequent ability to solve similar word problems. Students were first asked to write explanations for three word-problems that they thought would help another student understand the problems. Half of the participants explained typical word problems (i.e., problems with enough information to make calculating an answer possible), while the other half explained the same problems with numbers removed, making calculating an answer impossible. We hypothesized that removing opportunities to calculate would induce students to think relationally about the word problems, which would result in higher levels of performance on subsequent transfer problems.ResultsIn both studies, participants who explained the non-calculable problems performed significantly better on the transfer test than participants who explained the typical (i.e., calculable) problems. This was so in spite of the manipulation not fully suppressing students' desire to calculate. Many students in the non-calculable group explicitly stated that they needed numbers in order to answer the question or made up numbers with which to calculate. There was a significant, positive relationship between the frequency with which students made up numbers and their self-reported mathematics anxiety.ConclusionsWe hypothesized that the mechanism at play was a reduction in instrumental thinking (and an increase in relational thinking). Interventions designed to help students remediate prior mathematical failure should perhaps focus less on the specific skills students are lacking, and more on the dispositions they bring to the task of "doing mathematics.

Exploring the Role of Simulation and Visualization Tools in Improving Learning Outcomes in Support of Technology Programs

Online educational opportunities have provided students with the flexibility to advance their careers and complete certificate and degree programs. These have also provided educational institutions with increased capacity without the investment of costly brick-and-mortar expansions at campuses. Technology programs, however, have shied away from integrating these advances due to their program outcomes being heavily dependent on the use of tools and hands-on learning. This dissertation explores the use of digital learning lectures on linear measuring instruments accompanied with virtual reality tools in technology programs and its effects on both cognitive and psychomotor learning outcomes compared to current modality - face-to-face instruction. The research then investigates the differences in problem-solving self-efficacy and transfer of knowledge that occurs between the two groups. All three studies refer back to the Vygotsky\u27s Zone of Proximal Development as the theoretical framework (1978). The initial study recruited participants from entry level mathematics courses. It aimed to determine if the digital learning group performed at least as well as the conventional learning group in the educational gains, in skilled-based assessment scores, and perception of learning measures. Additional measures for the digital learning environment were collected to determine usability, technology acceptance, and workload. The between subjects experimental analysis showed statistical difference in the cognitive gains in favor of the digital learning group, but no statistical difference in the skilled-based assessment scores nor the perception of learning measures. A post hoc power analysis determined that a sample size of 102 participants, 51 per group, would be needed to obtain a statistical power at the recommended 0.80 level for a one-tailed test (Cohen, 1988). The second study replicated the first study with adjustments based on lessons learned and a larger sample size (N=86). One major change was that the participants were recruited from first semester students in automotive, aircraft maintenance, and avionics technology programs. This population better reflects the target population for the topic selected to test, metrology. Similar to the initial pilot study, the large scale study aimed to determine the effects of the digital learning materials on the educational gains, in skilled-based assessment scores, and perception of learning measures. The between subjects experimental analysis showed no statistical difference in the cognitive gains nor in the skilled-based assessment scores. However, the results did show statistical difference in the perception of learning measures in favor of the conventional learning group. The final study utilized a subset of the population from the large-scale study for a two-fold investigation: (1) problem-solving self-efficacy scores before and after completing a complex metrology task and (2) the transfer of knowledge that was uncovered during the completion of a complex metrology task. For the former, no significant difference was found in the pre- or post- problem solving self-efficacy scores between the digital learning group and the control group. In addition, both groups experienced positive self-efficacy gains after completing the complex task. These gains were also not statistically significantly different from one another. A transfer of knowledge framework by Rebello et al, (2005) and Hutchinson (2011) was used to analyze think aloud interviews conducted during the completion of a complex task. These revealed various instances of problem feature identification (target tool), mental processes to obtain an answer (workbench), and scaffolded and spontaneous transfer. In addition, themes emerged regarding the measurement systems used and the effectiveness of the digital learning environment. The implications of this work apply to the development of digital learning environments and virtual reality tools for 2-year technology programs. The performance based findings failed to reject that hypothesis that the digital learning group performed as least as well as the conventional learning group. Thus, we can recommend use of the digital learning environment to achieve at least the same mastery level. The qualitative findings, however, showed that participants did not feel that the digital learning environment prepared them well. Therefore, further attention should be paid to the development, scaffolding, and feedback loops of the digital learning environment in order to improve the perception of participants

Extending, broadening and rethinking existing research on transfer of training

Research on transfer of training has a long history, with thousands of empirical studies since the 1950s investigating whether, and under which conditions, knowledge and skills acquired during training are subsequently used in the work environment (see reviews by Baldwin and Ford, 1988, Blume et al., 2010 and Burke and Hutchins, 2007). The generation of such an abundance of research can be linked to organisations’ fundamental and ongoing concern to ensure that their employees possess the necessary knowledge and skills from their employer to maintain a competitive advantage and thrive economically. Training and development is, however, extremely costly to organisations, which has created the need to determine the effectiveness of training, and the conditions under which transfer of training is optimal. A recent overview of “what really matters” for successful transfer of training (Grossman & Salas, 2011), aimed at a training and development readership, summarized the most influential variables emerging from this vast body of research. Based on the expectation that the list of factors which may contribute to influence transfer could always be extended and that it would be impractical to incorporate every single factor in research designs, the authors recommended a shift in future research towards deeper investigations of the conditions under which selected variables are more or less influential in their relationship with training. This Special Issue contributes to this important research agenda and extends it further through the inclusion of a diverse collection of conceptual contributions and reviews, from several scientific disciplines, a plurality of theoretical perspectives and a range of methodological approaches. Expanding the theoretical grounding underpinning empirical work on transfer of training and scrutinizing existing conceptualizations of the notion of transfer is timely in light of widespread concerns from organisations about minimal return on investment in training, and repeated evidence in the transfer of training literature of an enduring “transfer problem”. The aim of this article is to explore the value of extending, broadening and rethinking existing research on transfer of training. The benefits of extending research on transfer of training is considered first, through examining how the contributions of this Special Issue add to the existing literature on transfer of training, and the implications of the new insights for addressing the “transfer problem”. How transfer of training research could be broadened, thus enriched, through incorporating ideas from recent literature on transfer of learning is considered next. Finally, proposals to rethink transfer as boundary crossing from an activity theory perspective are scrutinized for their potential to better understand the learning that takes place at the boundaries of training and work environments. The article concludes by elaborating on the conceptual value of a refocus on ‘transfer of learning from training’ within a perspective of adaptive learning, and a call for cross-fertilisation with the extensive theory grounded literatures on transfer of learning and boundary crossing

“Transfer Talk” in Talk about Writing in Progress: Two Propositions about Transfer of Learning

This article tracks the emergence of the concept of “transfer talk”—a concept distinct from transfer of learning—and teases out the implications of transfer talk for theories of transfer of learning. The concept of transfer talk was developed through a systematic examination of 30 writing center transcripts and is defined as “the talk through which individuals make visible their prior learning (in this case, about writing) or try to access the prior learning of someone else.” In addition to including a taxonomy of transfer talk and analysis of which types occur most often in this set of conferences, this article advances two propositions about the nature of transfer of learning: (1) transfer of learning may have an important social, even collaborative, component and (2) although meta-awareness about writing has long been recognized as valuable for transfer of learning, more automatized knowledge may play an important role as well