Effect of concept map supported teaching approaches from rules to sample and sample to rules to grammar teaching

2nd World Conference on Educational Sciences (WCES-2010) -- FEB 04-08, 2010 -- Bahceschir Univ, Istanbul, TURKEYWOS: 000282002803154In this study, the effect of approach from rules-to-sample and sample-to-rules to the teaching of grammar subjects has been analysed. While treating grammar subjects from rules-to-sample and sample-to-rules learning-teaching process in both approaches are supported by the concept map. Application has been maintained for six weeks and data were obtained by applying more assesment instruments to students taking part in experimental and control groups. In practice, pretest - post test model was applied. At the end of the learning-teaching process, achievements of students have been assesed by a variety of assesment instruments, the data gathered has been analysed with the help of statistical techniques such as one-way variance analysis, "t" test, arithmetic averages. 96 students at the level of fourth grade participated the application process; 30 of them participated application from rules-to-sample; 33 students participated application from sample to rules in an active manner, and 33 students in the control group continued to traditional teaching. Assesments have been analysed and results have been compared. As a result of research, results that participants obtained were compared in terms of variables such as students' participation level to teaching process, the time students spent for learning, students' rememberance level of what they have learned. In terms of foregoing variables, meaningful results were obtained in favor of approach from sample to rule. By taking into account of the results obtained, some suggestions have been done aiming to teaching done by teaching strategies and concept maps. (C) 2010 Elsevier Ltd. All rights reserved

Learning from, Understanding, and Supporting DevOps Artifacts for Docker

With the growing use of DevOps tools and frameworks, there is an increased need for tools and techniques that support more than code. The current state-of-the-art in static developer assistance for tools like Docker is limited to shallow syntactic validation. We identify three core challenges in the realm of learning from, understanding, and supporting developers writing DevOps artifacts: (i) nested languages in DevOps artifacts, (ii) rule mining, and (iii) the lack of semantic rule-based analysis. To address these challenges we introduce a toolset, binnacle, that enabled us to ingest 900,000 GitHub repositories. Focusing on Docker, we extracted approximately 178,000 unique Dockerfiles, and also identified a Gold Set of Dockerfiles written by Docker experts. We addressed challenge (i) by reducing the number of effectively uninterpretable nodes in our ASTs by over 80% via a technique we call phased parsing. To address challenge (ii), we introduced a novel rule-mining technique capable of recovering two-thirds of the rules in a benchmark we curated. Through this automated mining, we were able to recover 16 new rules that were not found during manual rule collection. To address challenge (iii), we manually collected a set of rules for Dockerfiles from commits to the files in the Gold Set. These rules encapsulate best practices, avoid docker build failures, and improve image size and build latency. We created an analyzer that used these rules, and found that, on average, Dockerfiles on GitHub violated the rules five times more frequently than the Dockerfiles in our Gold Set. We also found that industrial Dockerfiles fared no better than those sourced from GitHub. The learned rules and analyzer in binnacle can be used to aid developers in the IDE when creating Dockerfiles, and in a post-hoc fashion to identify issues in, and to improve, existing Dockerfiles.Comment: Published in ICSE'202

Finding Influential Users in Social Media Using Association Rule Learning

Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods

Optimal learning rules for discrete synapses

There is evidence that biological synapses have a limited number of discrete weight states. Memory storage with such synapses behaves quite differently from synapses with unbounded, continuous weights, as old memories are automatically overwritten by new memories. Consequently, there has been substantial discussion about how this affects learning and storage capacity. In this paper, we calculate the storage capacity of discrete, bounded synapses in terms of Shannon information. We use this to optimize the learning rules and investigate how the maximum information capacity depends on the number of synapses, the number of synaptic states, and the coding sparseness. Below a certain critical number of synapses per neuron (comparable to numbers found in biology), we find that storage is similar to unbounded, continuous synapses. Hence, discrete synapses do not necessarily have lower storage capacity

Nonlinear Hebbian learning as a unifying principle in receptive field formation

The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely Nonlinear Hebbian Learning. When Nonlinear Hebbian Learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio