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Order and chaos in the ancient Greco-Roman philosophical imagination
When did chaos come to be opposed to order? This paper considers the earliest references in the Western world to the concepts of “chaos” (Xάος) and “order” (κόσμος), understood as cosmological concepts; these terms are first attested in the epic Theogony of the ancient Greek poet Hesiod and the treatise On Nature of the Pythagorean philosopher Philolaus of Croton. This paper argues by way of a close reading of these texts that originally chaos was instrumental to an orderly Universe and that this idea persisted in the formal development of cosmological texts in the Greek world. The paper concludes by suggesting that the first person in the Western world to make chaos the opposite of order, i.e. absence of order or disorder, was the Roman epic poet Ovid in his celebrated Metamorphoses some seven hundred years after Hesiod first accounted for the role of chaos in instantiating the world order.
Journal Article
Text Classification Algorithms: A Survey
In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in real-world problems are discussed.
Journal Article
The Politics of Education Reform in the Middle East
Education systems and textbooks in selected countries of the Middle East are increasingly the subject of debate. This volume presents and analyzes the major trends as well as the scope and the limits of education reform initiatives undertaken in recent years. In curricula and teaching materials, representations of the \"Self\" and the \"Other\" offer insights into the contemporary dynamics of identity politics. By building on a network of scholars working in various countries in the Middle East itself, this book aims to contribute to the evolution of a field of comparative education studies in this region.
eBook
Effects of Classroom Practices on Reading Comprehension, Engagement, and Motivations for Adolescents
We investigated the roles of classroom supports for multiple motivations and engagement in students' informational text comprehension, motivation, and engagement. A composite of classroom contextual variables consisting of instructional support for choice, importance, collaboration, and competence, accompanied by cognitive scaffolding for informational text comprehension, was provided in four-week instructional units for 615 grade 7 students. These classroom motivational-engagement supports were implemented within integrated literacy/history instruction in the Concept-Oriented Reading Instruction (CORI) framework. CORI increased informational text comprehension compared with traditional instruction (TI) in a switching replications experimental design. Students' perceptions of the motivational-engagement supports were associated with increases in students' intrinsic motivation, value, perceived competence, and increased positive engagement (dedication) more markedly in CORI than in TI, according to multiple regression analyses. Results extended the evidence for the effectiveness of CORI to literacy/history subject matter and informational text comprehension among middle school students. The experimental effects in classroom contexts confirmed effects from task-specific, situated experimental studies in the literature.
Journal Article
Reading Text in the Wild with Convolutional Neural Networks
In this work we present an end-to-end system for text spotting—localising and recognising text in natural scene images—and text based image retrieval. This system is based on a region proposal mechanism for detection and deep convolutional neural networks for recognition. Our pipeline uses a novel combination of complementary proposal generation techniques to ensure high recall, and a fast subsequent filtering stage for improving precision. For the recognition and ranking of proposals, we train very large convolutional neural networks to perform word recognition on the whole proposal region at the same time, departing from the character classifier based systems of the past. These networks are trained solely on data produced by a synthetic text generation engine, requiring no human labelled data. Analysing the stages of our pipeline, we show state-of-the-art performance throughout. We perform rigorous experiments across a number of standard end-to-end text spotting benchmarks and text-based image retrieval datasets, showing a large improvement over all previous methods. Finally, we demonstrate a real-world application of our text spotting system to allow thousands of hours of news footage to be instantly searchable via a text query.
Journal Article