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"This book constitutes the refereed proceedings of the 7th International Conference on Sequences and Their Applications, SETA 2012, held in Waterloo, Canada, in June 2012. The 28 full papers presented together with 2 invited papers in this volume were carefully reviewed and selected from 48 submissions. The papers are grouped in topical sections on perfect sequences; finite fields, boolean functions, Golomb 80th birthday session, linear complexity, frequency hopping, correlation of sequences, bounds on sequences, cryptography, aperiodic correlation, and Walsh transform."

"This book provides a modern introduction to harmonic analysis and synthesis on topological groups. It serves as a guide to the abstract theory of Fourier transformation. For the first time, it presents a detailed account of the theory of classical harmonic analysis together with the recent developments in spectral analysis and synthesis."

"This book gives a comprehensive review of results for associated sequences and demimartingales developed so far, with special emphasis on demimartingales and related processes. Probabilistic properties of associated sequences, demimartingales and related processes are discussed in the first six chapters. Applications of some of these results to some problems in nonparametric statistical inference for such processes are investigated in the last three chapters."

"In this one-of-a-kind book, Ralph Grimaldi uses his extensive experience from the classroom and as a leader of mini-courses to present an accessible, single resource on the topics of Fibonacci Numbers and Catalan Numbers. The book first embarks on a complete treatment of Fibonacci numbers. Starting with a historical background on the topic, the author goes on to present the properties of Fibonacci numbers, a slew of introductory-level examples, and in-depth discussion of related topics including compositions and palindromes; tiling and Fibonacci numbers; solving linear recurrence relations; graph theory; Lucas numbers; and alternate Fibonacci numbers. The second half of the book explores Catalan numbers, and the author builds a complete foundation to the topic using a historical background and introductory examples, along with coverage of partial orders, total orders, topological sorting, graph theory, rooted ordered binary trees, pattern avoidance, and the Narayana numbers. Coverage of both topics are accompanied by interesting, real-world examples from areas such as sports, botany, and computer science. Each section concludes with detailed exercise sets that can also serve as extended examples of the presented material along with selected solutions. An Instructor’s Manual featuring complete solutions is available upon written request, and extensive reference sections outline resources for further study of the discussed topics."

"Skripsi ini membahas penerapan Long Short Term Memory RNN dan Manhattan Distance untuk membuat rancangan Sistem Penilaian Esai Otomatis (SIMPLE-O). SIMPLE-O adalah sistem yang sedang dikembangkan Departemen Teknik Elektro UI untuk menilai esai secara otomatis. Sistem ini menggunakan Recurrent Neural Network dengan arsitektur Long Short Term Memory untuk memberikan nilai pada esai Bahasa Jepang. Dari beberapa variasi yang diuji, model yang paling stabil adalah model yang memiliki layer LSTM, Manhattan Distance, dan Dropout dengan dropout rate sebesar 0.3, di-train selama 25 epoch dengan loss function crosscategorical entropy dan optimizer adam, dengan input model ditokenisasi per karakter dengan rata-rata akurasi sebesar 79.93%.

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This thesis will explore the application of Long Short Term Memory RNN and Manhattan Distance in designing the Automatic Essay Grading System (SIMPLE-O). SIMPLE-O is a system currently being developed by Departemen Teknik Elektro UI for automatically scoring Japanese essay exams.  Out of the variations tested, the most stable model is the model with the layers LSTM, Manhattan distance, and Dropout with a dropout rate of 0.3, trained for 25 epochs with the loss function cross categorical entropy and adam optimizer, and the model's input being tokenized by character with the highest average accuracy of 79.93%."