Hasil Pencarian  ::  Simpan CSV :: Kembali

"More than 2 million books sold in the DeMYSTiFieD series! A straightforward, step-by-step approach for fast and fun mastery of everyday math from the trusted DeMYSTiFieD brand."

"Imagine that you’ve finally found a parking space after a long and harrowing search, but are now encountering some difficulty in trying to enter this space. Wouldn’t it be great if you knew a formula that allowed you to enter the space without difficulty? Are you annoyed because your soda can doesn’t remain upright during a picnic? Would you like to know why a mirror swaps right and left, but not top and bottom? Are you looking for a mathematical speech to toast your mother-in-law’s 85th birthday? Or do you want to give your heart away mathematically? Dr. Norbert Herrmann provides amusing and entertaining solutions to these and many other problems that we encounter in everyday situations."

"Penalaran deduktif adalah suatu metode berpikir logis di mana seseorang menarik kesimpulan spesifik (hipotesis) berdasarkan premis atau pernyataan umum yang dianggap benar dengan menerapkan aturan inferensi logika. Aturan inferensi logika adalah prinsip-prinsip logika yang memungkinkan seseorang untuk mengambil hipotesis yang absah dari premis yang diberikan. Meskipun penalaran deduktif memiliki keunggulan pada penalaran yang absah, manusia cenderung membuat kesalahan dalam bernalar deduktif. Salah satu model bahasa untuk penalaran deduktif adalah Natural Logic (NatLog), yaitu model berbasis machine learning yang dilatih untuk melakukan klasifikasi kelas dari hubungan persyaratan antar kalimat. Namun, model memiliki keterbatasan pada rentang kalimat yang panjang. Di sisi lain, Large Language Model (LLM) seperti Generative Pre-trained Transformer (GPT) telah menunjukkan performa yang baik dalam tugas penalaran deduktif, terutama dengan menggunakan metode Chain of Thought (CoT). Namun, metode CoT masih menimbulkan masalah halusinasi dan inkonsistensi dari langkah perantaranya, yang berujung pada konklusi akhir yang tidak absah. Metode Chain of Thought - Self-Consistency (CoT-SC) merupakan pengembangan dari metode CoT yang bertujuan untuk meningkatkan kemampuan penalaran pada LLM. Dalam metode CoT-SC, CoT dijalankan beberapa kali untuk menghasilkan beberapa sampel jawaban. Setelah itu, dilakukan operasi modus, yaitu pemilihan jawaban yang paling sering muncul di antara sampel-sampel yang dihasilkan, untuk menentukan jawaban akhir. Jawaban dengan frekuensi kemunculan terbanyak dianggap sebagai jawaban yang paling konsisten dan akurat. Tujuan dari penelitian ini adalah untuk mengimplementasikan dan menganalisis kemampuan metode CoT-SC pada model GPT dalam menyelesaikan tugas penalaran deduktif. Penelitian ini akan mengevaluasi kemampuan penalaran deduktif pada model GPT menggunakan tiga sumber data yang merepresentasikan tiga domain tugas penalaran deduktif yang berbeda, yaitu ProntoQA, ProofWriter, dan FOLIO. Setelah itu, akan dilakukan analisis perbandingan performa LLM berbasis metode CoT-SC dengan manusia dalam menyelesaikan tugas penalaran deduktif. Hasil penelitian menunjukkan bahwa metode CoT-SC menunjukkan performa akurasi yang baik dalam mayoritas tugas penalaran deduktif serta LLM GPT dengan metode CoT-SC mengungguli 1 dari 3 domain tugas penalaran deduktif. Hasil ini menunjukkan model GPT berbasis metode CoT-SC memiliki potensi dalam tugas penalaran deduktif.

---

Deductive reasoning is a method of logical thinking in which one draws specific conclusions (hypotheses) based on general premises or statements that are considered true by applying the rules of logical inference. Rules of logical inference are principles of logic that allow one to derive valid hypotheses from given premises. Although deductive reasoning has the advantage of valid reasoning, humans tend to make mistakes in deductive reasoning. One of the language models for deductive reasoning is Natural Logic (NatLog), which is a machine learning-based model trained to perform class classification of conditional relations between sentences. However, the model has limitations on long sentence ranges. On the other hand, Large Language Models (LLMs) such as Generative Pre-trained Transformer (GPT) have shown good performance in deductive reasoning tasks, especially by using the Chain of Thought (CoT) method. However, the CoT method still raises the problem of hallucinations and inconsistencies of the intermediate steps, leading to invalid final conclusions. The Chain of Thought - Self-Consistency (CoT-SC) method is a development of the CoT method that aims to improve reasoning ability in LLM. In the CoT-SC method, CoT is run several times to produce several sample answers. After that, a mode operation is performed, which is the selection of the most frequently occurring answer among the generated samples, to determine the final answer. The answer with the highest frequency of occurrence is considered the most consistent and accurate answer. The purpose of this study is to implement and analyze the ability of the CoT-SC method on the GPT model in solving deductive reasoning tasks. This study will evaluate the deductive reasoning ability of the GPT model using three data sources representing three different deductive reasoning task domains, namely ProntoQA, ProofWriter, and FOLIO. After that, a comparative analysis of the performance of LLM based on the CoT-SC method with humans in solving deductive reasoning tasks. These results indicate the GPT model based on the CoT-SC method has a potential in deductive reasoning tasks."

"ABSTRACTThe "everydayness" of the modern life, "unthinking, mundane reality" as described by Lukács in History and Class Consciousness, is often associated with the reified state of consciousness in bourgeois society. Also, linked with repetitive, uncreative, and contingent qualities of housework, the everyday tends to be gendered feminine, as Henri Lefebvre declares that "women symbolizes everyday life in its entirety" and that women are its "active critique". However, if women immerse themselves in everyday life so as to be the symbol of it, the question is: how can women acquire the revolutionary consciousness that enables them to perceive, theorize, and alter the everyday? How can women's creative work such as poetry be both the symbol and at the same time an active critique of their everyday life?This paper will examine how postwar and contemporary women poets writing in Japanese and in the proletariat literary tradition - such as Ishigaki Rin, Chong Chuwol, Park Kyongmi - have been seeking for an alternative poetic language in which they could represent and critique women's everyday life. Particular focus will be given to the interconnections between women, their domestic work, and objects used for their work, as typically seen in Ishigaki's famous poem, "In Front of me the Pot and Rice-Pot and Burning Flames" (1959). Where as Ishigaki follows realistic methods to portray women's life, Chong and Park, being second- generation Korean poets and ever uncomfortably affiliated with the Japanese language, are visibly more experimental. As a contemporary "postmodern" poet, Park argues that in poetic language, words should carefully be arranged and combined so that they cannot invoke sentiments that are attached to so-called "mother tongue". This alienated state of language, or language as butsu (thing/ object), becomes analogous to women's body, labour, and their everyday life."

"This book identifies some of the challenges, political, mathematical, community based, and pedagogical, to the mathematics register, faced by an Indigenous school, in this case a Mäori immersion school. It also details the solutions created by the collaboration of teachers, researchers and community members."