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"Understanding augmented reality addresses the elements that are required to create augmented reality experiences. The technology that supports augmented reality will come and go, evolve and change. The underlying principles for creating exciting, useful augmented reality experiences are timeless.Augmented reality designed from a purely technological perspective will lead to an AR experience that is novel and fun for one-time consumption - but is no more than a toy. Imagine a filmmaking book that discussed cameras and special effects software, but ignored cinematography and storytelling! In order to create compelling augmented reality experiences that stand the test of time and cause the participant in the AR experience to focus on the content of the experience - rather than the technology - one must consider how to maximally exploit the affordances of the medium.Understanding augmented reality addresses core conceptual issues regarding the medium of augmented reality as well as the technology required to support compelling augmented reality. By addressing AR as a medium at the conceptual level in addition to the technological level, the reader will learn to conceive of AR applications that are not limited by today’s technology. At the same time, ample examples are provided that show what is possible with current technology."

"Skizofrenia adalah gangguan mental berat yang ditandai oleh psikosis, yang menyebabkan hilangnya hubungan dengan kenyataan dan gangguan komunikasi. Fase prodromal, yang terjadi sebelum munculnya psikosis, penting untuk diidentifikasi, terutama pada remaja, karena dapat berkembang menjadi skizofrenia. Deteksi dini gangguan berpikir, yang merupakan gejala dari gangguan bicara, sangatlah penting. Kemajuan terbaru dalam bidang Natural Language Processing (NLP) memberikan wawasan yang berharga untuk mendiagnosis gangguan berpikir. Penelitian kami menggunakan teknik NLP yang diintegrasikan ke dalam model berbasis IndoBERT Transformer untuk mengklasifikasikan gangguan pikiran secara efektif. Kami bertujuan untuk meningkatkan akurasi dan keandalan diagnosis, dengan mempertimbangkan kasus-kasus prodromal dan menggunakan skala TALD yang divalidasi untuk penilaian. Hasil penelitian menunjukkan bahwa penggunaan kombinasi fitur optimasi dan IndoBERT berhasil mencapai tingkat akurasi sebesar 72,2% dan rata-rata tertimbang F1 sebesar 70,2%. Analisis kami menunjukkan bahwa model ini memiliki potensi yang baik dalam mendeteksi gangguan berpikir.

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Schizophrenia is a severe mental disorder characterized by psychosis, leading to a disconnect from reality and disrupted communication. The prodromal phase, preceding psychosis, is essential to identify, particularly in adolescents, as it can progress into schizophrenia. Early detection of thought disorder, a symptom of disorganized speech, is crucial. Recent advancements in Natural Language Processing (NLP) provide valuable insights into diagnosing thought disorders. Our study leverages NLP techniques integrated into the IndoBERT Transformer-based model to classify thought disorders effectively. We aim to improve diagnosis accuracy and reliability, considering prodromal cases and using the validated TALD scale for assessment. The results showed that using a combination of optimized and IndoBERT features managed to obtain an accuracy rate of 72.2% and an F1 weighted average of 70.2%. The analysis shows that this model has good potential in detecting thought disorders."

"Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics."