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"Excellent business communication skills are especially important for information management professionals, particularly records managers, who have to communicate a complex idea: how an effective program can help the organization be better prepared for litigation, and do it in a way that is persuasive in order to win records program support and budget. Six key communication skills for records and information managers explores those skills that enable records and information to have a better chance of advancing their programs and their careers. Following an introduction from the author, this book will focus on six key communication skills: be brief, be clear, be receptive, be strategic, be credible and be persuasive. Honing these skills will enable readers to more effectively obtain support for strategic programs, communicate more effectively with senior management, IT personnel and staff, and master key forms of business communication including written, verbal and formal presentations. The final chapter will highlight one of the most practical applications of applying the skills for records and information managers: the business case. Based on real events, the business cases spotlighted involve executives who persuaded organizations to adopt new programs. These case histories bring to life many of the six keys to effective communication."

"[This volume contains the lectures given in honor to Georg Färber as tribute to his contributions in the area of real-time and embedded systems. The chapters of many leading scientists cover a wide range of aspects, like robot or automotive vision systems or medical aspects., This volume contains the lectures given in honor to Georg Färber as tribute to his contributions in the area of real-time and embedded systems. The chapters of many leading scientists cover a wide range of aspects, like robot or automotive vision systems or medical aspects.]"

"Popularitas telepon pintar dan aplikasi seluler membuat unduhan dan pengguna aplikasi meningkat secara eksponensial. Pengguna dapat memberikan ulasan terkait dengan penggalaman menggunakan aplikasi, ulasan ini dapat berisi keluhan atau saran yang berharga untuk dikaji lebih lanjut. Namun jumlah ulasan yang sangat banyak menyulitkan untuk mencari dan memahami informasi yang terkandung pada teks ulasan. Untuk mengatasi permasalahan tersebut pada penelitian ini mengusulkan model yang dapat menggali informasi serta mengkategorikan konten dan sentimen ulasan dengan menggunakan teknik pembelajaran mesin. Algoritme SentiStrength, Support Vector Machine SVM , Na ve Bayes, Logistic Regresion, Latent Dirichlet Allocation LDA dan Non-negative Matrix Factorization NMF digunakan pada penelitian ini. Hasil dari penelitian didapatkan rerata presisi sentimen ulasan mencapai 85 dan algoritme terbaik untuk klasifikasi konten ulasan didapatkan menggunakan SVM dengan nilai rerata f1-score 84.38 menggunakan fitur unigram sedangkan NMF berkerja lebih baik daripada LDA untuk menemukan topik pada teks ulasan.

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The popularity of smartphones and mobile applications makes app downloads and users of applications rises exponentially. Users can provide reviews related to their experience during using the app, these reviews may contain valuable complaints or suggestions which can be used for further in depth review based on the reviews given before. However, the large number volume of the reviews can make it very difficult to find and understand the information contained in a review. To solve the problem in this study proposes a model that can diging information by categorizing the content and sentiment reviews using machine learning technique. The algorithm SentiStrength, Support Vector Machine SVM , Na ve Bayes, Logistic Regression, Latent Dirichlet Allocation LDA and Non-negative Matrix Factorization NMF are used in this study. The result of the research shows that the average sentiment precision of review is 85 and the best algorithm for the review content classification is obtained using SVM with an average f1-score 84.38 using unigram feature whereas the NMF works better than LDA to find topics in a reviews."