Pemrograman Visual Basic Pendeteksi Dini Kecanduan Penggunaan Smartphone Terhadap Produktivitas Kerja Menggunakan Logika Fuzzy

The development of smartphones today is very rapid. Emerging technologies, especially smartphones, have an impact on work productivity. In one day, people can access their smartphone about 15-50 times. This resulted in decreased employee productivity in a company. There are many negative things that happen if an employee uses his smartphone while working. The most important thing that will happen is the employee is less focused. If it is not focused, the job will be neglected and will not be finished. Thus resulting in a decrease in employee productivity itself. There are already many rules applied by a company, but this does not have a good impact on addiction to the use of this smart phone. With the help of fuzzy logic using the Mamdani method and applied into the app, it can detect early addictions for smartphone usage. The purpose of this study is very useful for the personnel of a company. The personnel department can quickly detect the prospective employees and can give a decision that the employee can be accepted or not into the company. With the existence of matlab calculations and applications, will facilitate the work of the personnel department in viewing the productivity of employees as early as possible. From the calculations that have been done, the results obtained that the use of smartphone addiction affect work productivity as much as 50,85 in accordance with the existing numbers on matlab calculations. This 50,85 figure shows enough part of his addiction to smartphone usage.Perkembangan smartphone saat ini sangat pesat. Dalam waktu 1 tahun bisa muncul berbagai macam dan model. Teknologi yang berkembang, khususnya smartphone, berdampak pada produktivitas kerja. Dalam satu hari, orang dapat mengakses smartphonenya mereka sekitar 15-50 kali. Hal ini mengakibatkan penurunan produktivitas kerja karyawan pada suatu perusahaan. Sudah banyak hal-hal negatif yang terjadi jika seorang karyawan menggunakan ponsel pintarnya sewaktu berkerja. Hal paling utaman yang akan terjadi adalah karyawan tersebut kurang fokus. Jika sudah kurang fokus, pekerjaan yang diperintahkan akan terbengkalai dan tidak akan selesai. Sehingga mengakibatkan penurunan produktivitas karyawan itu sendiri. Sudah banyak peraturan yang diterapkan oleh suatu perusahaan, tetapi hal ini tidak memberikan dampak yang bagus terhadap kecanduan penggunaan ponsel pintar ini. Dengan bantuan logika fuzzy menggunakan metode Mamdani dan diterapkan ke dalam aplikasi, bisa mendeteksi kecanduan dini untuk penggunaan ponsel cerdas. Tujuan penelitian ini sangat berguna untuk bagian personalia suatu perusahaan. Bagian personalia bisa mendeteksi dengan cepat calon karyawannya dan bisa memberikan keputusan bahawa karyawan tersebut bisa diterima atau tidak kedalam perusahaannya. Dengan adanya perhitungan matlab dan aplikasi, akan mempermudah pekerjaan bagian personalia tersebut dalam melihat produktivitas kerja karyawannya sedini mungkin. Dari perhitungan yang sudah dilakukan, diperoleh hasil bahwa kecanduan penggunaan smartphone mempengaruhi produktivitas kerja sebanyak 50,85 sesuai dengan angka yang ada pada perhitungan matlab. Angka 50,85 ini menunjukkan bagian yang cukup dari kecanduannya terhadap penggunaan ponsel cerdas

HFST—Framework for Compiling and Applying Morphologies

HFST–Helsinki Finite-State Technology ( hfst.sf.net ) is a framework for compiling and applying linguistic descriptions with finite-state methods. HFST currently connects some of the most important finite-state tools for creating morphologies and spellers into one open-source platform and supports extending and improving the descriptions with weights to accommodate the modeling of statistical information. HFST offers a path from language descriptions to efficient language applications in key environments and operating systems. HFST also provides an opportunity to exchange transducers between different software providers in order to get the best out of each finite-state library.Peer reviewe

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

A Language for Specifying Compiler Optimizations for Generic Software

Compiler optimization is important to software performance, and modern processor architectures make optimization even more critical. However, many modern software applications use libraries providing high levels of abstraction. Such libraries often hinder effective optimization—the libraries are difficult to analyze using current compiler technology. For example, high-level libraries often use dynamic memory allocation and indirectly expressed control structures, such as iterator-based loops. Programs using these libraries often cannot achieve an optimal level of performance. On the other hand, software libraries have also been recognized as potentially aiding in program optimization. One proposed implementation of library-based optimization is to allow the library author, or a library user, to define custom analyses and optimizations. Only limited systems have been created to take advantage of this potential, however. One problem in creating a framework for defining new optimizations and analyses is how users are to specify them: implementing them by hand inside a compiler is difficult and prone to errors. Thus, a domain-specific language for library-based compiler optimizations would be beneficial. Many optimization specification languages have appeared in the literature, but they tend to be either limited in power or unnecessarily difficult to use. Therefore, I have designed, implemented, and evaluated the Pavilion language for specifying program analyses and optimizations, designed for library authors and users. These analyses and optimizations can be based on the implementation of a particular library, its use in a specific program, or on the properties of a broad range of types, expressed through concepts. The new system is intended to provide a high level of expressiveness, even though the intended users are unlikely to be compiler experts

Improving Programming Support for Hardware Accelerators Through Automata Processing Abstractions

The adoption of hardware accelerators, such as Field-Programmable Gate Arrays, into general-purpose computation pipelines continues to rise, driven by recent trends in data collection and analysis as well as pressure from challenging physical design constraints in hardware. The architectural designs of many of these accelerators stand in stark contrast to the traditional von Neumann model of CPUs. Consequently, existing programming languages, maintenance tools, and techniques are not directly applicable to these devices, meaning that additional architectural knowledge is required for effective programming and configuration. Current programming models and techniques are akin to assembly-level programming on a CPU, thus placing significant burden on developers tasked with using these architectures. Because programming is currently performed at such low levels of abstraction, the software development process is tedious and challenging and hinders the adoption of hardware accelerators. This dissertation explores the thesis that theoretical finite automata provide a suitable abstraction for bridging the gap between high-level programming models and maintenance tools familiar to developers and the low-level hardware representations that enable high-performance execution on hardware accelerators. We adopt a principled hardware/software co-design methodology to develop a programming model providing the key properties that we observe are necessary for success, namely performance and scalability, ease of use, expressive power, and legacy support. First, we develop a framework that allows developers to port existing, legacy code to run on hardware accelerators by leveraging automata learning algorithms in a novel composition with software verification, string solvers, and high-performance automata architectures. Next, we design a domain-specific programming language to aid programmers writing pattern-searching algorithms and develop compilation algorithms to produce finite automata, which supports efficient execution on a wide variety of processing architectures. Then, we develop an interactive debugger for our new language, which allows developers to accurately identify the locations of bugs in software while maintaining support for high-throughput data processing. Finally, we develop two new automata-derived accelerator architectures to support additional applications, including the detection of security attacks and the parsing of recursive and tree-structured data. Using empirical studies, logical reasoning, and statistical analyses, we demonstrate that our prototype artifacts scale to real-world applications, maintain manageable overheads, and support developers' use of hardware accelerators. Collectively, the research efforts detailed in this dissertation help ease the adoption and use of hardware accelerators for data analysis applications, while supporting high-performance computation.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155224/1/angstadt_1.pd