『Google Scholar』の利点

Table of Contents CHAPTER 1 INTRODUCTION 1.1 WHAT IS AN OPERATING SYSTEM? 1.2 HISTORY OF OPERATING SYSTEMS 1.3 OPERATING SYSTEM CONCEPTS 1.4 SYSTEM CALLS 1.5 OPERATING SYSTEM STRUCTURE 1.6 OUTLINE OF THE REST OF THIS BOOK 1.7 SUMMARY CHAPTER 2 PROCESSES 2.1 INTRODUCTION TO PROCESSES 2.2 INTERPROCESS COMMUNICATION 2.3 CLASSICAL IPC PROBLEMS 2.4 SCHEDULING 2.5 OVERVIEW OF PROCESSES IN MINIX 3 2.6 IMPLEMENTATION OF PROCESSES IN MINIX 3 2.7 THE SYSTEM TASK IN MINIX 3 2.8 THE CLOCK TASK IN MINIX 3 2.9 SUMMARY CHAPTER 3 INPUT/OUTPUT 3.1 PRINCIPLES OF I/O HARDWARE 3.2 PRINCIPLES OF I/O SOFTWARE 3.3 DEADLOCKS 3.4 OVERVIEW OF I/O IN MINIX 3 3.5 BLOCK DEVICES IN MINIX 3 3.6 RAM DISKS 3.7 DISKS 3.8 TERMINALS 3.9 SUMMARY CHAPTER 4 MEMORY MANAGEMENT 4.1 BASIC MEMORY MANAGEMENT 4.2 SWAPPING 4.3 VIRTUAL MEMORY 4.4 PAGE REPLACEMENT ALGORITHMS 4.5 DESIGN ISSUES FOR PAGING SYSTEMS 4.6 SEGMENTATION 4.7 OVERVIEW OF THE MINIX 3 PROCESS MANAGER 4.8 IMPLEMENTATION OF THE MINIX 3 PROCESS MANAGER 4.9 SUMMARY CHAPTER 5 FILE SYSTEMS 5.1 FILES 5.2 DIRECTORIES 5.3 FILE SYSTEM IMPLEMENTATION 5.4 SECURITY 5.5 PROTECTION MECHANISMS 5.6 OVERVIEW OF THE MINIX 3 FILE SYSTEM 5.7 IMPLEMENTATION OF THE MINIX 3 FILE SYSTEM 5.8 SUMMARY CHAPTER 6 READING LIST AND BIBLIOGRAPHY 6.1 SUGGESTIONS FOR FURTHER READING 6.2 ALPHABETICAL BIBLIOGRAPHY APPENDIX A - INSTALLING MINIX 3 APPENDIX B - MINIX 3 SOURCE CODE LISTING APPENDIX C - INDEX TO FILES INDEX

Operating Systems: Design and Implementation

Preface. Automatic Train Operation System by Predictive Fuzzy Control (S. Yasunobu, S. Miyamoto). Application of Fuzzy Reasoning to the Water Purification Process (O. Yagashita, O. Itoh, M. Sugeno). The Application of a Fuzzy Controller to the Control of a Multi-Degree-of-Freedom Robot Arm (E.M. Scharf, N.J. Mandic). Optimizing Control of a Diesel Engine (Y. Murayama et al.). Development of Performance Adaptive Fuzzy Controllers with Application to Continuous Casting Plants (G. Bartolini et al.). A Fuzzy Logic Controller for Aircraft Flight Control (L.I. Larkin). Automobile Speed Control System Using a Fuzzy Logic Controller (S. Murakami, M. Maeda). An Experimental Study on Fuzzy Parking Control Using a Model Car (M. Sugeno, K. Murakami). A Fuzzy Controller in Turning Process Automation (Y. Sakai, K. Ohkusa). Design of Fuzzy Control Algorithms with the Aid of Fuzzy Models (W. Pedrycz). Human Operator's Fuzzy Model in Man-Machine System with a Nonlinear Controlled Object (K. Matsushima, H. Sugiyama). The Influence of Some Parameters on the Accuracy of a Fuzzy Model (J.B. Kiszka, M.E. Kochanska, D.S. Sliwinska). A Microprocessor Based Fuzzy Controller for Industrial Purposes (T. Yamazaki, M. Sugeno). The Application of Fuzzy and Artificial Intelligence Methods in the Building of a Blast Furnace Smelting Process Model (H. Zhao, M. Ma). An Annotated Bibliography of Fuzzy Control (R.M. Tong).

Industrial applications of fuzzy control

Information on the size of academic search engines and bibliographic databases (ASEBDs) is often outdated or entirely unavailable. Hence, it is difficult to assess the scope of specific databases, such as Google Scholar. While scientometric studies have estimated ASEBD sizes before, the methods employed were able to compare only a few databases. Consequently, there is no up-to-date comparative information on the sizes of popular ASEBDs. This study aims to fill this blind spot by providing a comparative picture of 12 of the most commonly used ASEBDs. In doing so, we build on and refine previous scientometric research by counting query hit data as an indicator of the number of accessible records. Iterative query optimization makes it possible to identify a maximum number of hits for most ASEBDs. The results were validated in terms of their capacity to assess database size by comparing them with official information on database sizes or previous scientometric studies. The queries used here are replicable, so size information can be updated quickly. The findings provide first-time size estimates of ProQuest and EbscoHost and indicate that Google Scholar’s size might have been underestimated so far by more than 50%. By our estimation Google Scholar, with 389 million records, is currently the most comprehensive academic search engine.

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Google Scholar to overshadow them all? Comparing the sizes of 12 academic search engines and bibliographic databases

In the past years, traditional pattern recognition methods have made great progress. However, these methods rely heavily on manual feature extraction, which may hinder the generalization model performance. With the increasing popularity and success of deep learning methods, using these techniques to recognize human actions in mobile and wearable computing scenarios has attracted widespread attention. In this paper, a deep neural network that combines convolutional layers with long short-term memory (LSTM) was proposed. This model could extract activity features automatically and classify them with a few model parameters. LSTM is a variant of the recurrent neural network (RNN), which is more suitable for processing temporal sequences. In the proposed architecture, the raw data collected by mobile sensors was fed into a two-layer LSTM followed by convolutional layers. In addition, a global average pooling layer (GAP) was applied to replace the fully connected layer after convolution for reducing model parameters. Moreover, a batch normalization layer (BN) was added after the GAP layer to speed up the convergence, and obvious results were achieved. The model performance was evaluated on three public datasets (UCI, WISDM, and OPPORTUNITY). Finally, the overall accuracy of the model in the UCI-HAR dataset is 95.78%, in the WISDM dataset is 95.85%, and in the OPPORTUNITY dataset is 92.63%. The results show that the proposed model has higher robustness and better activity detection capability than some of the reported results. It can not only adaptively extract activity features, but also has fewer parameters and higher accuracy.

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LSTM-CNN Architecture for Human Activity Recognition

Big Data Computing Using Cloud-Based Technologies

Educational Intelligence is a broad area of big data analytical applications that make use of big data technologies for implementation of solutions for education and research. This paper demonstrates the designing, development and deployment of an educational intelligence application for real-world scenarios. Firstly, a quality assessment framework for higher education systems that evaluate institutions on the basis of performance of outgoing students was proposed. Secondly, big data enabled technological setup was used for its implementation. Literature was surveyed to evaluate existing quality frameworks. Most existing quality assessment systems take into account the dimensions related to inputs, processes and outputs, but they tend to ignore the perspective that assesses the institution on the basis of outcome of the educational process. This paper demonstrates the use of outcome perspective to compute quality metrics and create visual analytics. In order to implement and test the framework, R programming language and a cloud based big data technology that is Google, BigQuery were used.

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Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems

IoT (Internet of Things) is connecting inter-related computing devices and the main idea for this concept is to connect each device via internet so that it will be easy for the devices to communicate and share info among them. These days IoT has grown up to a massive extent and new and emerging topic in research. IoT is the new and vast technology for the upcoming future as almost all devices will be connected to each other and share data among them via internet. This chapter identifies various privacy challenges in IoT, and their respective solutions presented by several researches over the time. It presents various issues related to privacy and security in IoT like standardization, networking and addressing ones. The chapter also deals within solutions provided for academic, technical and industrial aspects for IoT enabled environment. The importance of data security and protection to overcome the vulnerabilities which can come for an IoT based environment and can reduce the risk for IoT infrastructure.

Privacy Challenges and Their Solutions in IoT

Cloud environment is usually associated with non-homogeneity and dynamicity in terms of resource usage and access at all levels. The study of this heterogeneous and non-uniform behavior is therefore an important problem. Google cluster trace which is a production trace released by Google in November 2014 serves as an example of a high scale Cloud environment. This paper deals with statistical analysis of this cluster trace. Since the size of production trace is very huge therefore, Hive which is a HadoopDistributed File System (HDFS) based platform for querying and analysis of big data, has been used. Hive was accessed through its Beeswax interface. The data was imported into HDFS through HCatalog. Apart from Hive, Pig which is a scripting language and provides abstraction on top of Hadoop was used. The method adopted deals with clustering and studying the distribution of arrival time of jobs, distribution of resource usage and also study of distribution of process runtime. To the best of our knowledge the analytical method adopted by us is novel. The findings revealed that jobs in a production trace can be classified into major, mediocre and minor resource usage types. Furthermore, it can be concluded from our study that arrival time of jobs followed weibull distribution. Usage of resources such as CPU and memory was observed to be following a zipf like distribution while study of process runtime shows that some jobs had very small values of runtime while others had very large values hence they followed heavy tailed distribution. Our analysis will help researchers in properly understanding the nonhomogenous and dynamic behavior synonymous with cloud environment. It will also help them in developing new algorithms for resource allocation and scheduling in Cloud.

Exploring Non-Homogeneity and Dynamicity of High Scale Cloud through Hive and Pig

With the advent of Internet of Things (IoT), anything on earth with embedded processor, storage, and communication technology can communicate with each other. IoT can interconnect billions and trillions of devices on earth. This is considered as next revolution in the world of internet. It is expected that this revolution will drastically improve quality of daily life, will bring new forms of collaboration, interaction, and activities. IoT is not considered a single technology, but it is an aggregation of various underlying technologies, making the application development task bit challenging. In order to cope up with this challenge, number of vendors are coming up with IoT platforms for application development. IoT platforms provide support for connecting, storing, computing, and analysing data from heterogeneous devices. This chapter, presents a reference architecture for IoT service platforms, outline a set of service and architectural requirements for IoT platform, and review four major IoT platforms (AWS IoT platform, IBM Watson platform, Microsoft Azure IoT Platform, and Google Cloud Platform) from these requirements viewpoint. Further, gaps and issues in present IoT platforms are discussed with future research directions.

Mansaf Alam

Papers

Big Data Computing Using Cloud-Based Technologies

Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems

Privacy Challenges and Their Solutions in IoT

Exploring Non-Homogeneity and Dynamicity of High Scale Cloud through Hive and Pig

Open Service Platforms for IoT