『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

Management of data in education sector particularly management of data for big universities with several employees, departments and students is a very challenging task. There are also problems such as lack of proper funds and manpower for management of such data in universities. Education sector can easily and effectively take advantage of cloud computing skills for management of data. It can enhance the learning experience as a whole and can add entirely new dimensions to the way in which education is imbibed. Several benefits of cloud computing such as monetary benefits, environmental benefits and remote data access for management of data such as university database can be used in education sector. Therefore, in this paper we have proposed an effective framework for managing university data using a cloud based environment. We have also proposed cloud data management simulator: a new simulation framework which demonstrates the applicability of cloud in the current education sector. The framework consists of a cloud developed for processing a universities database which consists of staff and students. It has the following features (i) support for modeling cloud computing infrastructure, which includes data centers containing university database; (ii) a user friendly interface; (iii) flexibility to switch between the different types of users; and (iv) virtualized access to cloud data.

An effective framework for managing university data using a cloud based environment

Recent research shows that colluded malware in different VMs sharing physical host may use access latency of a resource as a covert channel to leak critical information. Covert channels employ time characteristics to transmit confidential information to attackers. In this manuscript we have made two important contributions and to the best of our knowledge they are novel. One is to propose a framework for detecting covert channel attack based anomalies in cloud using machine learning . This framework is based on application criticality where the degree of error tolerance will be based on the sensitivity of application. This is a useful feature for cloud based applications as SLAs are trade off between service and cost. There are two modules of the framework, a cluster monitor and another module becomes part of hypervisor for monitoring purposes. Second contribution is we propose an approach to distribute the machine learning technique in such a manner to be able to handle imbalance learning as well as enabling SVM to handle large datasets.

Covert Channel Detection framework for cloud using distributed machine learning.

Academic performance of any individual is dependent upon numerous aspects regarding the day to day life of the individual under consideration. Academic performance is measured in terms of the grade point average or GPA as it is called. Grade point average is dependent not only on the faculty but also on various psychological parameters including the study habits, social anxiety and allied. In this study, a detail analysis of numerous psychological factors impacting the grade point was carried and based upon various psychological factors the performance for the student in forth coming examination was forecasted.

Multidimensional Analysis of Psychological Factors affecting Students Academic Performance.

Cloud computing has the ability to share data via public channel. Digital identity is needed for clients to use facilities of cloud in cloud computing. Presently, public key and asymmetric cryptography are used for most of cloud-based system to provide safe communication. Due to its characteristics, ID-based cryptography is very important in cloud domain. Our framework has three phases like as initialization, user registration and authentication, and key agreement phase. Also, we have compared our work with other works in the same domain and also found that our work is more secure than others. In our framework, user and server communicates to each other and establish key agreement.

A Secure Key Agreement Framework for Cloud Computing Using ECC

Data is growing at faster speed. In cloud, fast processing has become essential need to process hefty data. The earlier EGENMR system was developed for process large amount of data present in cloud repositories with the help of MapReduce functions we concluded that the system is better than the latest techniques for processing large amount of data. In this paper, we are enhancing EGENMR by further enhancing the speed of database query operation by using GPU as a co-processor. A theoretical comparison is made in terms of time taken and complexity for hybrid query processing using GPU and EGENMR.

Mansaf Alam

Papers

An effective framework for managing university data using a cloud based environment

Covert Channel Detection framework for cloud using distributed machine learning.

Multidimensional Analysis of Psychological Factors affecting Students Academic Performance.

A Secure Key Agreement Framework for Cloud Computing Using ECC

Accelerating EGENMR Database Operations Using GPU Processing