『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.

/pdf/lstm-cnn-architecture-for-human-activity-recognition-4eivwisqnc.pdf

LSTM-CNN Architecture for Human Activity Recognition

Object-Oriented Technology is an important discipline in the field of software engineering in general and it is, therefore, natural to ask whether it is relevant to the field of database management in particular and what that relevance is. There is, however, no consensus on answer to these questions. Some authorities believe that object oriented database systems will take over the world replacing relational system whereas others believe that they are suited only to certain very specific problems and will never capture more than a small fraction of the overall market. Object-Oriented technology represents real world very well, focusing on data rather than procedure and gives more security to data. Also, it is safe from unauthorized use of data because it provides three access specifiers viz. private, public and protected and strictly provides security to data in database. This technology also provides function as well as data together so that the data can be manipulated by the given function. In this paper, we show that how the data is more secure in object-oriented database than in relational database and also why do we migrate from RDBMS into OODBMS.

/pdf/migration-from-relational-database-into-object-oriented-98k4sqjegh.pdf

Migration from Relational Database into Object Oriented Database

Recent technological advancements in typical domains (e.g. internet, financial companies, health care, user generated data, supply chain systems etc.) have directed to inundate of data from these domains. Data outburst trend gave the insight meaning to the buzz word ‘Bigdata’. If we compare with traditional data, Bigdata exhibits some unique characteristics like it is commonly enormous and unstructured type of data that cannot be handled using traditional databases. Hence new system designs are required for the following processes i.e. data collection, data transmission, storage, and large-scale data processing mechanisms. The definition of Bigdata has been presented from many aspects in this paper. We analyzed Bigdata system architecture and various challenges of Bigdata. The prevalent Hadoop framework, Hive, No SQL, New SQL, MapReduce and HBase for addressing the biggest challenge of Bigdata i.e Data Analytic has also been analyzed and compared.

Bigdata analysis and comparison of bigdata analytic approches

Data is the heart of any system, which is perhaps the reason why big data is such a ‘big’ deal. With the growing application domain of the technology, research towards big data analytical applications’ development has gained immense attention lately. The cost-effective and scalable nature of Cloud Computing has made it an obvious infrastructural choice. However, until APIs are developed for provisioning analytical services, big data research will not meet its desired outcome. Moreover, existing frameworks are application-specific or cater to only a specific set of requirements. This paper proposes a framework for creating a technological stack for Big Data-as-a-Service. Besides this, it also presents criteria for technology selection, which is based on the identification of computing and data models for the application. Therefore, the proposed framework is application-independent and accommodates all the segments of the big data lifecycle.

On Designing a Generic Framework for Big Data-as-a-Service

This paper discusses covert channel attacks in VMM, with emphasis on some of its important detection techniques. This is an active research area whereby many security frameworks have been proposed for isolating VMs, but still covert channels in commercial clouds exist. Discussion in detail (with examples and expressions) has been done as to how information flow leads to covert channel and the methods used for covert channel detection. In this paper various scenarios are discussed where efforts have been made to detect all possible covert channels and counter the attacks. Among the basic four research areas in the study of covert channels (Define, Detect, Measure and Mitigate), Detection is the most challenging. This paper focuses on various detection techniques and tools available, and analyzes if those are sufficient to list all possible covert channels. A case study of Xen is discussed with respect to a storage covert channel.

http://ieeexplore.ieee.org/iel7/6821296/6832296/06832320.pdf

Covert Channel Detection Techniques in Cloud

This is an epoch of Big data, Cloud computing, Cloud Database Management techniques. Traditional database approaches are not suitable for such colossal amount of data. To overcome the limitations of RDBMS, Map Reduce codes can be considered as a probable solution for such huge amount of data processing. Map Reduce codes provide both scalability and reliability. Users till date can work snugly with traditional Database approaches such as SQL, MYSQL, ORACLE, DB2, etc., and they are not aware of Map Reduce codes. In this paper, we are proposing a model which can convert any RDBMS queries to Map Reduce codes. We also gear optimization technique which can improve the performance of such amalgam approach.

Mansaf Alam

Papers

Migration from Relational Database into Object Oriented Database

Bigdata analysis and comparison of bigdata analytic approches

On Designing a Generic Framework for Big Data-as-a-Service

Covert Channel Detection Techniques in Cloud

Generalized Query Processing Mechanism in Cloud Database Management System