『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

Cloud computing has gained interest amongst commercial organizations, research communities, developers and other individuals during the past few years. In order to move ahead with research in field of data management and to enable processing of such data, we need benchmark datasets and freely available data which are publicly accessible. Google in May 2011 released a trace of a cluster of 11k machines referred as "Google Cluster Trace". This trace contains cell information of about 29 days. This paper provides analysis of resource usage and requirements in this trace and is an attempt to give an insight about such kind of production trace similar to the ones in cloud environment. The major contributions of this paper include statistical profile of jobs based on resource usage, clustering of workload patterns and classification of jobs into different types based on k-means clustering. Though there have been earlier works for analysis of this trace, but our analysis provides several new findings such as jobs in a production trace are trimodal and there occurs symmetry in the tasks within a long job type.

Analysis and Clustering of Workload in Google Cluster Trace Based on Resource Usage

Smart card based user server mutual authentication framework is famous for safe communication via unfavorable and insecure communication system. The authenticated user and server communicate to each other and share information via Internet. Recently, Wang et al. suggested a lightweight password-assisted two factor authentication framework using smart card. We reviewed their scheme and observed that it does maintain security and privacy off-line password guessing attack and also impersonation attack. We proposed enhance elliptic curve cryptography(ECC) based authentication framework for the same environment. The proposed scheme ESEAP is secure resilience of many attractive security attributes and features like off-line password guessing attack, no password verifier-table, smart card loss attack, anonymity, mutual authentication, replay attack, impersonation attack, server spooling attack, no clock-synchronization attack, forward secrecy, insider attack, message authentication, provision of key agreement, parallel attack, sound repairability, no password exposure, timely typo detection, resistance to know attacks, password friendly, user unlinkability and server unlinkability. Further, the paper shows formal security analysis of the ESEAP which based on random oracle model. We compared the presented protocol with other related protocols in the same environment, and show that ESEAP is more efficient in terms of computation and communication cost. As a result, the presented protocol can be utilized over public communication channel.

ESEAP: ECC based secure and efficient mutual authentication protocol using smart card

Recurrent neural network based hybrid model for reconstructing gene regulatory network

Systems biology is an emerging interdisciplinary area of research that focuses on study of complex interactions in a biological system, such as gene regulatory networks. The discovery of gene regulatory networks leads to a wide range of applications, such as pathways related to a disease that can unveil in what way the disease acts and provide novel tentative drug targets. In addition, the development of biological models from discovered networks or pathways can help to predict the responses to disease and can be much useful for the novel drug development and treatments. The inference of regulatory networks from biological data is still in its infancy stage. This paper proposes a recurrent neural network (RNN) based gene regulatory network (GRN) model hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between the biological closeness and mathematical flexibility to model GRN. The RNN is able to capture complex, non-linear and dynamic relationship among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation even in noisy data. Hence, non-linear version of Kalman filter, i.e., generalized extended Kalman filter has been applied for weight update during network training. The developed model has been applied on DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We compared our results with other state-of-the-art techniques that show superiority of our model. Further, 5% Gaussian noise has been added in the dataset and result of the proposed model shows negligible effect of noise on the results.

Recurrent Neural Network Based Hybrid Model of Gene Regulatory Network.

Knowledge about product lifetime obtained from actual usage (field data) is of great importance to manufacturers who want to get information about the true reliability of their products. Warranty data are frequently used to estimate reliability characteristics because such databases are automatically generated and updated at no additional cost from repair claims during warranty coverage. For engineering purposes, usage time (e.g., mileage) is more relevant, and lifetime parameters measured in usage time is an integral part of reliability analysis using warranty data. Usually, warranty data consist of only failure information. Censored data (e.g., mileage of non-failure automobiles) are not obtainable, of which usage time distributions are different from those of failed ones. Effective usage-based estimation thus requires supplementary information about usage accumulation for non-failure units such as follow-up studies, or a usage time distribution which includes both failure, and non-failure products. However, sometimes usage-based data for non-failed units are expensive and difficult to obtain. Thus, the unavailability of the usage time of censored units makes it difficult to estimate the usage-based lifetime distribution of products. This paper deals with that problem, and discusses how to estimate the lifetime distribution using warranty data which consist of only failure information. The practical consequence of this finding is that supplemental information is not needed to obtain correct estimates of the lifetime parameters.

Mansaf Alam

Papers

Analysis and Clustering of Workload in Google Cluster Trace Based on Resource Usage

ESEAP: ECC based secure and efficient mutual authentication protocol using smart card

Recurrent neural network based hybrid model for reconstructing gene regulatory network

Recurrent Neural Network Based Hybrid Model of Gene Regulatory Network.

Lifetime Estimation Using Only Failure Information From Warranty Database