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This chapter explains the direction of Digital IT such as Cloud/Mobile IT, Big Data, and Internet of Things. This chapter introduces and briefs EA frameworks such as TOGAF, FEAF, DoDAF, MODAF, Adaptive EA, and EA3 Cube framework and analyzes the selected EA frameworks from standpoints of Cloud/Mobile IT integration, as well. Furthermore, this chapter briefs Agile Enterprise Architecture, introduces the primary scaling agile frameworks, and compares them. In the latter of this chapter, we investigated problems in Digital Transformation and related enterprise architecture, and we show solutions to cope with these problems in Digital Transformation and enterprise architecture. In later chapters, we will show the strategic architecture framework aligned with Digital IT strategies on the basis of the above solutions.

The purpose of this chapter is to propose an “Adaptive Integrated Digital Architecture Framework—AIDAF” to meet the requirements of the digital transformation in relation to the above agility-related aspects. The proposed EA framework should support an IT strategy promoting cloud/mobile IT/Digital IT, based on what our prior research suggested. Also, we show the recommended EA artifacts in AIDAF, compared with Zachman Framework and DODAF, for use with the EA3 Cube Framework. Moreover, we propose an architecture assessment model, global communication/knowledge management model, and Risk Management model related to the above AIDAF in this chapter. In later chapters, we will show a case of EA framework building by the global company.

This chapter describes the overall evaluation of AIDAF for agility-related elements and shows that the AIDAF can have capabilities for the five agility-related attributes necessary for the execution of Digital IT strategies. Moreover, we evaluated and briefed strategy elements to mitigate risks in Digital Transformation. Also, this chapter briefs perspectives on AIDAF, such as benefits, challenges, and critical success factors. In the latter of this chapter, we refer to global Architecture Board activities for digital transformation and the main limitation of this study. In later chapters, we will describe the conclusion in terms of the AIDAF, Architecture reviews, knowledge management and Risk Management for digital transformation and refer to the future researches.

This chapter describes a case of Risk Management with Architecture Board in global company. We evaluated and analyzed the above case study, and we verified the STrategic Risk Mitigation Model for Digital Transformation. Also, this chapter briefs solution categories to mitigate risks in the field of Digital IT systems such as Cloud, mobile IT, and Big Data. In the latter of this chapter, we refer to strategy elements to mitigate risks in Digital Transformation. In later chapters, we will show the overall evaluation of AIDAF and perspectives on AIDAF, such as benefits, challenges and critical success factors, and global Architecture Board activities for digital transformation.

This chapter describes a case of Architecture Board reviews in global company. We evaluated and analyzed this case study, and we verified Assessment model in Architecture Board. Also, this chapter briefs solutions to mitigate risks in the field of Digital IT systems, CRM and ERP. In the latter of this chapter, a global communication case study is described, and we analyzed this global communication case study and verified the Global Digital Transformation Communication (GDTC) model and Solution Collaboration Model. In later chapters, we will show the case study for the Risk Management with Architecture Board and evaluate STrategic Risk Mitigation Model for Digital Transformation.

Accurate measurement of poses and expressions can increase the efficiency of recognition systems by avoiding the recognition of spurious faces. This paper presents a novel and robust pose-expression invariant face recognition method in order to improve the existing face recognition techniques. First, we apply the TSL color model for detecting facial region and estimate the vector X-Y-Z of face using connected components analysis. Second, the input face is mapped by a deformable 3D facial model. Third, the mapped face is transformed to the frontal face which appropriates for face recognition by the estimated pose vector and action unit of expression. Finally, the damaged regions which occur during the process of normalization are reconstructed using PCA. Several empirical tests are used to validate the application of face detection model and the method for estimating facial poses and expression. In addition, the tests suggest that recognition rate is greatly boosted through the normalization of the poses and expression.

The problem of selection among competing models has been a fundamental issue in statistical data analysis. Good fits to data can be misleading since they can result from properties of the model that have nothing to do with it being a close approximation to the source distribution of interest (for example, overfitting). In this study we focus on the preference among models from a family of polynomial regressors. Three decades of research has spawned a number of plausible techniques for the selection of models, namely, Akaike's Finite Prediction Error (FPE) and Information Criterion (AIC), Schwartz's criterion (SCH), Generalized Cross Validation (GCV), Wallace's Minimum Message Length (MML), Minimum Description Length (MDL), and Vapnik's Structural Risk Minimization (SRM). The fundamental similarity between all these principles is their attempt to define an appropriate balance between the complexity of models and their ability to explain the data. This paper presents an empirical study of the above principles in the context of model selection, where the models under consideration are univariate polynomials. The paper includes a detailed empirical evaluation of the model selection methods on six target functions, with varying sample sizes and added Gaussian noise. The results from the study appear to provide strong evidence in support of the MML- and SRM- based methods over the other standard approaches (FPE, AIC, SCH and GCV).