構造化データに対する予測手法：グラフ，順序，時系列

京都大学新制・課程博士博士(情報学)甲第23439号情博第769号新制||情||131(附属図書館)京都大学大学院情報学研究科知能情報学専攻(主査)教授 鹿島 久嗣, 教授 山本 章博, 教授 阿久津 達也学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA

Training the Federal Acquisition Workforce: A Correlational Study of Perceived Learner-Centric Interaction Effectiveness and Distance Learning Environmental Preferences

Public procurement, the purchase of goods and services by governments from external sources, is a strategic tool commonly used by governments to fulfill their mandates. To manage public procurement inside the United States federal government, the Acquisition Workforce (AWF) was established. Training to support the development and maintenance of necessary competencies in the AWF occurs in both face-to-face and distance learning environments. The trend, in general, has been towards a greater dependency on the use of distance learning. The purpose of this quantitative study was to examine AWF perceptions of learner-centric interaction effectiveness and learning environment preferences, along with the correlation, if any, between these factors inside the federal acquisition distance learning environment. The study’s findings indicate a preference by the AWF for training consisting of learner-centric interaction diversity delivered in a bichronous distance learning environment

Exploring Strategies that IT Leaders Use to Adopt Cloud Computing

Information Technology (IT) leaders must leverage cloud computing to maintain competitive advantage. Evidence suggests that IT leaders who have leveraged cloud computing in small and medium sized organizations have saved an average of $1 million in IT services for their organizations. The purpose of this qualitative single case study was to explore strategies that IT leaders use to adopt cloud computing for their organizations. The target population consisted of 15 IT leaders who had experience with designing and deploying cloud computing solutions at their organization in Long Island, New York within the past 2 years. The conceptual framework of this research project was the disruptive innovation theory. Semistructured interviews were conducted and company documents were gathered. Data were inductively analyzed for emergent themes, then subjected to member checking to ensure the trustworthiness of findings. Four main themes emerged from the data: the essential elements for strategies to adopt cloud computing; most effective strategies; leadership essentials; and barriers, critical factors, and ineffective strategies affecting adoption of cloud computing. These findings may contribute to social change by providing insights to IT leaders in small and medium sized organizations to save money while gaining competitive advantage and ensure sustainable business growth that could enhance community standards of living

An Operational Utility Assessment: Measuring the Effectiveness of the Joint Concept Technology Demonstration (JCTD), Joint Forces Protection Advance Security System (JFPASS)

Sponsored Report (for Acquisition Research Program)Planning modern military operations requires an accurate intelligence assessment of potential threats, combined with a detailed assessment of the physical theater of operations. This information can then be combined with equipment and manpower resources to set up a logistically supportable operation that mitigates as much of the enemy threat as possible. Given such a daunting challenge, military planners often turn to intelligent software agents to support their efforts. The success of the mission often hinges on the accuracy of these plans and the integrity of the security umbrella provided. The purpose of this project is to provide a comprehensive assessment of the Joint Forces Protection Advanced Security System (JFPASS) Joint Concept Technology Demonstration (JCTD) to better meet force-protection needs. It will also address the adaptability of this technology to an ever-changing enemy threat by the use of intelligent software. This project will collect and analyze data pertaining to the research, development, testing, and effectiveness of the JFPASS and develop an operational effectiveness model to quantify overall system performance.Naval Postgraduate School Acquisition Research ProgramApproved for public release; distribution is unlimited

A Hybrid Optimization Algorithm for Efficient Virtual Machine Migration and Task Scheduling Using a Cloud-Based Adaptive Multi-Agent Deep Deterministic Policy Gradient Technique

This To achieve optimal system performance in the quickly developing field of cloud computing, efficient resource management—which includes accurate job scheduling and optimized Virtual Machine (VM) migration—is essential. The Adaptive Multi-Agent System with Deep Deterministic Policy Gradient (AMS-DDPG) Algorithm is used in this study to propose a cutting-edge hybrid optimization algorithm for effective virtual machine migration and task scheduling. An sophisticated combination of the War Strategy Optimization (WSO) and Rat Swarm Optimizer (RSO) algorithms, the Iterative Concept of War and Rat Swarm (ICWRS) algorithm is the foundation of this technique. Notably, ICWRS optimizes the system with an amazing 93% accuracy, especially for load balancing, job scheduling, and virtual machine migration. The VM migration and task scheduling flexibility and efficiency are greatly improved by the AMS-DDPG technology, which uses a powerful combination of deterministic policy gradient and deep reinforcement learning. By assuring the best possible resource allocation, the Adaptive Multi-Agent System method enhances decision-making even more. Performance in cloud-based virtualized systems is significantly enhanced by our hybrid method, which combines deep learning and multi-agent coordination. Extensive tests that include a detailed comparison with conventional techniques verify the effectiveness of the suggested strategy. As a consequence, our hybrid optimization approach is successful. The findings show significant improvements in system efficiency, shorter job completion times, and optimum resource utilization. Cloud-based systems have unrealized potential for synergistic optimization, as shown by the integration of ICWRS inside the AMS-DDPG framework. Enabling a high-performing and sustainable cloud computing infrastructure that can adapt to the changing needs of modern computing paradigms is made possible by this strategic resource allocation, which is attained via careful computational utilization

Autonomous synthesis of thin film materials with pulsed laser deposition enabled by in situ spectroscopy and automation

Synthesis of thin films has traditionally relied upon slow, sequential processes carried out with substantial human intervention, frequently utilizing a mix of experience and serendipity to optimize material structure and properties. With recent advances in autonomous systems which combine synthesis, characterization, and decision making with artificial intelligence (AI), large parameter spaces can be explored autonomously at rates beyond what is possible by human experimentalists, greatly accelerating discovery, optimization, and understanding in materials synthesis which directly address the grand challenges in synthesis science. Here, we demonstrate autonomous synthesis of a contemporary 2D material by combining the highly versatile pulsed laser deposition (PLD) technique with automation and machine learning (ML). We incorporated in situ and real-time spectroscopy, a high-throughput methodology, and cloud connectivity to enable autonomous synthesis workflows with PLD. Ultrathin WSe2 films were grown using co-ablation of two targets and showed a 10x increase in throughput over traditional PLD workflows. Gaussian process regression and Bayesian optimization were used with in situ Raman spectroscopy to autonomously discover two distinct growth windows and the process-property relationship after sampling only 0.25% of a large 4D parameter space. Any material that can be grown with PLD could be autonomously synthesized with our platform and workflows, enabling accelerated discovery and optimization of a vast number of materials

Enhancing entrepreneurial innovation through industry-led accelerators: corporate-new venture dynamics and organizational redesign in a port maritime ecosystem

This PhD dissertation studies the management and design of corporate accelerators, in particular, industry-led value chain corporate accelerators. I addressed a multi-faceted research question about the novelty, corporate impact, dynamics and design of industry-led accelerators. Using a longitudinal, inductive, multiple-case embedded research design that analyses the industrial accelerator interface, the relationships between incumbent firms and external new ventures and the R&D/innovation units of established firms in a port maritime complex, this dissertation addresses this multi-faceted research question and it makes five core contributions. First, it positions, for the first time, the corporate accelerator phenomena at the intersection of fundamental management research streams, including organizational design, dynamic capabilities and corporate entrepreneurship. Second, it conducts the first study of the promising model of industry-led accelerator by inductively generating a four-step framework of how these accelerators work: i) co-define a broad innovation remit, ii) generate an innovation funnel to attract start-ups and scale-ups, iii) mutual sensing via flexible matching iv) select for scale and investment. Third, it finds striking counter-intuitive evidence in that the industry-led accelerator not only accelerates external new ventures but rather the corporate partners themselves by triggering them to internalize the lean start-up method and redesign their R&D/innovation processes and routines. To explain this, I inductively developed a four-phases process model of corporate entrepreneurial capability-building, comprising: a) attracting, b) strategic fit sensing, c) shaping and d) internalizing. Fourth, this dissertation uncovers three novel tensions—internalization, implementation and role—at the incumbent - new venture interface and develops a new ecological and symbiotically-inspired framework for tension identification and mitigation in industrial acceleration contexts. Fifth, and finally, using the frameworks and process models developed, this dissertation proposes a new toolkit (industrial acceleration design canvas and workshops) to orient practitioners when strategizing, designing and sustaining corporate new venture ecosystem acceleration initiatives.Open Acces

A Building Information Modeling (BIM)-centric Digital Ecosystem for Smart Airport Life Cycle Management

An increasing number of new airport infrastructure construction and improvement projects are being delivered in today\u27s modern world. However, value creation is a recurring issue due to inefficiencies in managing capital expenditures (CapEx) and operating expenses (OpEx), while trying to optimize project constraints of scope, time, cost, quality, and resources. In this new era of smart infrastructure, digitalization transforms the way projects are planned and delivered. Building Information Modeling (BIM) is a key digital process technique that has become an imperative for today\u27s Architecture, Engineering, Construction and Operations (AECO) sector. This research suggests a BIM-centric digital ecosystem by detailing technical and strategic aspects of Airport BIM implementation and digital technology integration from a life cycle perspective. This research provides a novel approach for consistent and continuous use of digital information between business and functional levels of an airport by developing a digital platform solution that will enable seamless flow of information across functions. Accordingly, this study targets to achieve three objectives: 1- To provide a scalable know-how of BIM-enabled digital transformation; 2- To guide airport owners and major stakeholders towards converging information siloes for airport life cycle data management by an Airport BIM Framework; 3- To develop a BIM-based digital platform architecture towards realization of an airport digital twin for airport infrastructure life cycle management. Airport infrastructures can be considered as a System of Systems (SoS). As such, Model Based Systems Engineering (MBSE) with Systems Modeling Language (SysML) is selected as the key methodology towards designing a digital ecosystem. Applying MBSE principles leads to forming an integrating framework for managing the digital ecosystem. Furthermore, this research adopts convergent parallel mixed methods to collect and analyze multiple forms of data. Data collection tools include extensive literature and industry review; an online questionnaire; semi-structured interviews with airport owner parties; focus group discussions; first-hand observations; and document reviews. Data analysis stage includes multiple explanatory case study analyses, thematic analysis, project mapping, percent coverage analysis for coded themes to achieve Objective 1; thematic analysis, cluster analysis, framework analysis, and non-parametric statistical analysis for Objective 2; and qualitative content analysis, non-parametric statistical analysis to accomplish Objective 3. This research presents a novel roadmap toward facilitation of smart airports with alignment and integration of disruptive technologies with business and operational aspects of airports. Multiple comprehensive case study analyses on international large-hub airports and triangulation of organization-level and project-level results systematically generate scalable technical and strategic guidelines for BIM implementation. The proposed platform architecture will incentivize major stakeholders for value-creation, data sharing, and control throughout a project life cycle. Introducing scalability and minimizing complexity for end-users through a digital platform approach will lead to a more connected environment. Consequently, a digital ecosystem enables sophisticated interaction between people, places, and assets. Model-driven approach provides an effective strategy for enhanced decision-making that helps optimization of project resources and allows fast adaptation to emerging business and operational demands. Accordingly, airport sustainability measures -economic vitality, operational efficiency, natural resources, and social responsibility- will improve due to higher levels of efficiency in CapEx and OpEx. Changes in business models for large capital investments and introducing sustainability to supply chains are among the anticipated broader impacts of this study

DoR Communicator - June 2014

The June 2014 issue of the Division of Research newsletter.https://digitalcommons.fiu.edu/research_newsletter/1009/thumbnail.jp