Characterizing Graduateness in Computing Education

In my research, I employ a highly qualitative, narrative methodology to explore the sense students make of their own educational experiences within their wider learning trajectories. By taking such a holistic perspective on a Computing Education, I hope to be able to identify and distil aspects of successful Computing programs, whose effects may only emerge over time

A Cloud Computing Capability Model for Large-Scale Semantic Annotation

Semantic technologies are designed to facilitate context-awareness for web content, enabling machines to understand and process them. However, this has been faced with several challenges, such as disparate nature of existing solutions and lack of scalability in proportion to web scale. With a holistic perspective to web content semantic annotation, this paper focuses on leveraging cloud computing for these challenges. To achieve this, a set of requirements towards holistic semantic annotation on the web is defined and mapped with cloud computing mechanisms to facilitate them. Technical specification for the requirements is critically reviewed and examined against each of the cloud computing mechanisms, in relation to their technical functionalities. Hence, a mapping is established if the cloud computing mechanism’s functionalities proffer a solution for implementation of a requirement’s technical specification. The result is a cloud computing capability model for holistic semantic annotation which presents an approach towards delivering large-scale semantic annotation on the web via a cloud platform

A Cloud Computing Capability Model for Large-Scale Semantic Annotation

Semantic technologies are designed to facilitate context-awareness for web content, enabling machines to understand and process them. However, this has been faced with several challenges, such as disparate nature of existing solutions and lack of scalability in proportion to web scale. With a holistic perspective to web content semantic annotation, this paper focuses on leveraging cloud computing for these challenges. To achieve this, a set of requirements towards holistic semantic annotation on the web is defined and mapped with cloud computing mechanisms to facilitate them. Technical specification for the requirements is critically reviewed and examined against each of the cloud computing mechanisms, in relation to their technical functionalities. Hence, a mapping is established if the cloud computing mechanism's functionalities proffer a solution for implementation of a requirement's technical specification. The result is a cloud computing capability model for holistic semantic annotation which presents an approach towards delivering large scale semantic annotation on the web via a cloud platform

Everywhere Energy-Efficient E-Computing

This document outlines a vision for “green computing for a clean tomorrow” [Feng06]. The first piece of the vision is a bit pedestrian – holistic energy-efficient computing “in a box” – but serves as a foundation to a more audacious (tongue-in-cheek) vision of holistic energy-efficient computing “in a world.” As recently noted by IDC in an IBM presentation at the Gartner Data Center Summit, December 2006, the annual spending for power and cooling would match the annual budget for new server spending in 2007, as shown in the figure below. In addition to cost, energy-efficient (power- aware) computing can enhance the reliability and availability of ever-increasingly dense computing systems, such as blades; it can also provide additional computational headroom when an institution has reached the limits of its power and cooling infrastructure, particularly when the infrastructure cannot be expanded any further [Feng08]

Funding, repo and credit inclusive valuation as modified option pricing

We take the holistic approach of computing an OTC claim value that incorporates credit and funding liquidity risks and their interplays, instead of forcing individual price adjustments: CVA, DVA, FVA, KVA. The resulting nonlinear mathematical problem features semilinear PDEs and FBSDEs. We show that for the benchmark vulnerable claim there is an analytical solution, and we express it in terms of the Black-Scholes formula with dividends. This allows for a detailed valuation analysis, stress testing and risk analysis via sensitivities.Comment: 1 figur

Mining for Culture: Reaching Out of Range

The goal of this paper is to present a tool that will sustain the development of culturally relevant computing artifacts by providing an effective means of detecting culture identities and cultures of participation. Culturally relevant designs rely heavily on how culture impacts design and though the guidelines for producing culturally relevant objects provide a mechanism for incorporating culture in the design, there still requires an effective method for garnering and identifying said cultures that reflects a holistic view of the target audience. This tool presents culturally relevant designs as a process of communicating with key audiences and thus bridging people and technology in a way that once seemed out of range

Холістичний підхід до підготовки ІКТ-компетентних педагогічних кадрів

The article intends to explore and estimate the possible pedagogical advantages and potential of cloud computing technology application with aim to increase organizational level, availability and quality of ICT-based learning tools and re-sources. Holistic model of a specialist is proposed and the problems of development of a system of methodological and technological support for elaboration of cloud-based learning environment of educational institution are considered.Cтаття присвячена аналізу і оцінці можливих педагогічних переваг і потенціалу застосування технології хмарних обчислень з метою підвищення організаційного рівня, доступності і якості засобів та ресурсів ІКТ-орієнтованого навчання. Запропонована холістична модель фахівця та висвітлено проблеми розвитку системи методичного та технологічного підтримування процесів розгортання хмаро-орієнтованого навчального середовища освітньої установи

Leveraging Edge Computing through Collaborative Machine Learning

The Internet of Things (IoT) offers the ability to analyze and predict our surroundings through sensor networks at the network edge. To facilitate this predictive functionality, Edge Computing (EC) applications are developed by considering: power consumption, network lifetime and quality of context inference. Humongous contextual data from sensors provide data scientists better knowledge extraction, albeit coming at the expense of holistic data transfer that threatens the network feasibility and lifetime. To cope with this, collaborative machine learning is applied to EC devices to (i) extract the statistical relationships and (ii) construct regression (predictive) models to maximize communication efficiency. In this paper, we propose a learning methodology that improves the prediction accuracy by quantizing the input space and leveraging the local knowledge of the EC devices