There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

An energy-aware service composition algorithm for multiple cloud-based IoT applications

Path planning in construction sites: performance evaluation of the Dijkstra, A∗, and GA search algorithms

The ever-increasing density in cloud computing parties, i.e. users, services, providers and data centres, has led to a significant exponential growth in: data produced and transferred among the cloud computing parties; network traffic; and the energy consumed by the cloud computing massive infrastructure, which is required to respond quickly and effectively to users requests. Transferring big data volume among the aforementioned parties requires a high bandwidth connection, which consumes larger amounts of energy than just processing and storing big data on cloud data centres, and hence producing high carbon dioxide emissions. This power consumption is highly significant when transferring big data into a data centre located relatively far from the users geographical location. Thus, it became high-necessity to locate the lowest energy consumption route between the user and the designated data centre, while making sure the users requirements, e.g. response time, are met. The main contribution of this paper is GreeDi, a network-based routing algorithm to find the most energy efficient path to the cloud data centre for processing and storing big data. The algorithm is, first, formalised by the situation calculus. The linear, goal and dynamic programming approaches are used to model the algorithm. The algorithm is then evaluated against the baseline shortest path algorithm with minimum number of nodes traversed, using a real Italian ISP physical network topology.

/pdf/greedi-an-energy-efficient-routing-algorithm-for-big-data-on-4j2yeqvw69.pdf

GreeDi: An energy efficient routing algorithm for big data on cloud

The fast propagation of the Internet of Things (IoT) devices has driven to the development of collaborative healthcare frameworks to support the next generation healthcare industry for quality medical healthcare. This paper presents a generalized collaborative framework named collaborative shared healthcare plan (CSHCP) for cognitive health and fitness assessment of people using ambient intelligent application and machine learning techniques. CSHCP provides support for daily physical activity recognition, monitoring, assessment and generate a shared healthcare plan based on collaboration among different stakeholders: doctors, patient guardians, as well as close community circles. The proposed framework shows promising outcomes compared to the existing studies. Furthermore, the proposed framework enhances team communication, coordination, long-term plan management of healthcare information to provide a more efficient and reliable shared healthcare plans to people.

/pdf/a-collaborative-healthcare-framework-for-shared-healthcare-36fnd8nwes.pdf

A collaborative healthcare framework for shared healthcare plan with ambient intelligence

In spite of the advancement and variety of available mobile based applications, there is an eminent need to investigate the current position of the acceptance of those mobile health applications that are tailored towards the patients' medical information management and access. This paper explores and analysis the technology acceptance of the use of smart mobile phones from users' perceptions, viewpoint, and experience. The paper will focus on studies of the factors affecting the intention to use the smart mobile phone in the medical domain. Our proposed Mobile Technology Acceptance Model (Mo-HTAM) assessed the significance of the correlation between technology design, perceived ease of use, perceived usefulness as independent factors and the intention to use m-Health services as the dependent factor. The correlation coefficient analysis indicated that there is a significant relationship between the major constructs. Rigorous analytical testing confirmed that there is a direct relationship between social, cultural and technological constructs with intention to use m-Health applications. The model indicated that a well-informed technology design will increase the intention to use m-Health application, and perceived usefulness is more significant to the intention to use m-health applications than the perceived ease of use. These findings indicated that intention to use mobile health informatics is formed by the perceptions of the user of m-Health technology design approach, especially in relation to the navigation and the data presentation on the mobile device.

Hissam Tawfik

Papers

An energy-aware service composition algorithm for multiple cloud-based IoT applications

Path planning in construction sites: performance evaluation of the Dijkstra, A∗, and GA search algorithms

GreeDi: An energy efficient routing algorithm for big data on cloud

A collaborative healthcare framework for shared healthcare plan with ambient intelligence

MoHTAM: A Technology Acceptance Model for Mobile Health Applications