다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

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

This paper shows a generic and simple conversion from weak asymmetric and symmetric encryption schemes into an asymmetric encryption scheme which is secure in a very strong sense- indistinguishability against adaptive chosen-ciphertext attacks in the random oracle model. In particular, this conversion can be applied efficiently to an asymmetric encryption scheme that provides a large enough coin space and, for every message, many enough variants of the encryption, like the ElGamal encryption scheme.

Secure integration of asymmetric and symmetric encryption schemes

Universal scaling for the dilemma strength in evolutionary games.

Sending data to the cloud for analysis was a prominent trend during the past decades, driving cloud computing as a dominant computing paradigm. However, the dramatically increasing number of devices and data traffic in the Internet-of-Things (IoT) era are posing significant burdens on the capacity-limited Internet and uncontrollable service delay. It becomes difficult to meet the delay-sensitive and context-aware service requirements of IoT applications by using cloud computing alone. Facing these challenges, computing paradigms are shifting from the centralized cloud computing to distributed edge computing. Several new computing paradigms, including Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet, have emerged to leverage the distributed resources at network edge to provide timely and context-aware services. By integrating end devices, edge servers, and cloud, they form a hierarchical IoT architecture, i.e., End-Edge-Cloud orchestrated architecture to improve the performance of IoT systems. This article presents a comprehensive survey of these emerging computing paradigms from the perspective of end-edge-cloud orchestration. Specifically, we first introduce and compare the architectures and characteristics of different computing paradigms. Then, a comprehensive survey is presented to discuss state-of-the-art research in terms of computation offloading, caching, security, and privacy. Finally, some potential research directions are envisioned for fostering continuous research efforts.

A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms: Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet

As cloud-based services become more numerous and dynamic, resource provisioning becomes more and more challenging. A QoS constrained resource allocation problem is considered in this paper, in which service demanders intend to solve sophisticated parallel computing problem by requesting the usage of resources across a cloud-based network, and a cost of each computational service depends on the amount of computation. Game theory is used to solve the problem of resource allocation. A practical approximated solution with the following two steps is proposed. First, each participant solves its optimal problem independently, without consideration of the multiplexing of resource assignments. A Binary Integer Programming method is proposed to solve the independent optimization. Second, an evolutionary mechanism is designed, which changes multiplexed strategies of the initial optimal solutions of different participants with minimizing their efficiency losses. The algorithms in the evolutionary mechanism take both optimization and fairness into account. It is demonstrated that Nash equilibrium always exists if the resource allocation game has feasible solutions.

A game-theoretic method of fair resource allocation for cloud computing services

Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter

CCA-secure ABE with outsourced decryption for fog computing

Task scheduling problems are of paramount importance which relate to the efficiency of the whole cloud computing facilities. In Hadoop, the open-source implementation of MapReduce, scheduling policies, such as FIFO or delay scheduling in FAIR scheduler is used by the master node to distribute waiting tasks to computing nodes (slaves) in response to the status messages of these nodes it receives. Although delay scheduling policy has claimed to improve the throughput and response times by a factor of 2 compared to FIFO policy, it can still achieve more improvement by considering a holistic view of all the tasks waiting to be processed. Therefore, this paper proposes a new scheduler which makes a scheduling decision by evaluating the entire group of tasks in the job queue. A genetic algorithm is designed as the optimization method for the new scheduler. The preliminary simulation results show that our scheduler can get a shorter make span for jobs than FIFO and delay scheduling policies and achieve a better balanced load across all the nodes in the cloud.

GA-Based Task Scheduler for the Cloud Computing Systems

Cloud computing is now a popular computing paradigm that can provide end users access to configurable resources on any device, from anywhere, at any time. During the past years, cloud computing has been developed dramatically. However, with the development of the Internet of Things, the disadvantages (such as high latency) of cloud computing are gradually revealed due to the long distance between the cloud and end users. Fog computing is proposed to solve this problem by extending the cloud to the edge of the network. In particular, fog computing introduces an intermediate layer called fog that is designed to process the communication data between the cloud and end users. Hence, fog computing is usually considered as an extension of cloud computing. In this article, we discuss the design issues for data security and privacy in fog computing. Specially, we present the unique data security and privacy design challenges presented by the fog layer and highlight the reasons why the data protection techniques in cloud computing cannot be directly applied in fog computing.

Guiyi Wei

Papers

A game-theoretic method of fair resource allocation for cloud computing services

Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter

CCA-secure ABE with outsourced decryption for fog computing

GA-Based Task Scheduler for the Cloud Computing Systems

Data Security and Privacy in Fog Computing