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

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

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

The network capacity is fastened by utilizing unmanned aerial vehicles (UAVs) and mobile users can get feasible services independent of the infrastructure coverage. Furthermore, with the help of network virtualization technology, mobile network operators (MNOs) can lease their cellular network infrastructures and wireless network resources to the service providers (SPs) who are providing specific services to their mobile users. Wireless resource leasing among SPs, on the other hand, is problematic because each aims to maximize its own profit whilst assuring the QoS requirement of their users. Thus, in this paper, we propose a wireless resource sharing problem in the UAVs-assisted virtualized wireless networks with the goal of maximizing the total profit of SPs whilst guaranteeing the QoS requirement of each mobile user and satisfying the resource constraint of the UAVs. Then, we deploy the Lagrangian relaxation-based solution approach in order to address our proposed problem. Finally, we provide detailed numerical results to show the effectiveness of our proposed algorithm.

An Efficient Resource Sharing Model for Multi-UAV-Assisted Wireless Networks

대화 답변 생성 모델은 시퀀스-투-시퀀스 모델을 통해 큰 발전을 이루어왔음에도 불구하고 대화 답변 생성 모델은 일반적인 답변이나 주어진 질의와 관련 없는 답변을 하는 문제가 빈번하게 발생하고 있다. 이를 해결하기 위해 전통적인 로스 함수를 수정하려는 노력과 대화 생성 모델의 배경 지식 부족 문제를 해결하려는 노력이 있었지만, 이들은 일반적인 답변과 관련 없는 답변 생성을 모두 해결하지 못했다. 본 논문에서는 대화 답변 생성 모델이 질의와 상술한 문제의 원인으로 질의의 핵심을 파악하지 못하는데 들어 질의 토큰 중요도 계산기 사용을 제안한다. 또한 질의자는 청자의 특정한 답변을 유도하며 발화를 설계한다는 이론에 근거하여 질의의 내용 파악을 위해 정답 답변을 이용하는 것을 제안한다. 정성적 평가를 통해 제안하는 모델을 활용한 답변 생성기가 사용하지 않은 모델에 비해 질의와 연관된 답변을 생성할 수 있었음을 확인하였다. 

Response-Considered Query Token Importance Weight Calculator with Potential Response for Generating Query-Relevant Responses

http://networking.khu.ac.kr/layouts/net/publications/data/MCM4163.pdf

Distributed throughput-optimal scheduling framework with delay analysis in multi-hop wireless networks

Rapid technological advances are being made toward developing self-organizing wireless sensor network The essential purpose of such system is to sense the environment and inform users Security and privacy protection are of extreme importance for many of the proposed applications of wireless sensor networks (WSNs) However, security in sensor networks is complicated by the lack of tamper-resistant hardware (to keep per-node costs low) In addition, sensor nodes have limited storage and computational resource This paper proposes a lightweight identity authentication mechanism at the link layer for data frame in WSNs With the proposed mechanism there are only n-bits for authentication, which can greatly reduce overhead Statistical method and simulation results indicate that our mechanism is successful in handling MAC layer attack

A Lightweight Identity Authentication Mechanism for Self-Organizing Wireless Sensor Networks

Information Centric Networking (ICN) is envisioned to be the future Internet architecture and mobile access network like Long Term Evolution (LTE) i.e., 4G and 5G will be the major access networks. In this paper, we present a cache management and request forwarding schemes for Scalable Video Streaming (SVS) in Information Centric Networking (ICN) enabled mobile access networks. SVS video is consisted a mandatory baselayer and multiple optional enhancement layers. Baselayer, which is enough to decode the video with the lowest quality, is needed by every user that want to watch the video while enhancement layers are used to improve the video quality. Only a subset of users download enhance layers of the video. Therefore, caching the baselayer nearer to the users will increase their Quality of Experience.

Choong Seon Hong

Papers

An Efficient Resource Sharing Model for Multi-UAV-Assisted Wireless Networks

Response-Considered Query Token Importance Weight Calculator with Potential Response for Generating Query-Relevant Responses

Distributed throughput-optimal scheduling framework with delay analysis in multi-hop wireless networks

A Lightweight Identity Authentication Mechanism for Self-Organizing Wireless Sensor Networks

Cache Management and Interest Forwarding for Scalable Video Streaming in ICN Enabled Mobile Access Networks