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

National Institute of Standards and Technology における超伝導研究及び生活

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 objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Usually, a proof of a theorem contains more knowledge than the mere fact that the theorem is true. For instance, to prove that a graph is Hamiltonian it suffices to exhibit a Hamiltonian tour in it; however, this seems to contain more knowledge than the single bit Hamiltonian/non-Hamiltonian.In this paper a computational complexity theory of the “knowledge” contained in a proof is developed. Zero-knowledge proofs are defined as those proofs that convey no additional knowledge other than the correctness of the proposition in question. Examples of zero-knowledge proof systems are given for the languages of quadratic residuosity and 'quadratic nonresiduosity. These are the first examples of zero-knowledge proofs for languages not known to be efficiently recognizable.

/pdf/the-knowledge-complexity-of-interactive-proof-systems-31apre0ecf.pdf

The knowledge complexity of interactive proof-systems

A flexible biometrics remote user authentication scheme

Mutations in the kinase domain of epidermal growth factor receptor (EGFR) are associated with clinical responsiveness to gefitinib in patients with non-small-cell lung cancers (NSCLC). Recently, we have identified many novel EGFR mutations in NSCLC tissues. In this study, we found that gefitinib could suppress the tyrosine phosphorylation of most EGFR mutants better than the wild-type receptor. However, gefitinib had quite variable growth-suppressive effects on different EGFR mutant-expressing cells. All tested EGFR mutants have high basal phosphorylation at multiple tyrosine residues. Upon EGF stimulation, the mutated EGFRs did not have apparently stronger phosphorylation at tyrosines 845, 992, 1068, and 1173 than the wild-type receptor. However, stronger phosphorylation at tyrosine 1045 was observed in the S768I, L861Q, E709G, and G719S mutants. The E746-A750 deletion mutant was less responsive to EGF than the wild-type and other mutant receptors. The S768I, L861Q, E709G, and G719S mutants were refractory to EGF-induced ubiquitination and had more sustained tyrosine phosphorylation. E709G and G719S also lacked EGF-induced receptor downregulation. Our results indicate that, in addition to sensitivity to gefitinib, EGFR mutations also caused various changes in EGFR's regulatory mechanisms, which may contribute to the constitutive activation of EGFR mutants and oncogenesis in NSCLC.

Distinctive activation patterns in constitutively active and gefitinib-sensitive EGFR mutants

Research note: Threshold signature schemes with traceable signers in group communications

Generalization of Threshold Signature and Authenticated Encryption for Group Communications

In 2000, Saeednia proposed a new scheme to make the Hill cipher secure. The author makes use of permutations of columns and rows of a matrix to get a different key for encrypting each message. This paper shows that the cipher key Ht can be obtained by parameter u. Besides, the Saeednia's scheme costs a lot of time in matrix computation. To overcome the drawbacks of Saeednia's scheme, a more secure cryptosystem with a one‐way hash function is proposed.

https://ir.nctu.edu.tw:443/bitstream/11536/26412/1/000223895700013.pdf

Chu-Hsing Lin

Papers

A flexible biometrics remote user authentication scheme

Distinctive activation patterns in constitutively active and gefitinib-sensitive EGFR mutants

Research note: Threshold signature schemes with traceable signers in group communications

Generalization of Threshold Signature and Authenticated Encryption for Group Communications

Comments on Saeednia's Improved Scheme for the Hill Cipher