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

Data Mining - Concepts and Techniques.

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

[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

The presence of smartphones and their daily usages have changed several aspects of modern life. Android and IOS devices are widely used these days by the public. Besides, enormous number of mobile applications have been developed for the users. Google launched an online market which is known as Google Play for offering applications to end users as well as managing them in an integrated environment. Applications have many features that developers should clarify while they are uploading apps. These features have potential correlations which studying them could be useful in several tasks such as detecting malicious or miscategorized apps. Motivated by this, the purpose of this paper is to study these correlations through Machine Learning (ML) techniques. We apply various ML classification algorithms to distinguish these relations among key features of applications. Additionally, we perform many examinations to observe the relations between the size of the feature vector and the accuracy of the mentioned algorithms. Furthermore, we compare the algorithms to find the best choices for each part of our experiments. The results of our evaluation are promising. Also, in the majority of cases there are strong correlations between features.

Correlation Analysis of Applications’ Features: A Case Study on Google Play

This paper presents a reliable decentralized mutual exclusion algorithm for distributed systems in which processes communicate by asynchronous message passing. When any failure happens in system, the algorithm protects the distributed system against any crash. It also makes possible the recovery of lost data in system. It requires between (N-1) and 2(N-1) messages per critical section access, where N is the number of processes in the system. The exact message complexity can be expressed as a order function of clients in computation. The algorithm does not introduce any other overhead over Lamport's and Ricart-Agrawala's algorithms, which require 3(N-1) and 2(N-1) messages per critical section access, respectively.

/pdf/a-reliable-optimization-on-distributed-mutual-exclusion-341qv13piq.pdf

A reliable optimization on distributed mutual exclusion algorithm

Wireless Mobile Ad Hoc Networks: Channel Assignment

Learning Automaton (LA) is an adaptive self-organized model that improves its action-selection through interaction with an unknown environment. LA with finite action set can be classified into two main categories: fixed and variable structure. Furthermore, variable action-set learning automaton (VASLA) is one of the main subsets of variable structure learning automaton. In this paper, we propose VDHLA, a novel hybrid learning automaton model, which is a combination of fixed structure and variable action set learning automaton. In the proposed model, variable action set learning automaton can increase, decrease, or leave unchanged the depth of fixed structure learning automaton during the action switching phase. In addition, the depth of the proposed model can change in a symmetric (SVDHLA) or asymmetric (AVDHLA) manner. To the best of our knowledge, it is the first hybrid model that intelligently changes the depth of fixed structure learning automaton. Several computer simulations are conducted to study the performance of the proposed model with respect to the total number of rewards and action switching in stationary and non-stationary environments. The proposed model is compared with FSLA and VSLA. In order to determine the performance of the proposed model in a practical application, the selfish mining attack which threatens the incentive-compatibility of a proof-of-work based blockchain environment is considered. The proposed model is applied to defend against the selfish mining attack in Bitcoin and compared with the tie-breaking mechanism, which is a well-known defense. Simulation results in all environments have shown the superiority of the proposed model.

VDHLA: Variable Depth Hybrid Learning Automaton and Its Application to Defense Against the Selfish Mining Attack in Bitcoin

Maximal clique finding is a fundamental problem in graph theory and has been broadly investigated. However, maximal clique finding is time-consuming due to its nature and always returns tremendous cliques with large overlap nodes. Hence, a solution uses the relaxed version of the clique called k-clique, which follows up the subset of vertices with size k such that each pair in this subset has an edge. The k-clique problem has several applications in different domains, such as motif detection, finding anomalies in large graphs, and community structure discovery. In this paper, an algorithm based on learning automata is proposed for finding k-clique called (KC-LA) to apply communities in complex social networks. In (KC-LA), a network of learning automata is considering to the underlying networks. Then, select the proper action from a set of allowable actions, the reward and penalty guide KC-LA to detect the k-clique. Also, we applied the k-clique in the concept of finding communities in complex social networks. The KC-LA algorithm is to design some breakthroughs on the real and synthetic graphs in terms of high efficiency and effectiveness.

Distributed Learning Automata-Based Algorithm for Finding K-Clique in Complex Social Networks

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