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

Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

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

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

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Anomaly detection: A survey

Data Mining - Concepts and Techniques.

Metaquerying is a datamining technology by which hidden dependencies among several database relations can be discovered. This tool has already been successfully applied to several real-world applications. Recent papers provide only preliminary results about the complexity of metaquerying. In this paper we define several variants of metaquerying that encompass, as far as we know, all variants defined in the literature. We study both the combined complexity and the data complexity of these variants. We show that, under the combined complexity measure, metaquerying is generally intractable (unless P=NP), lying sometimes quite high in the complexity hierarchies (as high as NP^PP), depending on the characteristics of the plausibility index. However, we are able to single out some tractable and interesting metaquerying cases (whose combined complexity is LOGCFL-complete). As for the data complexity of metaquerying, we prove that, in general, this is in TC0, but lies within AC0 in some simpler cases. Finally, we discuss implementation of metaqueries, by providing algorithms to answer them.

Computational Properties of Metaquerying Problems

This paper proposes a platform for achieving accountability across distributed business processes involving heterogeneous entities that need to establish various types of agreements in a standard way. The devised solution integrates blockchain and digital identity technologies in order to exploit the guarantees about the authenticity of the involved entities’ identities, coming from authoritative providers (e.g. public), and the trustiness ensured by the decentralized consensus and reliability of blockchain transactions.

Achieving Service Accountability through Blockchain and Digital Identity

Metaquerying is a data mining technology by which hidden dependencies among several database relations can be discovered. This tool has already been successfully applied to several real-world applications, but only preliminary results about the complexity of metaquerying can be found in the literature. In this article, we define several variants of metaquerying that encompass, as far as we know, all the variants that have been defined in the literature. We study both the combined complexity and the data complexity of these variants. We show that under the combined complexity measure metaquerying is generally intractable (unless P = NP), lying sometimes quite high in the complexity hierarchies (as high as NPPP), depending on the characteristics of the plausibility index. Nevertheless, we are able to single out some tractable and interesting metaquerying cases, whose combined complexity is LOGCFL-complete. As for the data complexity of metaquerying, we prove that, in general, it is within TC0, but lies within AC0 in some simpler cases. Finally, we discuss the implementation of metaqueries by providing algorithms that answer them.

Computational properties of metaquerying problems

Nowadays, achieving strong accountability for two or more parties that interact each other within heterogeneous and distributed business processes using a trustable and decentralised approach has become a very important issue. In this paper a solution is proposed that consists of an IT infrastructure which uses blockchain and digital identity technologies as the building blocks. In particular, the notion of accountability node, a special case of blockchain 3.0 masternode, is identified as the key element of the devised infrastructure which acts as the main interface to cooperating services and relieves the users from the burden of having to directly interact with the blockchain.

An Infrastructure for Service Accountability based on Digital Identity and Blockchain 3.0

Designing algorithms capable of efficiently constructing minimal models of CNFs is an important task in AI. This paper provides new results along this research line and presents new algorithms for performing minimal model finding and checking over positive propositional CNFs and model minimization over propositional CNFs. An algorithmic schema, called the Generalized Elimination Algorithm (GEA) is presented, that computes a minimal model of any positive CNF. The schema generalizes the Elimination Algorithm (EA) [BP97], which computes a minimal model of positive head-cycle-free (HCF) CNF theories. While the EA always runs in polynomial time in the size of the input HCF CNF, the complexity of the GEA depends on the complexity of the specific eliminating operator invoked therein, which may in general turn out to be exponential. Therefore, a specific eliminating operator is defined by which the GEA computes, in polynomial time, a minimal model for a class of CNF that strictly includes head-elementary-set-free (HEF) CNF theories [GLL06], which form, in their turn, a strict superset of HCF theories. Furthermore, in order to deal with the high complexity associated with recognizing HEF theories, an "incomplete" variant of the GEA (called IGEA) is proposed: the resulting schema, once instantiated with an appropriate elimination operator, always constructs a model of the input CNF, which is guaranteed to be minimal if the input theory is HEF. In the light of the above results, the main contribution of this work is the enlargement of the tractability frontier for the minimal model finding and checking and the model minimization problems.

Fabrizio Angiulli

Papers

Computational Properties of Metaquerying Problems

Achieving Service Accountability through Blockchain and Digital Identity

Computational properties of metaquerying problems

An Infrastructure for Service Accountability based on Digital Identity and Blockchain 3.0

On the Tractability of Minimal Model Computation for Some CNF Theories