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

Data Mining - Concepts and Techniques.

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

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

In the Internet age, malware (such as viruses, trojans, ransomware, and bots) has posed serious and evolving security threats to Internet users. To protect legitimate users from these threats, anti-malware software products from different companies, including Comodo, Kaspersky, Kingsoft, and Symantec, provide the major defense against malware. Unfortunately, driven by the economic benefits, the number of new malware samples has explosively increased: anti-malware vendors are now confronted with millions of potential malware samples per year. In order to keep on combating the increase in malware samples, there is an urgent need to develop intelligent methods for effective and efficient malware detection from the real and large daily sample collection. In this article, we first provide a brief overview on malware as well as the anti-malware industry, and present the industrial needs on malware detection. We then survey intelligent malware detection methods. In these methods, the process of detection is usually divided into two stages: feature extraction and classification/clustering. The performance of such intelligent malware detection approaches critically depend on the extracted features and the methods for classification/clustering. We provide a comprehensive investigation on both the feature extraction and the classification/clustering techniques. We also discuss the additional issues and the challenges of malware detection using data mining techniques and finally forecast the trends of malware development.

https://dl.acm.org/doi/pdf/10.1145/3073559

A Survey on Malware Detection Using Data Mining Techniques

Disclosed is a computer-implemented method, system and computer program(s) for migration of a source machine instance to a target machine instance of least one heterogeneous virtual server device managed within a cloud computing environment. The method includes discovering machine images of a cloud service provider and storing results in a computer-readable catalog containing cloud metadata comprised of machine image identifiers and information discovered about the machine images. The method further includes, in response to a request for migration document in a computer-readable form that comprises a specification of a required migration target machine instance, specifying weight/priority information for components to be included in the target machine instance, where the weight information indicates weights for operations comprising component installation, component removal and component upgrade in the target machine instance. The method also includes executing a best fit matching algorithm to examine the catalog in accordance with the weight/priority information to identify optimal machine images to be used for the migration of the source machine instance to the target machine instance.

Method, System and Computer Programs to Assist Migration to a Cloud Computing Environment

The steady drumbeat of Cloud as a disruptive influence for Infrastructure Service Providers (ISP’s) and the enablement vehicle for Software As A Service (SAAS)providers can be heard loud and clear in the industry today. In fact, Cloud is probably at the peak of the hype curve, and already there are identified challenges associated with effective deployment for business critical applications (so called Production Applications) in mature enterprises. One of these challenges is the smooth migration of workload from the previous environment to the new cloud enabled environment in a cost effective way, with minimal disruption and risk. In this paper we introduce extensions to an integrated automation capability called the Darwin framework that enables workload migration for this scenario and discuss the impact that automated migration has on the cost and risks normally associated with migration to clouds.

Workload Migration into Clouds Challenges, Experiences, Opportunities

A software provisioning model which effectively combines characteristics of both push and pull models. In response to a request, a server sends a workflow or recipe of actions along with code server parameters and a requesting client computer system executes the,workflow and pulls necessary software updates and services to the client.

Method, apparatus and program product for software provisioning

Online interactive recommender systems strive to promptly suggest to consumers appropriate items (e.g., movies, news articles) according to the current context including both the consumer and item content information. However, such context information is often unavailable in practice for the recommendation, where only the users' interaction data on items can be utilized. Moreover, the lack of interaction records, especially for new users and items, worsens the performance of recommendation further. To address these issues, collaborative filtering (CF), one of the recommendation techniques relying on the interaction data only, as well as the online multi-armed bandit mechanisms, capable of achieving the balance between exploitation and exploration, are adopted in the online interactive recommendation settings, by assuming independent items (i.e., arms). Nonetheless, the assumption rarely holds in reality, since the real-world items tend to be correlated with each other (e.g., two articles with similar topics). In this paper, we study online interactive collaborative filtering problems by considering the dependencies among items. We explicitly formulate the item dependencies as the clusters on arms, where the arms within a single cluster share the similar latent topics. In light of the topic modeling techniques, we come up with a generative model to generate the items from their underlying topics. Furthermore, an efficient online algorithm based on particle learning is developed for inferring both latent parameters and states of our model. Additionally, our inferred model can be naturally integrated with existing multi-armed selection strategies in the online interactive collaborating setting. Empirical studies on two real-world applications, online recommendations of movies and news, demonstrate both the effectiveness and efficiency of the proposed approach.

Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms

People, processes, technology and information are the essential building blocks for creating a successful IT infrastructure in today's fast-paced, service-focused marketplace. ITIL which is recognized as the de facto standard for service management is a process based approach. ITIL focuses on a set of integrated processes which run the gamut from highly interactive and dynamic processes such as problem determination to highly repeatable processes such as patch deployment which are best handled in a fully automated, non-interactive fashion. The ability to support and integrate the full spectrum of interactivity for these processes with the appropriate level of automation is crucial for the service provider. Also key is the ability to identify opportunities to increase the level of automation as maturity and technology permit. In this paper, we propose a conceptual methodology for IT service management process automation that leverages the ontological relationships between process artifacts and resource artifacts to develop data aware processes for an effective automated approach to integrate both highly automated and human centric process models. The objective is to develop a systematic approach that addresses the needs of an IT organization in order that highly automated operational processes work in conjunction with collaborative human decision centric processes in order to effectively deliver IT services. In addition, we propose a complexity model to assist in identifying automation opportunities to satisfy the need for continuous efficiency and cost improvement.

https://www2.computer.org/portal/web/csdl/doi/10.1109/SCC.2007.75

Larisa Shwartz

Papers

Method, System and Computer Programs to Assist Migration to a Cloud Computing Environment

Workload Migration into Clouds Challenges, Experiences, Opportunities

Method, apparatus and program product for software provisioning

Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms

IT service management automation - A hybrid methodology to integrate and orchestrate collaborative human centric and automation centric workflows