Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing incomplete big data lies in addressing the uncertainty problem caused by their incomplete characteristics. However, existing LFA methods do not fully consider such uncertainty. In this book, the author introduces several robust latent feature learning methods to address such uncertainty for effectively and efficiently analyzing incomplete big data, including robust latent feature learning based on smooth L1-norm, improving robustness of latent feature learning using L1-norm, improving robustness of latent feature learning using double-space, data-characteristic-aware latent feature learning, posterior-neighborhood-regularized latent feature learning, and generalized deep latent feature learning. Readers can obtain an overview of the challenges of analyzing incomplete big data and how to employ latent feature learning to build a robust model to analyze incomplete big data. In addition, this book provides several algorithms and real application cases, which can help students, researchers, and professionals easily build their models to analyze incomplete big data.

This book constitutes the thoroughly refereed proceedings of the 6th Joint International Semantic Technology Conference, JIST 2016, held in Singapore, Singapore, in November 2016. The main topics of JIST 2016 include among others ontology and reasoning; linked data; knowledge graph. The JIST 2016 conference consists of two keynotes, a main technical track, including (full and short papers) from the research and the in-use tracks, a Poster and Demo session, a workshop and two tutorials. The 16 full and 8 short papers presented were carefully reviewed and selected from 34 submissions. The papers cover the following topics: ontology and data management; linked data; information retrieval and knowledge discovery; RDF and query; knowledge graph; application of semantic technologies.

Hunting and running through the woods of west Tennessee is a drastically different scene than selling drugs and rubbing shoulders with drug dealers in the inner city. This is the story of Adam French, a story of humble and healthy beginnings, a story of harsh and horrific interruption and a story wrapped in the life changing love of Christ. His dad's journey of successful business owner to violent alcoholic coincides with his mother's struggle of successful teacher to pill addict. Somewhere in the middle of their stories is a scared little boy, curious pre-teen, broken teenager and a wounded man. For the past 17yrs Adam has been on a journey of healing. Adam draws from his counseling degree, bible college education, personal recovery and pastoral experience to create an on ramp in his book, MANdentity, for men seeking help in their marriages, addictions, traumas, broken relationships and everyday struggles. MANdentity is designed for any man with any struggle to find healing. It's purposely written to be simple and direct, so that every man can heal by working through this book. All men have wounds. The MANdentity book intends to heal them. Today, Adam travels the country sharing the redemptive story of Grace that Jesus brought to his life through his personal journey to MANdentity. He is a motivational speaker who encourages others to live out their faith. You will be challenged, motivated and encourage by his teaching.

Employee stock option plans have become a common component of remuneration packages in multinational enterprises. This publication presents and examines the many important tax issues that arise for beneficiaries and companies.

This two-volume set on Mathematical Principles of the Internet provides a comprehensive overview of the mathematical principles of Internet engineering. The books do not aim to provide all of the mathematical foundations upon which the Internet is based. Instead, they cover a partial panorama and the key principles. Volume 1 explores Internet engineering, while the supporting mathematics is covered in Volume 2. The chapters on mathematics complement those on the engineering episodes, and an effort has been made to make this work succinct, yet self-contained. Elements of information theory, algebraic coding theory, cryptography, Internet traffic, dynamics and control of Internet congestion, and queueing theory are discussed. In addition, stochastic networks, graph-theoretic algorithms, application of game theory to the Internet, Internet economics, data mining and knowledge discovery, and quantum computation, communication, and cryptography are also discussed. In order to study the structure and function of the Internet, only a basic knowledge of number theory, abstract algebra, matrices and determinants, graph theory, geometry, analysis, optimization theory, probability theory, and stochastic processes, is required. These mathematical disciplines are defined and developed in the books to the extent that is needed to develop and justify their application to Internet engineering.

Dealing with many aspects of the design, implementation and operation of databases for production management systems, this book presents research that is important to all those presently concerned with the computerisation of production management.