This book contains the proceedings of the Tenth International Network Conference (INC 2014), which was held in Plymouth, UK, in July 2014. A total of 13 papers were accepted for inclusion in the conference (with a further three papers included in these proceedings from the Ninth International Workshop on Digital Forensics and Incident Analysis). The main topics of the book include: Network Technologies; Mobile and Wireless Networking; Security and Privacy; Applications and Impacts. The papers address state-of-the-art research and applications of network technology, arising from both the academic and industrial domains. These proceedings should consequently be of interest to network practitioners, researchers, academics, and technical managers involved in the design, development and use of network systems.

This work presents link prediction similarity measures for social networks that exploit the degree distribution of the networks. In the context of link prediction in dense networks, the text proposes similarity measures based on Markov inequality degree thresholding (MIDTs), which only consider nodes whose degree is above a threshold for a possible link. Also presented are similarity measures based on cliques (CNC, AAC, RAC), which assign extra weight between nodes sharing a greater number of cliques. Additionally, a locally adaptive (LA) similarity measure is proposed that assigns different weights to common nodes based on the degree distribution of the local neighborhood and the degree distribution of the network. In the context of link prediction in dense networks, the text introduces a novel two-phase framework that adds edges to the sparse graph to forma boost graph.

This book contains the proceedings of the Eleventh International Network Conference (INC 2016), which was held in Frankfurt, Germany, in July 2016. A total of 30 papers were accepted for inclusion in the conference. The main topics of the book include: Network Technologies; Mobile and Wireless Networking; Security and Privacy; Applications and Impacts. The papers address state-of-the-art research and applications of network technology, arising from both the academic and industrial domains. These proceedings should consequently be of interest to network practitioners, researchers, academics, and technical managers involved in the design, development and use of network systems.

This book constitutes the proceedings of the 24th International Symposium on Foundations of Intelligent Systems, ISMIS 2018, held in Limassol, Cyprus, in October 2018. The 32 full, 8 short, and 4 application papers presented in this volume were carefully reviewed and selected from 59 submissions. The papers deal with topics such as bioinformatics and health informatics, graph mining, image analysis, intelligent systems, mining complex patterns, novelty detection and class imbalance, social data analysis, spatio-temporal analysis, and topic modeling and opinion mining. In addition, three special sessions were organized, namely: Special Session on Granular and Soft Clustering for Data Science, Special Session on Intelligent Methodologies for Traffic Data Analysis and Mining, and Special Session on Advanced Methods in Machine Learning for Modeling Complex Data.

Spectrum Sharing in Wireless Networks: Fairness, Efficiency, and Security provides a broad overview of wireless network spectrum sharing in seven distinct sections: The first section examines the big picture and basic principles, explaining the concepts of spectrum sharing, hardware/software function requirements for efficient sharing, and future trends of sharing strategies. The second section contains more than 10 chapters that discuss differing approaches to efficient spectrum sharing. The authors introduce a new coexistence and sharing scheme for multi-hop networks, describe the space-time sharing concept, introduce LTE-U, and examine sharing in broadcast and unicast environments. They then talk about different cooperation strategies to achieve mutual benefits for primary users (PU) and secondary users (SU), discuss protocols in a spectrum sharing context, and provide different game theory models between PUs and SUs. The third section explains how to model the interactions of PUs and SUs, using an efficient calculation method to determine spectrum availability. Additionally, this section explains how to use scheduling models to achieve efficient SU traffic delivery. The subject of the fourth section is MIMO-oriented design. It focuses on how directional antennas and MIMO antennas greatly enhance wireless network performance. The authors include a few chapters on capacity/rate calculations as well as beamforming issues under MIMO antennas. Power control is covered in the fifth section which also describes the interference-aware power allocation schemes among cognitive radio users and the power control schemes in cognitive radios. The sixth section provides a comprehensive look at security issues, including different types of spectrum sharing attacks and threats as well as corresponding countermeasure schemes. The seventh and final section covers issues pertaining to military applications and examines how the military task protects its data flows when sharing the spectrum with civilian applications.

A highly accessible, step-by-step introduction to deep learning, written in an engaging, question-and-answer style. The Little Learner introduces deep learning from the bottom up, inviting students to learn by doing. With the characteristic humor and Socratic approach of classroom favorites The Little Schemer and The Little Typer, this kindred text explains the workings of deep neural networks by constructing them incrementally from first principles using little programs that build on one another. Starting from scratch, the reader is led through a complete implementation of a substantial application: a recognizer for noisy Morse code signals. Example-driven and highly accessible, The Little Learner covers all of the concepts necessary to develop an intuitive understanding of the workings of deep neural networks, including tensors, extended operators, gradient descent algorithms, artificial neurons, dense networks, convolutional networks, residual networks, and automatic differentiation. Conversational style, illustrations, and question-and-answer format make deep learning accessible and fun Incremental approach constructs advanced concepts from first principles Presents key ideas of machine learning using a small, manageable subset of the Scheme language Suitable for anyone with knowledge of high school math and some programming experience

This book constitutes the thoroughly refereed proceedings of the 38th National Conference of Theoretical Computer Science, NCTCS 2020, held in Nanning, China, in November 2020. The 13 full papers presented were carefully reviewed and selected from 28 submissions. They present recent research in the areas of algorithms and complexity, matrix computation; deep learning; network communication and security.