"Information Theory and Language" is a collection of 12 articles that appeared recently in Entropy as part of a Special Issue of the same title. These contributions represent state-of-the-art interdisciplinary research at the interface of information theory and language studies.

On May 1, 2004, the world of theoretical computer science su?ered a stunning loss: Shimon Even passed away. Few computer scientists have had as long, s- tained, and in?uential a career as Shimon. Shimon Even was born in Tel-Aviv in 1935. He received a B.Sc. in Elect- cal Engineering from the Technion in 1959, an M.A. in Mathematics from the University of Northern Carolina in 1961, and a Ph.D. in Applied Mathematics from Harvard University in 1963. He held positions at the Technion (1964–67 and 1974–2003), Harvard University (1967–69), the Weizmann Institute (1969– 74), and the Tel-Aviv Academic College (2003-04). He visited many universities and research institutes, including Bell Laboratories, Boston University, Cornell, Duke, Lucent Technologies, MIT, Paderborn, Stanford, UC-Berkeley, USC and UT-Dallas. Shimon Even played a major role in establishing computer science education in Israel and led the development of academic programs in two major insti- tions: the Weizmann Institute and the Technion. In 1969 he established at the Weizmann the ?rst computer science education program in Israel, and led this program for ?ve years. In 1974 he joined the newly formed computer science department at the Technion and shaped its academic development for several decades. These two academic programs turned out to have a lasting impact on the evolution of computer science in Israel.

Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data.

This volumes comprises select proceedings of the International Conference on Computational Intelligence in Machine Learning (ICCIML 2022). The contents cover latest research trends and developments in the areas of machine learning, smart cities, IoT, Artificial Intelligence, cyber physical systems, cybernetics, data science, neural network, cognition, among others. It also addresses the comprehensive nature of computational intelligence, AI, ML and DL to emphasize its character in modelling, identification, optimization, prediction, forecasting, and control of future intelligent systems. This volume will be a useful guide to those working as researchers in academia and industry by presenting in-depth fundamental research contributions from a methodological/application perspective in understanding Artificial intelligence and machine learning approaches and their capabilities in solving diverse range of problems in industries and its real-world applications.

The widespread interest this book has found among professors, scientists and stu­ dents working in a variety of fields has made a new edition necessary. I have used this opportunity to add three new chapters on recent developments. One of the most fascinating fields of modern science is cognitive science which has become a meet­ ing place of many disciplines ranging from mathematics over physics and computer science to psychology.

Rainbow connections are natural combinatorial measures that are used in applications to secure the transfer of classified information between agencies in communication networks. Rainbow Connections of Graphs covers this new and emerging topic in graph theory and brings together a majority of the results that deal with the concept of rainbow connections, first introduced by Chartrand et al. in 2006.

Most networks and databases that humans have to deal with contain large, albeit finite number of units. Their structure, for maintaining functional consistency of the components, is essentially not random and calls for a precise quantitative description of relations between nodes (or data units) and all network components. This book is an introduction, for both graduate students and newcomers to the field, to the theory of graphs and random walks on such graphs. The methods based on random walks and diffusions for exploring the structure of finite connected graphs and databases are reviewed (Markov chain analysis). This provides the necessary basis for consistently discussing a number of applications such diverse as electric resistance networks, estimation of land prices, urban planning, linguistic databases, music, and gene expression regulatory networks.

Facilitate coding in generating geometric motives with a special focus on analyzing their geometric formulations. This book aims to teach analytical coding skills by combining arts and mathematics.

This SpringerBrief presents channel estimation strategies for the physical later network coding (PLNC) systems. Along with a review of PLNC architectures, this brief examines new challenges brought by the special structure of bi-directional two-hop transmissions that are different from the traditional point-to-point systems and unidirectional relay systems. The authors discuss the channel estimation strategies over typical fading scenarios, including frequency flat fading, frequency selective fading and time selective fading, as well as future research directions. Chapters explore the performance of the channel estimation strategy and optimal structure of training sequences for each scenario. Besides the analysis of channel estimation strategies, the book also points out the necessity of revisiting other signal processing issues for the PLNC system. Channel Estimation of Physical Layer Network Coding Systems is a valuable resource for researchers and professionals working in wireless communications and networks. Advanced-level students studying computer science and electrical engineering will also find the content helpful.

Mohammed Rajab proposes different technologies like the error correction coding (ECC), sources coding and offset calibration that aim to improve the reliability of the NAND flash memory with low implementation costs for industrial application. The author examines different ECC schemes based on concatenated codes like generalized concatenated codes (GCC) which are applicable for NAND flash memories by using the hard and soft input decoding. Furthermore, different data compression schemes are examined in order to reduce the write amplification effect and also to improve the error correct capability of the ECC by combining both schemes.

The video coding standard High Efficiency Video Coding (HEVC) targets at improved compression performance for video resolutions of HD and beyond, providing Ultra HD video at similar compressed bit rates as for HD video encoded with the well-established video coding standard H.264/AVC. Based on known concepts, new coding structures and improved coding tools have been developed and specified in HEVC. The standard is expected to be taken up easily by established industry as well as new endeavors, answering the needs of todays connected and ever-evolving online world.