Research Directions in Computer Science celebrates the twenty-fifth anniversary of the founding of MIT's Project MAC. It covers the full range of ongoing computer science research at the MIT Laboratory for Computer Science and the MIT Artificial Intelligence Laboratory, both of which grew out of the original Project MAC. Leading researchers from the faculties and staffs of the laboratories highlight current research and future activities in multiprocessors and parallel computer architectures, in languages and systems for distributed computing, in intelligent systems (AI) and robotics, in complexity and learning theory, in software methodology, in programming language theory, in software for engineering research and education, and in the relation between computers and economic productivity. ContributorsAbelson, Arvind, Rodney Brooks, David Clark, Fernando Corbato, William Daily, Michael Dertouzos, John Guttag, Berthold K. P. Horn, Barbara Liskov, Albert Meyer, Nicholas Negroponte, Marc Raibert, Ronald Rivest, Michael Sipser, Gerald Sussman, Peter Szolovits, and John Updike

Once a radical notion, object-oriented programming is one of today's most active research areas. It is especially well suited to the design of very large software projects involving many programmers all working on the same project. The original contributions in this book will provide researchers and students in programming languages, databases, and programming semantics with the most complete survey of the field available. Broad in scope and deep in its examination of substantive issues, the book focuses on the major topics of object-oriented languages, models of computation, mathematical models, object-oriented databases, and object-oriented environments. The object-oriented languages include Beta, the Scandinavian successor to Simula (a chapter by Bent Kristensen, whose group has had the longest experience with object-oriented programming, reveals how that experience has shaped the group's vision today); CommonObjects, a Lisp-based language with abstraction; Actors, a low-level language for concurrent modularity; and Vulcan, a Prolog-based concurrent object-oriented language. New computational models of inheritance, composite objects, block-structure layered systems, and classification are covered, and theoretical papers on functional object-oriented languages and object-oriented specification are included in the section on mathematical models. The three chapters on object-oriented databases (including David Maier's "Development and Implementation of an Object-Oriented Database Management System," which spans the programming and database worlds by integrating procedural and representational capability and the requirements of multi-user persistent storage) and the two chapters on object-oriented environments provide a representative sample of good research in these two important areas. Bruce Shriver is a researcher at IBM's Thomas J. Watson Research Center. Peter Wegner is a professor in the Department of Computer Science at Brown University. Research Directions in Object-Oriented Programmingis included in the Computer Systems series, edited by Herb Schwetman.

Programming is hard. Building a large program is like constructing a steam locomotive through a hole the size of a postage stamp. An artefact that is the fruit of hundreds of person-years is only ever seen by anyone through a lOO-line window. In some ways it is astonishing that such large systems work at all. But parallel programming is much, much harder. There are so many more things to go wrong. Debugging is a nightmare. A bug that shows up on one run may never happen when you are looking for it - but unfailingly returns as soon as your attention moves elsewhere. A large fraction of the program's code can be made up of marshalling and coordination algorithms. The core application can easily be obscured by a maze of plumbing. Functional programming is a radical, elegant, high-level attack on the programming problem. Radical, because it dramatically eschews side-effects; elegant, because of its close connection with mathematics; high-level, be cause you can say a lot in one line. But functional programming is definitely not (yet) mainstream. That's the trouble with radical approaches: it's hard for them to break through and become mainstream. But that doesn't make functional programming any less fun, and it has turned out to be a won derful laboratory for rich type systems, automatic garbage collection, object models, and other stuff that has made the jump into the mainstream.

Discrete mathematics and theoretical computer science are closely linked research areas with strong impacts on applications and various other scientific disciplines. Both fields deeply cross fertilize each other. One of the persons who particularly contributed to building bridges between these and many other areas is László Lovász, a scholar whose outstanding scientific work has defined and shaped many research directions in the last 40 years. A number of friends and colleagues, all top authorities in their fields of expertise and all invited plenary speakers at one of two conferences in August 2008 in Hungary, both celebrating Lovász’s 60th birthday, have contributed their latest research papers to this volume. This collection of articles offers an excellent view on the state of combinatorics and related topics and will be of interest for experienced specialists as well as young researchers.

The past 50 years have witnessed a revolution in computing and related communications technologies. The contributions of industry and university researchers to this revolution are manifest; less widely recognized is the major role the federal government played in launching the computing revolution and sustaining its momentum. Funding a Revolution examines the history of computing since World War II to elucidate the federal government's role in funding computing research, supporting the education of computer scientists and engineers, and equipping university research labs. It reviews the economic rationale for government support of research, characterizes federal support for computing research, and summarizes key historical advances in which government-sponsored research played an important role. Funding a Revolution contains a series of case studies in relational databases, the Internet, theoretical computer science, artificial intelligence, and virtual reality that demonstrate the complex interactions among government, universities, and industry that have driven the field. It offers a series of lessons that identify factors contributing to the success of the nation's computing enterprise and the government's role within it.

The book details deep learning models like ANN, RNN, LSTM, in many industrial sectors such as transportation, healthcare, military, agriculture, with valid and effective results, which will help researchers find solutions to their deep learning research problems. We have entered the era of smart world devices, where robots or machines are being used in most applications to solve real-world problems. These smart machines/devices reduce the burden on doctors, which in turn make their lives easier and the lives of their patients better, thereby increasing patient longevity, which is the ultimate goal of computer vision. Therefore, the goal in writing this book is to attempt to provide complete information on reliable deep learning models required for e-healthcare applications. Ways in which deep learning can enhance healthcare images or text data for making useful decisions are discussed. Also presented are reliable deep learning models, such as neural networks, convolutional neural networks, backpropagation, and recurrent neural networks, which are increasingly being used in medical image processing, including for colorization of black and white X-ray images, automatic machine translation images, object classification in photographs/images (CT scans), character or useful generation (ECG), image caption generation, etc. Hence, reliable deep learning methods for the perception or production of better results are a necessity for highly effective e-healthcare applications. Currently, the most difficult data-related problem that needs to be solved concerns the rapid increase of data occurring each day via billions of smart devices. To address the growing amount of data in healthcare applications, challenges such as not having standard tools, efficient algorithms, and a sufficient number of skilled data scientists need to be overcome. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in transportation, healthcare, biomedicine, military, agriculture.