WWW: past, present, and future

The World Wide Web is simply defined as the universe of global network-accessible information. It is an abstract space within which people can interact, and it is chiefly populated by interlinked pages of text, images, and animations, with occasional sounds, videos, and three-dimensional worlds. The Web marks the end of an era of frustrating and debilitating incompatibility between computer systems. It has created an explosion of accessibility, with many potential social and economical impacts. The Web was designed to be a space within which people could work on a project. This was a powerful concept, in that: people who build a hypertext document of their shared understanding can refer to it at all times; people who join a project team can have access to a history of the team's activities, decisions, and so on; the work of people who leave a team can be captured for future reference; and a team's operations, if placed on the Web, can be machine-analyzed in a way that could not be done otherwise. The Web was originally supposed to be a personal information system and a tool for groups of all sizes, from a team of two to the entire world. People have rapidly developed new features for the Web, because of its tremendous commercial potential. This has made the maintenance of globalWeb interoperability a continuous task. This has also created a number of areas into which research must continue     

Connectionism: past,present, and future

Research efforts to study computation and cognitive modeling on neurally-inspired mechanisms have come to be called Connectionism. Rather than being brand new, it is actually the rebirth of a research programme which thrived from the 40s through the 60s and then was severely retrenched in the 70s. Connectionism is often posed as a paradigmatic competitor to the Symbolic Processing tradition of Artificial Intelligence (Dreyfus & Dreyfus, 1988), and, indeed, the counterpoint in the timing of their intellectual and commercial fortunes may lead one to believe that research in cognition is merely a zero-sum game. This paper surveys the history of the field, often in relation to AI, discusses its current successes and failures, and makes some predictions for where it might lead in the future.     

Rough sets: past,present, and future

Introduction of rough sets by Professor Zdzis?aw Pawlak has completed 35 years. The theory has already attracted the attention of many researchers and practitioners, who have contributed essentially to its development, from all over the world. The methods, developed based on rough set theory alone or in combination with other approaches, found applications in many areas. In this article, we outline some selected past and present research directions of rough sets. In particular, we emphasize the importance of searching strategies for relevant approximation spaces as the basic tools in achieving computational building blocks (granules or patterns) required for approximation of complex vague concepts. We also discuss new challenges related to problem solving by intelligent systems (IS) or complex adaptive systems (CAS). The concern is to control problems using interactive granular computing, an extension of the rough set approach, for effective realization of computations realized in IS or CAS. These challenges are important for the development of natural computing too.     

Energy-efficient networking: past, present, and future

The twenty-first century has witnessed major technological changes that have transformed the way we live, work, and interact with one another. One of the major technology enablers responsible for this remarkable transformation in our global society is the deployment and use of Information and Communication Technology (ICT) equipment. In fact, today ICT has become highly integrated in our society that includes the dependence on ICT of various sectors, such as business, transportation, education, and the economy to the point that we now almost completely depend on it. Over the last few years, the energy consumption resulting from the usage of ICT equipment and its impact on the environment have fueled a lot of interests among researchers, designers, manufacturers, policy makers, and educators. We present some of the motivations driving the need for energy-efficient communications. We describe and discuss some of the recent techniques and solutions that have been proposed to minimize energy consumption by communication devices, protocols, networks, end-user systems, and data centers. In addition, we highlight a few emerging trends and we also identify some challenges that need to be addressed to enable novel, scalable, cost-effective energy-efficient communications in the future.     

Coping with complexity: past, present and future

In 1981, a technical report was published with the somewhat enigmatic title ‘Coping with complexity.’ Its purpose was to discuss how computers could be used to assist process plant operators in coping with complex situations during plant disturbances. Today, coping with complexity is a problem not only for process plant operators but for everyone. And while computers in 1981 were looked upon as the solution, they are now seen as the source of the problem. This paper discusses why and how the meaning of ‘coping with complexity’ has changed over the years and speculate on what may lie ahead.     

Neural machine translation: past,present, and future

Neural Computing and Applications - Deep neural networks (DNN) have achieved great success in several research areas like information retrieval, image processing, and speech recognition. In the...     

Information ecology research: past,present, and future

This paper examines the status of information ecology research through studying the published papers on the topic of information ecology included in Social Sciences Citation Index and Science Citation Index database from 1992 to 2013. It applies bibliometrics and knowledge mapping to analyze the changes in the number of published papers as time goes on, in terms of country and geographic area, research topics, research methods, funding sources, hot research spots, and research trends. In addition, this paper summarizes the origin and the evolution of information ecology research and introduces institutions that conduct information ecology research. The results indicate that information ecology is an emerging field with vigorous development in recent years, and information ecology research is a multi-disciplinary subject. The research also reveals that information ecology research mainly focuses on information ecosystems, information ecology in e-commerce, and information ecology in a network. This paper calls for wider and deeper research on information ecology, in order to explore information ecology issues caused by the rapid development of new technologies.     

Design automation in MEDEA: present and future

The semiconductor industry has been growing at an unprecedented level since its start in the early 1960s. Capitalizing on the outstanding properties of silicon and its stable oxide permitted the introduction of the CMOS industry, the leading semiconductor industry process. However, the average 15% to 16% annual growth in semiconductor sales has also presented tremendous problems of huge investments in manufacturing. Mandatory now is a rapid return of investment through advanced products (in the latest available processes) that have high added value at the system level. Sometimes the system is the product itself, which raises thoughts of new ways to design these complex systems on a single chip that mixes several functionalities. This article explores a future European evolution of design automation, discussing the present status of Europe's lack of local industrial developments as well as its tremendous knowledge reservoir. Earlier MEDEA activities paved the road to new design solutions and gave European companies the chance to influence US developments, as is evidenced by frequent technology partnerships with US software vendors. Recently, we have seen a significant increase in European start-ups in advanced design automation domains (hardware-software codesign, intellectual property reuse, deep-submicron effects)     

Application areas of AIS: The past,the present and the future

After a decade of research into the area of artificial immune systems, it is worthwhile to take a step back and reflect on the contributions that the paradigm has brought to the application areas to which it has been applied. Undeniably, there have been a lot of successful stories—however, if the field is to advance in the future and really carve out its own distinctive niche, then it is necessary to be able to illustrate that there are clear benefits to be obtained by applying this paradigm rather than others. This paper attempts to take stock of the application areas that have been tackled in the past, and ask the difficult question “was it worth it ?”. We then attempt to suggest a set of problem features that we believe will allow the true potential of the immunological system to be exploited in computational systems, and define a unique niche for AIS.     

Digital multimedia audio forensics: past,present and future

Multimedia Tools and Applications - Digital audio forensics is used for a variety of applications ranging from authenticating audio files to link an audio recording to the acquisition device (e.g.,...     

Putting the brain to work: neuroergonomics past, present, and future

OBJECTIVE: The authors describe research and applications in prominent areas of neuroergonomics. BACKGROUND: Because human factors/ergonomics examines behavior and mind at work, it should include the study of brain mechanisms underlying human performance. METHODS: Neuroergonomic studies are reviewed in four areas: workload and vigilance, adaptive automation, neuroengineering, and molecular genetics and individual differences. RESULTS: Neuroimaging studies have helped identify the components of mental workload, workload assessment in complex tasks, and resource depletion in vigilance. Furthermore, real-time neurocognitive assessment of workload can trigger adaptive automation. Neural measures can also drive brain-computer interfaces to provide disabled users new communication channels. Finally, variants of particular genes can be associated with individual differences in specific cognitive functions. CONCLUSIONS: Neuroergonomics shows that considering what makes work possible - the human brain - can enrich understanding of the use of technology by humans and can inform technological design. APPLICATION: Applications of neuroergonomics include the assessment of operator workload and vigilance, implementation of real-time adaptive automation, neuroengineering for people with disabilities, and design of selection and training methods.     

Theories, tools and research methods in program comprehension: past, present and future

Program comprehension research can be characterized by both the theories that provide rich explanations about how programmers understand software, as well as the tools that are used to assist in comprehension tasks. In this paper, I review some of the key cognitive theories of program comprehension that have emerged over the past thirty years. Using these theories as a canvas, I then explore how tools that are commonly used today have evolved to support program comprehension. Specifically, I discuss how the theories and tools are related and reflect on the research methods that were used to construct the theories and evaluate the tools. The reviewed theories and tools are distinguished according to human characteristics, program characteristics, and the context for the various comprehension tasks. Finally, I predict how these characteristics will change in the future and speculate on how a number of important research directions could lead to improvements in program comprehension tool development and research methods. Dr. Margaret-Anne Storey is an associate professor of computer science at the University of Victoria, a Visiting Scientist at the IBM Centre for Advanced Studies in Toronto and a Canada Research Chair in Human Computer Interaction for Software Engineering. Her research passion is to understand how technology can help people explore, understand and share complex information and knowledge. She applies and evaluates techniques from knowledge engineering and visual interface design to applications such as reverse engineering of legacy software, medical ontology development, digital image management and learning in web-based environments. She is also an educator and enjoys the challenges of teaching programming to novice programmers.     

On the automated,evolutionary design of neural networks: past,present, and future

Neuroevolution is the name given to a field of computer science that applies evolutionary computation for evolving some aspects of neural networks. After the AI Winter came to an end, neural networks reemerged to solve a great variety of problems. However, their usage requires designing their topology, a decision with a potentially high impact on performance. Whereas many works have tried to suggest rules-of-thumb for designing topologies, the truth is that there are not analytic procedures for determining the optimal one for a given problem, and trial-and-error is often used instead. Neuroevolution arose almost 3 decades ago, with some works focusing on the evolutionary design of the topology and most works describing techniques for learning connection weights. Since then, evolutionary computation has been proved to be a convenient approach for determining the topology and weights of neural networks, and neuroevolution has been applied to a great variety of fields. However, for more than 2 decades neuroevolution has mainly focused on simple artificial neural networks models, far from today’s deep learning standards. This is insufficient for determining good architectures for modern networks extensively used nowadays, which involve multiple hidden layers, recurrent cells, etc. More importantly, deep and convolutional neural networks have become a de facto standard in representation learning for solving many different problems, and neuroevolution has only focused in this kind of networks in very recent years, with many works being presented in 2017 onward. In this paper, we review the field of neuroevolution during the last 3 decades. We will put the focus on very recent works on the evolution of deep and convolutional neural networks, which is a new but growing field of study. To the best of our knowledge, this is the best survey reviewing the literature in this field, and we have described the features of each work as well as their performance on well-known databases when available. This work aims to provide a complete reference of all works related to neuroevolution of convolutional neural networks up to the date. Finally, we will provide some future directions for the advancement of this research area.