To commemorate the 50th anniversary of the IEEE Transactions on Systems, Man, and Cybernetics: Systems, this article examines and reports on its past to current topical coverage of systems science and engineering toward exploring the evolving focus of the research community. Results of a systematic bibliometric analysis are presented with associated conclusions, implications, and summary of topical areas. In addition, respective views regarding the current state of the field and where it is headed are offered by recent leaders of the IEEE Systems, Man, and Cybernetics Society, including its continued relevance and role in the advancement of systems technology.

Welcome to the last issue of the IEEE Transactions on Computational Social Systems (IEEE TCSS) in 2018. Starting from the first issue next year, our Transactions will be a bimonthly publication, entering a new stage for the IEEE TCSS.

In this paper and attached video, we present a third-generation expert system named Knowledge Amplification by Structured Expert Randomization (KASER) for which a patent has been filed by the U.S. Navy's SPAWAR Systems Center, San Diego, CA (SSC SD). KASER is a creative expert system. It is capable of deductive, inductive, and mixed derivations. Its qualitative creativity is realized by using a tree-search mechanism. The system achieves creative reasoning by using a declarative representation of knowledge consisting of object trees and inheritance. KASER computes with words and phrases. It possesses a capability for metaphor-based explanations. This capability is useful in explaining its creative suggestions and serves to augment the capabilities provided by the explanation subsystems of conventional expert systems. KASER also exhibits an accelerated capability to learn. However, this capability depends on the particulars of the selected application domain. For example, application domains such as the game of chess exhibit a high degree of geometric symmetry. Conversely, application domains such as the game of craps played with two dice exhibit no predictable pattern, unless the dice are loaded. More generally, we say that domains whose informative content can be compressed to a significant degree without loss (or with relatively little loss) are symmetric. Incompressible domains are said to be asymmetric or random. The measure of symmetry plus the measure of randomness must always sum to unity.

This paper addresses the problem of detecting and countering potential terrorist threats. It describes a synergistic conjunction of intelligent technologies with a view towards that goal. Many databases exist, which when fused through the use of intelligent agents can provide an intermediary database that often contains critical information. The goal of this paper is to show how a directed mining approach using a blackboard architecture can be designed to extract the maximum amount of relevant information from the database, while filtering out the irrelevant information in tractable timeframes. Blackboard architectures are rule-based and allow for the incremental insertion of knowledge. Subsumption and contradiction can be automatically detected and remedied. Moreover, the use of a segmented knowledge base allows computational resources to be focused where they will do the most good. Objects for directed mining and linking are invoked as rule consequents in a blackboard architecture. The blackboard architecture provides for chaining and incremental knowledge acquisition (including maintenance). Using information hiding, objects can be visually displayed in the form of a tree to facilitate acquisition and maintenance operations. Predicate knowledge is represented using a VHLL that provides several attendant benefits. First, higher-level languages are not only easier to use, but they tend to be self-documenting. Second, the higher the level of language, the more reusable are components written in the language. Not only does this save on the cost of generating new components, but it also serves to further their testing (i.e., debugging).

Perception is crucial to data mining. When dealing with real-world problems, e.g., expert systems analyzing stock market and financial data, etc., there is a difference between the real world and what the user (or expert system) perceives to be the real world. Often data mining retrieves perceived data and the problem is to reconcile this perceived data with the real world. For example, data mining might retrieve a perceived set of rules learned from long experience by a plant operator in operating a plant (and which can vary significantly from the mathematical model of the plant system). How do we reconcile the two different models? Which is the real one? Information granulation can help with this problem.

Introduces a notion of relevance (conceptual stability) of information granules. Granulation of data results in a series of chunks of information usually referred to as information granules. These information granules are basic building entities involved in the design of a broad class of systems. Information granules are also percepts-entities being perceived by humans as being essential while working with some real-world phenomena, especially describing and interacting with them. The percepts need to be comprehensible. They should also reflect the experimental evidence. Furthermore, information granules should be stable, meaning that they reconcile experimental reality with the subjective and ultimately observer-based judgement about the environment. Once being stable, information granules could be viewed as independent. The proposed environment supporting this concept dwells on the ideas of statistical inference that helps quantify stability thorough nonparametric testing.

We discuss distributed denial of service attacks in the Internet. We were motivated by the widely known February 2000 distributed attacks on Yahoo!, Amazon.com, CNN.com, and other major Web sites. A denial of service is characterized by an explicit attempt by an attacker to prevent legitimate users from using resources. An attacker may attempt to: "flood" a network and thus reduce a legitimate user's bandwidth, prevent access to a service, or disrupt service to a specific system or a user. We describe methods and techniques used in denial of service attacks, and we list possible defences. In our study, we simulate a distributed denial of service attack using ns-2 network simulator. We examine how various queuing algorithms implemented in a network router perform during an attack, and whether legitimate users can obtain desired bandwidth. We find that under persistent denial of service attacks, class based queuing algorithms can guarantee bandwidth for certain classes of input flows.

Summary form only. Data mining in databases aims to make sense of data by revealing meaningful and easily interpretable relationships. In spite of many existing variations, this research goal permeates the entire area. The domain of data mining is highly heterogeneous embracing a number of well-established information technologies including: statistical pattern recognition, neural networks, machine learning, knowledge-based systems, etc. The synergistic character of data mining is one of its dominant features that makes this pursuit an emerging new area of research and application. By its nature, data mining is very much oriented towards the end-user, thus implying that any results need to be easily interpretable. Granulation of information promotes this interpretability and channels pursuits of data mining, which can be computationally intensive and thus highly prohibitive, towards more efficient and feasible processing of information granules. Finally, an interesting emerging area of data mining involves perception. Data mining often retrieves "perceived data" and the problem is to reconcile this perceived data with the real world. Information granulation can help with this problem.

We describe the conceptual design of FuzzyBase, an information-intelligent system. The purpose of the proposed system is to facilitate intelligent and fast retrieval of information that is of interest to scientific research communities with specific needs, such as getting relevant technical information fast. It involves the intelligent retrieval of information (both crisp and fuzzy), a subsystem for collecting usage statistics and traffic data, and a high end computer and communications infrastructure.

This paper discusses the swing-up control of a two link robot mechanism known as the Acrobot. First a PD controller is investigated and then a fuzzy controller is implemented by a combination of genetic algorithms, dynamic switching fuzzy systems, and meta-rule techniques. Limitations of the PD controller and the fuzzy controller are shown by investigating the effect of external random disturbances on the Acrobot using simulation. Design considerations are discussed. This study illustrates how intelligent control methods can reduce development effort and system complexity while achieving better performance.