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The traditional collaborative fuzzy clustering can effectively perform data clustering in distributed peer-to-peer (P2P) networks, which is an impossible task to complete for the centralized clustering methods due to privacy and security requirements or network transmission technology constraints. But it will increase the number of clustering iterations and lead to lower efficiency of the clustering. Moreover, the collaborative mechanism hidden in the iterative process of clustering cannot be well revealed and explained. In this paper, a novel series of transfer collaborative fuzzy clustering algorithms are proposed to solve these issues. In the first basic algorithm, the transfer learning among neighbor nodes vividly expresses the collaborative mechanism and enhances the information collaboration to accelerate the convergence of fuzzy clustering. Meanwhile, neighbor nodes can learn the knowledge from each other to further promote their respective clustering performance. Then, an improved version with the learning-rate-adjustable strategy instead of fixed values is designed to highlight the different influence between neighbor nodes, and the appropriate learning rates between neighbor nodes are achieved to ensure the stable clustering accuracy. Finally, two extended versions with the attribute-weight-entropy regularization technique are presented for the clustering of high dimensional sparse data and the extraction of important subspace features. Experiments show the efficiency of the proposed algorithms compared with the related prototype-based clustering methods.

Uncertain data clustering has been recognized as an essential task in the research of data mining. Many centralized clustering algorithms are extended by defining new distance or similarity measurements to tackle this issue. With the fast development of network applications, these centralized methods show their limitations in conducting data clustering in a large dynamic distributed peer-to-peer network due to the privacy and security concerns or the technical constraints brought by distributive environments. In this paper, we propose a novel distributed uncertain data clustering algorithm, in which the centralized global clustering solution is approximated by performing distributed clustering. To shorten the execution time, the reduction technique is then applied to transform the proposed method into its deterministic form by replacing each uncertain data object with its expected centroid. Finally, the attribute-weight-entropy regularization technique enhances the proposed distributed clustering method to achieve better results in data clustering and extract the essential features for cluster identification. The experiments on both synthetic and real-world data have shown the efficiency and superiority of the presented algorithm.

A persistent challenge to AI theories and technologies is fake news recognition which demands not only syntactic analyses of language expressions, but also their semantics comprehension. This work presents an autonomous system for fake news recognition based on a novel approach of machine semantic learning. A training-free machine learning algorithm of Differential Sentence Semantic Analyses (DSSA) is designed and implemented for fake news detection. A large set of 876 experiments randomly selected from DataCup’ 19 has demonstrated a level of 70.4% accuracy that outperforms the traditional data-driven neural network technologies normally projected at the accuracy level of 55.0%. The DSSA methodology paves a way towards autonomous, training-free, and real-time trustworthy technologies for machine knowledge learning and semantics composition.

Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, physiology, psychology, arts and affective computing. In this article, music emotions are classified into four types known as those of pleasing, angry, sad and relaxing. MER is formulated as a classification problem in cognitive computing where 548 dimensions of music features are extracted and modeled. A set of classifications and machine learning algorithms are explored and comparatively studied for MER, which includes Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Neuro-Fuzzy Networks Classification (NFNC), Fuzzy KNN (FKNN), Bayes classifier and Linear Discriminant Analysis (LDA). Experimental results show that the SVM, FKNN and LDA algorithms are the most effective methodologies that obtain more than 80% accuracy for MER.

Cognitive knowledge learning (CKL) is a fundamental methodology for cognitive robots and machine learning. Traditional technologies for machine learning deal with object identification, cluster classification, pattern recognition, functional regression and behavior acquisition. A new category of CKL is presented in this paper embodied by the Algorithm of Cognitive Concept Elicitation (ACCE). Formal concepts are autonomously generated based on collective intension (attributes) and extension (objects) elicited from informal descriptions in dictionaries. A system of formal concept generation by cognitive robots is implemented based on the ACCE algorithm. Experiments on machine learning for knowledge acquisition reveal that a cognitive robot is able to learn synergized concepts in human knowledge in order to build its own knowledge base. The machine–generated knowledge base demonstrates that the ACCE algorithm can outperform human knowledge expressions in terms of relevance, accuracy, quantification and cohesiveness.

Cognitive Informatics (CI) is a contemporary field of basic studies on the brain, computational intelligence theories and underpinning denotational mathematics. Its applications include cognitive systems, cognitive computing, cognitive machine learning and cognitive robotics. IEEE ICCI*CC'17 on Cognitive Informatics and Cognitive Computing was focused on the theme of neurocomputation, cognitive machine learning and brain-inspired systems. This paper reports the plenary panel (Part I) at IEEE ICCI*CC'17 held at Oxford University. The summary is contributed by invited keynote speakers and distinguished panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and cognitive computing.

Autonomous systems underpinned by cognitive intelligence represent advanced forms of artificial intelligence studied in intelligence science, systems science, and computational intelligence. Traditional theories and technologies of autonomous systems put emphases on humansystem interactions and humans in-the-loop. This paper explores the intelligence and system foundations of autonomous systems. It focuses on what structural and behavioral properties constitute the intelligence power of autonomous systems. It explains how system intelligence aggregates from reflexive, imperative, adaptive intelligence to autonomous and cognitive intelligence. A Hierarchical Intelligence Model (HIM) is introduced to elaborate the evolution of human and system intelligence as an inductive process. A set of properties of system autonomy is formally analyzed towards a wide range of autonomous system applications in computational intelligence and systems engineering. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

The traditional clustering algorithms can not effectively deal with the clustering when the data for current task are not enough. In this paper, we utilize transfer learning to assist the entropy-weighted fuzzy c-means clustering. The clustering centers and corresponding weights of dimensions learned from the known data domain are used in the new objective function to assist the unknown data clustering. Experiments on synthetic data sets have demonstrated the superiority of the new algorithm.

Music emotion recognition (MER) is a challenging field of studies that has been addressed in multiple disciplines such as cognitive science, physiology, psychology, musicology, and arts. In this paper, music emotions are modeled as a set of continuous variables composed of valence and arousal (VA) values based on the Valence-Arousal model. MER is formulated as a regression problem where 548 dimensions of music features were extracted and selected. A wide range of methods including multivariate adaptive regression spline, support vector regression (SVR), radial basis function, random forest regression (RFR), and regression neural networks are adopted to recognize music emotions. Experimental results show that these regression algorithms have led to good regression effect for MER. The optimal R2 statistics and VA values are 29.3% and 62.5%, respectively, which are obtained by the RFR and SVR algorithms in the relief feature space.

Knowledge learning is the sixth and the most fundamental category of machine learning mimicking the brain. It is recognized that the semantic space of machine knowledge is a hierarchical concept network (HCN), which can be rigorously represented by formal concepts in concept algebra and semantic algebra. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic analysis based on machine learning. Semantic equivalence between formal concepts is rigorously measured by an Algorithm of Concept Equivalence Analysis (ACEA). The semantic hierarchy among formal concepts is quantitatively determined by an Algorithm of Relational Semantic Classification (ARSC). Experiments applying Algorithms ACEA and ARSC on a set of formal concepts have been successfully conducted, which demonstrate a deep machine understanding of formal concepts and quantitative relations in the hierarchical semantic space by machine learning beyond human empirical perspectives.

Process discovery, one of the key steps in process management, aims at discovering process models from process execution data stored in event logs. Most discovery algorithms assume that all data in an event log fully comply with the process execution specification. However, in real event logs, noise and irrelevant infrequent behaviour are often present. In this paper, we propose a novel filtering method that the removal of infrequent behavior from event logs. The method has been evaluated in detail and it is shown that its application in existing process discovery algorithms significantly improves the quality of the discovered process models and that it scales well to large datasets.

Although lexical and syntactic theories for phrase analyses have been well studied in linguistic theories and computational linguistics, semantic synthesis theories for cognitive computing are still a challenge in machine learning and brain-inspired systems. This paper studies theories and mathematical models of machine knowledge learning and semantic comprehension. An Algorithm of Unsupervised Phrase Learning (AUPL) is developed that enables cognitive machines to autonomously learn phrase semantics in the sixth category of machine knowledge learning. A set of experimental results is reported to demonstrate the methodology and algorithm. This work plays a fundamental role for sentence learning where the semantics of natural languages is reduced onto those of phrases and terminal words represented by formal concepts in cognitive systems.

Autonomous program code generation by machine learning is not only an ultimate goal but also a theoretical challenge to software science and engineering. A methodology and case study for code generation based on Real-Time Process Algebra (RTPA) by machine learning are presented in this paper. It describes a machine learning approach for code generation in MATLAB based on acquired RTPA rules and formal specifications. The design and implementation of the RTPA-MATLAB code generator is introduced, which is implemented by an RTPA parser and an MATLAB code builder. The experimental case studies have demonstrated the novelty of the theories and methodologies for code generation based on machine-learnt programming rules.

Recent development in machine learning and computational linguistics has enabled cognitive machines to understand the semantics of human expressions. A system for sentence syntactic analysis and semantic synthesis is developed based on denotational mathematics. Machine sentence learning and comprehension are reduced to the building of a composed concept that maps the semantics of the subject onto the counterpart of object(s) represented by formal concepts and phrases. A set of semantic operations such as concept composition, modification, generalization, specification, extension and reduction is formally specified based on concept algebra and semantic algebra for machine learning. An Algorithm for Unsupervised Sentence Learning (AUSL) is designed and implemented, which expresses a learnt sentence as a knowledge graph related to the semantic hierarchy of the machine's knowledge base. Experimental results demonstrate the autonomous learning algorithm and case studies on machine learning towards applications in cognitive robots and knowledge learning systems.

Formal semantic theories underpin a wide range of methodologies in knowledge science, cognitive linguistics, machine learning, and cognitive computing. This paper presents a set of mathematical rules for rigorous concept and semantics manipulations. Mathematical models of the universe of discourse of knowledge, semantic space and formal concepts are created. The formal properties of knowledge are elaborated in the categories of intension/extension, semantic and hierarchical rules based on concept algebra. Recent experiments and breakthroughs in cognitive systems of machine knowledge learning demonstrate applications of the fundamental theories for cognitive linguistics and brain-inspired machine learning.

Concept generation and refinement is a process to generate and improve machine's knowledge base represented by a comprehensive set of formal concepts. An unsupervised algorithm for concept refinement is developed for autonomously upgrading and enhancing acquired concepts of knowledge in a cognitive knowledge base built by cognitive robots and systems. The concept refinement algorithm is implemented based on a set of rules of concept algebra and semantic analyses. Experimental results demonstrate that cognitive machines can autonomously refine their knowledge by improving acquired concepts in a dynamic process mimicking human learning mechanisms in deep machine learning and cognitive computing.

An ontology is a taxonomic hierarchy of lexical terms and their syntactic and semantic relations for representing a framework of structured knowledge. Ontology used to be problem-specific and manually built due to its extreme complexity. Based on the latest advances in cognitive knowledge learning and formal semantic analyses, an Algorithm of Formal Ontology Generation (AFOG) is developed. The methodology of AFOG enables autonomous generation of quantitative ontologies in knowledge engineering and semantic comprehension via deep machine learning. A set of experiments demonstrates applications of AFOG in cognitive computing, semantic computing, machine learning and computational intelligence.

Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, physiology, psychology, arts and affective computing. In this paper, music emotions are classified into four types known as those of pleasing, angry, sad and relaxing. MER is formulated as a classification problem in cognitive computing where 548 dimensions of music features are extracted and modeled. A comprehensive set of classification algorithms are explored and comparatively studied for MER including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Neuro-Fuzzy Networks Classification (NFNC), Fuzzy KNN (FKNN), Bayes classifier and Linear Discriminant Analysis (LDA). Experimental results show that the SVM, FKNN and LDA algorithms are the most effective methodologies which obtain more than 80% accuracy for MER in performance.

Semantic comprehension is a fundamental challenge to machine learning. A methodology for building quantitative semantic hierarchies of formal concepts is formally described by cognitive machine learning. The algorithm of concept semantic hierarchy learning (CSH_Learning) is developed based on a set of semantic analysis and synthesis rules according to concept algebra. The algorithm and rules are applied in quantitative determination of levels of arbitrary concepts for the semantic hierarchy of a machines' cognitive knowledge bases. Experiments on 600+ formal concepts indicate the effectiveness of the algorithm for a wide range of applications including cognitive language processing, semantic analyses, cognitive machine learning, cognitive computation and computational linguistics.

A cognitive knowledge base (CKB) is a novel structure of intelligent knowledge base that represents and manipulates knowledge as a dynamic concept network mimicking human knowledge processing. The essence of CKB is the denotational mathematical model of formal concept that is dynamically associated to other concepts in a CKB beyond conventional rule-based or ontology-based knowledge bases. This paper presents a formal CKB and autonomous knowledge manipulation system based on recent advances in neuroinformatics, concept algebra, semantic algebra, and cognitive computing. An item knowledge in CKB is represented by a formal concept, while the entire knowledge base is embodied by a dynamic concept network. The CKB system is manipulated by algorithms of knowledge acquisition and retrieval on the basis of concept algebra. CKB serves as a kernel of cognitive learning engines for cognitive robots and machine learning systems. CKB plays a central role not only in explaining the mechanisms of human knowledge acquisition and learning, but also in the development of cognitive robots, cognitive learning engines, and knowledge-based systems.

Concept algebra (CA) is a denotational mathematics for formal knowledge manipulation and natural language processing. In order to explicitly demonstrate the mathematical models of formal concepts and their algebraic operations in CA, a simulation and visualization software is developed in the MATLAB environment known as the Visual Simulator of Concept Algebra (VSCA). This paper presents the design and implementation of VSCA and the theories underpinning its development. Visual simulations for the sets of reproductive and compositional operations of CA are demonstrated by real-world examples throughout the elaborations of CA and VSCA.

Semantics is composed meaning of language expressions and perceptions at the levels of words, phrase, sentence, paragraph, and essay. The basic unit of formal semantics is a concept as a formal model of words in a natural language. Cognitive linguistics focuses on cognitive semantics and its interaction with underlying syntactic structures. A denotational mathematics known as semantic algebra is presented for rigorous semantics manipulations in cognitive linguistics. Semantic algebra deals with formal semantics of linguistics by a general mathematical model of semantics and a set of algebraic operators. On the basis of semantic algebra, language semantics may not only be deductively analyzed based on syntactic structures from the top down, but also be synthetically composed by the algebraic semantic operators from the bottom up at different levels of language units. Semantic algebra enables a wide range of applications in cognitive informatics, cognitive linguistics, computational linguistics, semantic computing, cognitive computing, machine learning, computing with words, as well as natural language analysis, synthesis, and comprehension.

It is recognized that formal grammars and rigorous syntactic rules are indispensable in formal, cognitive, and computational linguistics. The formal syntax of linguistics can be classified into two categories known as the analytic and synthetic syntax. The former are a set of lexical rules of individual words, phrases, and parts of speech; while the latter are a set of relational rules of formal syntaxes within and beyond sentences. The synthetic syntaxes can be formally embodied by a set of relational syntactic rules for lexis, phrases, and parts of speech within and beyond sentences. A theoretical framework of formal linguistics is coherently presented by analytic and synthetic syntaxes based on contemporary denotational mathematics. The formalization of natural language syntaxes and grammars enables a wide range of applications not only in cognitive informatics, cognitive linguistics, natural language processing, cognitive computing, semantic computing, cognitive robotics, and computational linguistics in general, but also in word processing, web search engines, online text processing, machine-enabled language comprehension, autonomous machine learning, cognitive translators, computing with words, and cognitive systems in particular.

The fuzzy nature of language structures and semantics is formally studied towards a methodology for reasoning with fuzzy concepts (RFC). Mathematical models of fuzzy concepts and fuzzy semantics are introduced based on concept algebra and semantic algebra. The semantic effects of fuzzy modifiers and quantifiers on fuzzy concepts are quantitatively analyzed. Experiments on collective intension and extension elicitation for formal concepts demonstrate that fuzziness of human knowledge stem from the cognitive complexity, inexplicitness, subjectivity, diversity, redundancy, incompleteness, mixed synonyms, informal representation, incoherent attributes, divergent objects, and contextual influence. The RFC methodology provides a formal approach to computing with words (CW) for cognitive robots, deep machine learning, and fuzzy systems to rigorously manipulate fuzzy language entities, semantics, and reasoning in a wide range of applications.

Concept elicitation is a fundamental methodology for knowledge extraction and representation in cognitive robot learning. Traditional machine learning technologies deal with object identification, cluster classification, functional regression, and behavior acquisition. This paper presents a supervised machine knowledge learning methodology for concept elicitation from sample dictionaries in natural languages. Formal concepts are autonomously generated based on collective intention of attributes and collective extension of objects elicited from informal definitions in dictionaries. A system of formal concept generation for a cognitive robot is implemented by the Algorithm of Machine Concept Elicitation (AMCE) in MATLAB. Experiments on machine learning for creating a set of twenty formal concepts reveal that the cognitive robot is able to learn synergized concepts in human knowledge in order to build its own cognitive knowledge base. The results of machine-generated concepts demonstrate that the AMCE algorithm can over perform human knowledge expressions in dictionaries in terms of relevance, accuracy, quantitativeness, and cohesiveness.

It is recognized that the semantic space of knowledge is a hierarchical concept network. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic relations via machine learning based on concept algebra. The equivalence between formal concepts are analyzed by an Algorithm of Concept Equivalence Analysis (ACEA), which quantitatively determines the semantic similarity of an arbitrary pair of formal concepts. This leads to the development of the Algorithm of Relational Semantic Classification (ARSC) for hierarchically classify any given concept in the semantic space of knowledge. Experiments applying Algorithms ACEA and ARSC on 20 formal concepts are successfully conducted, which encouragingly demonstrate the deep machine understanding of semantic relations and their quantitative weights beyond human perspectives on knowledge learning and natural language processing.

The Telephone Switching Systems (TSS) is a typical real-time system that is highly complicated in design and implementation. In order to deal with the extreme complexity in real-world settings, a suitable and efficient mathematical means is required beyond any programming language. To this purpose, an efficient and precise denotational mathematics known as the Real-Time Process Algebra (RTPA) and the RTPA methodology for system modeling are introduced. Empirical experimental results are reported in this paper on the implementation of TSS based on formal models of the system in RTPA. Three phases of experiments are designed on TSS conceptual modeling, system interface design, and programming implementation and testing. All groups in the experiments with 7 to 8 members have been able to efficiently understood, design, and implement the TSS system in a simplified version in four weeks, which has been estimated as a 10+ person-year project in the industry. The efficiency and expressiveness of RTPA are empirically demonstrated base on the case studies in the experiments.

Monitoring industrial machine health in real-time is not only in high demand, it is also complicated and difficult. Possible reasons for this include: (a) access to the machines on site is sometimes impracticable, and (b) the environment in which they operate is usually not human-friendly due to pollution, noise, hazardous wastes, etc. Despite theoretically sound findings on developing intelligent solutions for machine condition-based monitoring, few commercial tools exist in the market that can be readily used. This paper examines the development of an intelligent fault recognition and monitoring system (Melvin I), which detects and diagnoses rotating machine conditions according to changes in fault frequency indicators. The signals and data are remotely collected from designated sections of machines via data acquisition cards. They are processed by a signal processor to extract characteristic vibration signals of ten key performance indicators (KPIs). A 3-layer neural network is designed to recognize and classify faults based on a pre-determined set of KPIs. The system implemented in the laboratory and applied in the field can also incorporate new experiences into the knowledge base without overwriting previous training. Results show that Melvin I is a smart tool for both system vibration analysts and industrial machine operators.

Arrays are one of the most fundamental and widely applied data structures, which are useful for modeling both logical designs and physical implementations of multi-dimensional data objects sharing the same type of homogeneous elements. However, there is a lack of a formal model of the universal array based on it any array instance can be derived. This paper studies the fundamental properties of Universal Array (UA) and presents a comprehensive design pattern. A denotational mathematics, Real-Time Process Algebra (RTPA), allows both architectural and behavioral models of UA to be rigorously designed and refined in a top-down approach. The conceptual model of UA is rigorously described by tuple- and matrix-based mathematical models. The architectural models of UA are created using RTPA architectural modeling methodologies known as the Unified Data Models (UDMs). The physical model of UA is implemented using linear list that is indexed by an offset pointer of elements. The behavioral models of UA are specified and refined by a set of Unified Process Models (UPMs). As a case study, the formal UA models are implemented in Java. This work has been applied in a number of real-time and nonreal-time systems such as compilers, a file management system, the real-time operating system (RTOS+), and the ADT library for an RTPA-based automatic code generation tool.

Files are a typical abstract data type for data objects and software modeling, which provides a standard encapsulation and access interface for manipulating large-volume information and persistent data. File management systems are an indispensable component of operating systems and real-time systems for file manipulations. This paper develops a comprehensive design pattern of files and a File Management System (FMS). A rigorous denotational mathematics, Real-Time Process Algebra (RTPA), is adopted, which allows both architectural and behavioral models of files and FMS to be rigorously designed and implemented in a top-down approach. The conceptual model, architectural model, and the static/dynamic behavioral models of files and FMS are systematically presented. This work has been applied in the design and modeling of a real-time operating system (RTOS+).

Type theories are fundamental for underpinning data object modeling and system architectural design in computing and software engineering. Abstract Data Types (ADTs) are a set of highly generic and rigorously modeled data structures in type theory. ADTs also play a key role in Object-Oriented (OO) technologies for software system design and implementation. This paper presents a formal modeling methodology for ADTs using the Real-Time Process Algebra (RTPA), which allows both architectural and behavioral models of ADTs and complex data objects. Formal architectures, static behaviors, and dynamic behaviors of a set of ADTs are comparatively studied. The architectural models of the ADTs are created using RTPA architectural modeling methodologies known as the Unified Data Models (UDMs). The static behaviors of the ADTs are specified and refined by a set of Unified Process Models (UPMs) of RTPA. The dynamic behaviors of the ADTs are modeled by process dispatching technologies of RTPA. This work has been applied in a number of real-time and non-real-time system designs such as a Real-Time Operating System (RTOS+), a Cognitive Learning Engine (CLE), and the automatic code generator based on RTPA.

Comprehension is an ability to understand the meaning of a concept or an action. Comprehension is an important intelligent power of abstract thought and reasoning of humans or intelligent systems. It is highly curious to explore the internal process of comprehension in the brain and to explain its basic mechanisms in cognitive informatics and computational intelligence. This paper presents a formal model of the cognitive process of comprehension. The mechanism and process of comprehension are systematically explained with its conceptual, mathematical, and process models based on the Layered Reference Model of the Brain (LRMB) and the Object-Attribute-Relation (OAR) model for internal knowledge representation. Contemporary denotational mathematics such as concept algebra and Real-Time Process Algebra (RTPA) are adopted in order to formally describe the comprehension process and its interaction with other cognitive processes of the brain.

Semantics is the meaning of symbols, notations, concepts, functions, and behaviors, as well as their relations that can be deduced onto a set of predefined entities and/or known concepts. Semantic computing is an emerging computational methodology that models and implements computational structures and behaviors at semantic or knowledge level beyond that of symbolic data. In semantic computing, formal semantics can be classified into the categories of to be, to have, and to do semantics. This paper presents a comprehensive survey of formal and cognitive semantics for semantic computing in the fields of computational linguistics, software science, computational intelligence, cognitive computing, and denotational mathematics. A set of novel formal semantics, such as deductive semantics, concept-algebra-based semantics, and visual semantics, is introduced that forms a theoretical and cognitive foundation for semantic computing. Applications of formal semantics in semantic computing are presented in case studies on semantic cognition of natural languages, semantic analyses of computing behaviors, behavioral semantics of human cognitive processes, and visual semantic algebra for image and visual object manipulations.

Real-Time Process Algebra (RTPA) is a denotational mathematics for the algebraic modeling and manipulations of software system architectures and behaviors by the Unified Data Models (UDMs) and Unified Process Models (UPMs). On the basis of the RTPA specification and refinement methodologies, automatic software code generation is enabled toward improving software development productivity. This paper examines designing and developing the RTPA-based software code generator (RTPA-CG) that transfers system models in RTPA architectures and behaviors into C++ or Java. A two-phrase strategy has been employed in the design of the code generator. The first phrase analyzes the lexical, syntactical, and type specifications of a software system modeled in RTPA, which results in a set of abstract syntax trees (ASTs). The second phrase translates the ASTs into C++ or Java based on predesigned mapping strategies and code generation rules. The toolkit of RTPA code generator encompasses an RTPA lexer, parser, type-checker, and a code builder. Experimental results show that system models in RTPA can be rigorously processed and corresponding C++/Java code can be automatically generated using the toolkit. The code generated is executable and effective under the support of an RTPA run-time library.

An Automated Teller Machine (ATM) is a safety-critical and real-time system that is highly complicated in design and implementation. This article presents the formal design, specification, and modeling of the ATM system using a denotational mathematics known as Real-Time Process Algebra (RTPA). The conceptual model of the ATM system is introduced as the initial requirements for the system. The architectural model of the ATM system is created using RTPA architectural modeling methodologies and refined by a set of Unified Data Models (UDMs), which share a generic mathematical model of tuples. The static behaviors of the ATM system are specified and refined by a set of Unified Process Models (UPMs) for the ATM transition processing and system supporting processes. The dynamic behaviors of the ATM system are specified and refined by process priority allocation, process deployment, and process dispatch models. Based on the formal design models of the ATM system, code can be automatically generated using the RTPA Code Generator (RTPA-CG), or be seamlessly transformed into programs by programmers. The formal models of ATM may not only serve as a formal design paradigm of real-time software systems, but also a test bench for the expressive power and modeling capability of exiting formal methods in software engineering.

A new form of denotational mathematics known as Visual Semantic Algebra (VSA) is presented for abstract visual object and architecture manipulations. A set of cognitive theories for pattern recognition is explored such as cognitive principles of visual perception and basic mechanisms of object and pattern recognition. The cognitive process of pattern recognition is rigorously modeled using VSA and Real-Time Process Algebra (RTPA), which reveals the fundamental mechanisms of natural pattern recognition by the brain. Case studies on VSA in pattern recognition are presented to demonstrate VAS’ expressive power for algebraic manipulations of visual objects. VSA can be applied not only in machinable visual and spatial reasoning, but also in computational intelligence as a powerful man-machine language for representing and manipulating visual objects and patterns. On the basis of VSA, computational intelligent systems such as robots and cognitive computers may process and inference visual and image objects rigorously and efficiently.

A Lift Dispatching System (LDS) is a typical real-time system that is highly complicated in design and implementation. This article presents the formal design, specification, and modeling of the LDS system using a denotational mathematics known as Real-Time Process Algebra (RTPA). The conceptual model of the LDS system is introduced as the initial requirements for the system. The architectural model of the LDS system is created using RTPA architectural modeling methodologies and refined by a set of Unified Data Models (UDMs). The static behaviors of the LDS system are specified and refined by a set of Unified Process Models (UPMs) for the lift dispatching and serving processes. The dynamic behaviors of the LDS system are specified and refined by process priority allocation and process deployment models. Based on the formal design models of the LDS system, code can be automatically generated using the RTPA Code Generator (RTPA-CG), or be seamlessly transferred into programs by programmers. The formal models of LDS may not only serve as a formal design paradigm of real-time software systems, but also a test bench of the expressive power and modeling capability of exiting formal methods in software engineering. SPE cial SE ction

This paper explores the architecture, theoretical foundations, and paradigms of contemporary cybernetics from perspectives of cognitive informatics (CI) and computational intelligence. The modern domain and the hierarchical behavioral model of cybernetics are elaborated at the imperative, autonomic, and cognitive layers. The CI facet of cybernetics is presented, which explains how the brain may be mimicked in cybernetics via CI and neural informatics. The computational intelligence facet is described with a generic intelligence model of cybernetics. The compatibility between natural and cybernetic intelligence is analyzed. A coherent framework of contemporary cybernetics is presented toward the development of transdisciplinary theories and applications in cybernetics, CI, and computational intelligence.

A typical real-time system, the Telephone Switching System (TSS), is a highly complicated system in design and implementation. This paper presents the formal design, specification, and modeling of the TSS system using a denotational mathematics known as Real-Time Process Algebra (RTPA). The conceptual model of the TSS system is introduced as the initial requirements for the system. Then, the architectural model of the TSS system is created using the RTPA architectural modeling methodologies and refined by a set of Unified Data Models (UDMs). The static behaviors of the TSS system are specified and refined by a set of Unified Process Models (UPMs) such as call processing and support processes. The dynamic behaviors of the TSS system are specified and refined by process priority allocation, process deployment, and process dispatching models. Based on the formal design models of the TSS system, code can be automatically generated using the RTPA Code Generator (RTPA-CG), or be seamlessly transformed into programs by programmers. The formal model of TSS may not only serve as a formal design paradigm of real-time software systems, but also a test bench of the expressive power and modeling capability of exiting formal methods in software engineering. [Article copies are available for purchase from InfoSci-on-Demand.com]

The abstract, rigorous, and expressive needs in cognitive informatics, intelligence science, software science, and knowledge science lead to new forms of mathematics collectively known as denotational mathematics. Denotational mathematics is a category of expressive mathematical structures that deals with high level mathematical entities beyond numbers and sets, such as abstract objects, complex relations, behavioral information, concepts, knowledge, processes, and systems. Denotational mathematics is usually in the form of abstract algebra that is a branch of mathematics in which a system of abstract notations is adopted to denote relations of abstract mathematical entities and their algebraic operations based on given axioms and laws. Four paradigms of denotational mathematics, known as concept algebra, system algebra, Real-Time Process Algebra (RTPA), and Visual Semantic Algebra (VSA), are introduced in this paper. Applications of denotational mathematics in cognitive informatics and computational intelligence are elaborated. Denotational mathematics is widely applicable to model and manipulate complex architectures and behaviors of both humans and intelligent systems, as well as long chains of inference processes.

Despite the fact that the origin of software agent systems has been rooted in autonomous artificial intelligence and cognitive psychology, their implementations are still based on conventional imperative computing techniques rather than autonomous computational intelligence. This paper presents a cognitive informatics perspective on autonomous agent systems (AAS’s). A hierarchical reference model of AAS’s is developed, which reveals that an autonomous agent possesses intelligent behaviors at three layers known as those of imperative, autonomic, and autonomous from the bottom up. The theoretical framework of AAS’s is described from the facets of cognitive informatics, computational intelligence, and denotational mathematics. According to Wang’s abstract intelligence theory, an autonomous software agent is supposed to be called as an intelligent-ware, shortly, an intelware, parallel to hardware and software in computing, information science, and artificial intelligence.

This paper presents a formal syntax framework of natural languages for computational linguistics. The abstract syntax of natural languages, particularly English, and their formal manipulations are described. On the basis of the abstract syntax, a universal language processing model and the deductive grammar of English are developed toward the formalization of Chomsky's universal grammar in linguistics. Comparative analyses of natural and programming languages, as well as the linguistic perception on software engineering, are discussed. A wide range of applications of the deductive grammar of English have been explored in language acquisition, comprehension, generation, and processing in cognitive informatics, computational intelligence, and cognitive computing.

The cognitive processes modeled at the metacognitive level of the layered reference mode of the brain (LRMB) encompass those of object identification, abstraction, concept establishment, search, categorization, comparison, memorization, qualification, quantification, and selection. It is recognized that all higher layer cognitive processes of the brain rely on the metacognitive processes. Each of this set of fundamental cognitive processes is formally described by a mathematical model and a process model. Real-time process algebra (RTPA) is adopted as a denotational mathematical means for rigorous modeling and describing the metacognitive processes. All cognitive models and processes are explained on the basis of the object-attribute- relation (OAR) model for internal information and knowledge representation and manipulation.

The need for new forms of mathematics to express software engineering concepts and entities has been widely recognized. Real-time process algebra (RTPA) is a denotational mathematical structure and a system modeling methodology for describing the architectures and behaviors of real-time and nonreal-time software systems. This article presents an operational semantics of RTPA, which explains how syntactic constructs in RTPA can be reduced to values on an abstract reduction machine. The operational semantics of RTPA provides a comprehensive paradigm of formal semantics that establishes an entire set of operational semantic rules of software. RTPA has been successfully applied in real-world system modeling and code generation for software systems, human cognitive processes, and intelligent systems.

Deductive semantics is a novel software semantic theory that deduces the semantics of a program in a given programming language from a unique abstract semantic function to the concrete semantics embodied by the changes of status of a finite set of variables constituting the semantic environment of the program. There is a lack of a generic semantic function and its unified mathematical model in conventional semantics, which may be used to explain a comprehensive set of programming statements and computing behaviors. This article presents a complete paradigm of formal semantics that explains how deductive semantics is applied to specify the semantics of real-time process algebra (RTPA) and how RTPA challenges conventional formal semantic theories. Deductive semantics can be applied to define abstract and concrete semantics of programming languages, formal notation systems, and large-scale software systems, to facilitate software comprehension and recognition, to support tool development, to enable semantics-based software testing and verification, and to explore the semantic complexity of software systems. Deductive semantics may greatly simplify the description and analysis of the semantics of complicated software systems specified in formal notations and implemented in programming languages.

Real-time process algebra (RTPA) is a denotational mathematical structure for denoting and manipulating system behavioral processes. RTPA is designed as a coherent algebraic system for intelligent and software system modeling, specification, refinement, and implementation. RTPA encompasses 17 metaprocesses and 17 relational process operations. RTPA can be used to describe both logical and physical models of software and intelligent systems. Logic views of system architectures and their physical platforms can be described using the same set of notations. When a system architecture is formally modeled, the static and dynamic behaviors performed on the architectural model can be specified by a three-level refinement scheme at the system, class, and object levels in a top-down approach. RTPA has been successfully applied in real-world system modeling and code generation for software systems, human cognitive processes, and intelligent systems.

Software patterns are recognized as an ideal documentation of expert knowledge in software design and development. However, its formal model and semantics have not been generalized and matured. The traditional UML specifications and related formalization efforts cannot capture the essence of generic patterns precisely, understandably, and essentially. A generic mathematical model of patterns is presented in this article using real-time process algebra (RTPA). The formal model of patterns are more readable and highly generic, which can be used as the meta model to denote any design patterns deductively, and can be translated into code in programming languages by supporting tools. This work reveals that a pattern is a highly complicated and dynamic structure for software design encapsulation, because of its complex and flexible internal associations between multiple abstract classes and instantiations. The generic model of patterns is not only applicable to existing patterns’ description and comprehension, but also useful for future patterns’ identification and formalization.

Theoretical research is predominately an inductive process; while applied research is mainly a deductive process. Both inference processes are based on the cognitive process and means of abstraction. This article describes the cognitive processes of formal inferences such as deduction, induction, abduction, and analogy. Conventional propositional arguments adopt static causal inference. This article introduces more rigorous and dynamic inference methodologies, which are modeled and described as a set of cognitive processes encompassing a series of basic inference steps. A set of mathematical models of formal inference methodologies is developed. Formal descriptions of the four forms of cognitive processes of inferences are presented using Real-Time Process Algebra (RTPA). The cognitive processes and mental mechanisms of inferences are systematically explored and rigorously modeled. Applications of abstraction and formal inferences in both the revilement of the fundamental mechanisms of the brain and the investigation of next generation cognitive computers are explored.