Recently, as a consequence of the coronavirus disease (COVID-19) pandemic, dependence on contact tracing (CT) models has significantly increased to prevent the spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors, there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. This article addresses this gap and proposes the trustworthy blockchain-enabled system for an indoor CT (TB-ICT) framework. The TB-ICT framework is proposed to protect privacy and integrity of the underlying CT data from unauthorized access. More specifically, it is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof-of-Work (dPoW) credit-based consensus algorithm coupled with randomized hash window (W-Hash) and dynamic Proof-of-Credit (dPoC) mechanisms to differentiate between honest and dishonest nodes. The TB-ICT not only provides a decentralization in data replication but also quantifies the node’s behavior based on its underlying credit-based mechanism. For achieving a high localization performance, we capitalize on the availability of Internet of Things (IoT) indoor localization infrastructures, and develop a data-driven localization model based on bluetooth low-energy (BLE) sensor measurements. The simulation results show that the proposed TB-ICT prevents the COVID-19 from spreading by the implementation of a highly accurate CT model while improving the users’ privacy and security.

Advances in the frontier of intelligence and system sciences have triggered the emergence of Autonomous AI (AAI) systems. AAI is cognitive intelligent systems that enable non-programmed and non-pretrained inferential intelligence for autonomous intelligence generation by machines. Basic research challenges to AAI are rooted in their transdisciplinary nature and trustworthiness among interactions of human and machine intelligence in a coherent framework. This work presents a theory and a methodology for AAI trustworthiness and its quantitative measurement in real-time context based on basic research in autonomous systems and symbiotic human-robot coordination. Experimental results have demonstrated the novelty of the methodology and effectiveness of real-time applications in hybrid intelligence systems involving humans, robots, and their interactions in distributed, adaptive, and cognitive AI systems.

Neurorehabilitation is an emerging and transdisciplinary field that is not only highly demanded in medical science and healthcare, but also a challenging problem in cognitive neurology, neurosignaling theory, neuroinformatics, and brain science. This paper presents findings in basic research on neuroinformatics towards neurorehabilitation by exploring its cognitive and mathematical foundations. It introduces novel theories for neurorehabilitation encompassing neural spike frequency modulation, basic neural circuits, and neurosignaling pathways. It develops diagnostic and assessment technologies for clinic neurorehabilitation such as consciousness status and cognitive memory recovery.

Autonomous decision-making is a central challenge to theories and technologies of AI and computational intelligence. It could not be implemented because the inexhaustive, indeterministic, and uncertain bottlenecks in traditional machine inference theories. This paper presents a formal study on mathematical models of decision and decision-making. Generic mathematical models of formal decisions are created for qualitative, quantitative, and serial decision-making. Cognitive algorithms are developed to extend traditional intuitive, empirical, or heuristic decisions to machine-learnt inferences for decision making. Experiments have demonstrated the power of cognitive decision-making algorithms and technologies for autonomous systems and computational intelligence.

It is recognized that system trustworthiness is a hyperstructure embodied by the structural, behavioral, and system dimensions with a set of coherent attributes. We explore a theoretical framework of tripartite trustworthiness that can be applied to real-world autonomous systems. We present a formal study of the essences and mathematical models of system trustworthiness and their quantitative measurements in the contexts of autonomous and mission-critical intelligent systems where humans and machines interact in a hybrid environment.

One of the key challenges to AI is autonomous Decision Making (DM). This paper strives towards a formal approach to model cognitive DM in order to enable AI machines to conduct autonomous DM. On the basis of the theoretical models for rational decisions and rigorous DM developed in our Lab, we implement a set of algorithms for autonomously handling primitive, single, and serial decisions rigorously. An autonomous DM supporting tool is designed and implemented based on the algorithms. Experiments on various decision problems have demonstrated the capability of the tool and the methodology in many cases beyond those of human DM outcomes in both complexity and speed.

One of the fundamental problems in neurology and neuroinformatics is whether the neural signals in human nerves systems are digital or analogue. This paper presents a novel neural signaling theory of Spike Frequency Modulation (SFM), which explains the nature of neural signals and their transformation in the nervous systems of the brain. Mathematical models of the unified signals of neural spikes across the sensory, associate and motor neurons are formally described. The time-divided mechanism for neural signal transmission and the space-divided mechanism for neural semantic representation in human nervous systems are rigorously explained. A set of experimental simulations demonstrates the SFM theory and the cognitive mechanisms of the neural pathways and networks. The SFM theory reveals the neurological and cognitive foundations of both natural and artificial neural networks for brain-inspired systems and engineering applications.

It is found that neural circuits are fundamental structures of neural networks and the nervous systems in neurology and neuroinformatics. This paper presents a novel neural circuit theory for analytic neurology and formal neuroinformatics. Three categories of neurons known as the association, sensory, and motor nerves are formally modeled. A set of functional structures of neural circuits are identified including serial, convergent, divergent, parallel, and positive/negative-feedback neural circuits. Applying the basic neural circuits, complex neural networks of the brain can be rigorously modeled, which explain the formation of temporal thinking threads, permanent memory, and knowledge representations in the brain and their interfaces to machines and neurocomputing systems.

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.

Brain-Inspired Systems (BIS) are an emerging field of brain and intelligence sciences that studies natural intelligence models of AI and cognitive systems in one direction, and the formal models of the brain simulated by computational intelligence in another direction. A typical BIS is the cognitive robots that mimic and implement the brain through all cognitive levels. BIS provides insights for brain-machine interfaces (BMI), which may lead to novel man-machine interactions and hybrid intelligent systems. BIS may also advance classic computers from dada processors to the next generation of knowledge processors mimicking the brain. BIS will underpin a wide range of engineering paradigms such as cognitive systems, cognitive computers, cognitive robots, machine learning systems, semantic comprehension systems, big data systems, unmanned systems, self-driving vehicles and hybrid man-machine systems.

It is recognized that systems are the most complicated entities in abstract, physical, information, cognitive, brain, and social worlds across a wide range of science and engineering disciplines. This paper presents a theory of distributed system topologies and adaptive fusion mechanisms. It reveals that structural and functional relations play a centric role in system behavior modeling. A special phenomenon of system incremental fusion is discovered and formally described in order to explain the adaptive mechanisms of system gains. The hierarchical topology theory of systems provides a methodology for efficiently handling distributed structural and behavioral complexities in system representation, modeling, analysis, synthesis, inference, and implementation. Applications of the formal theories of distributed system topology and adaptive hierarchical relations are explored in distributed, adaptive and cognitive system engineering.

One of the fundamental means for interaction and coordination between humans and cognitive robots is knowledge sharing in general and formal concept comprehension in particular. A cognitive knowledge base (CKB) is introduced as a formal structure of collective knowledge embodied by a weighted hierarchical concept network. This paper formally describes a CKB based on concept algebra and semantic algebra. An Algorithm of CKB Generation (ACKBG) is developed for autonomous machine learning from complex human knowledge and semantic expressions. A set of experiments demonstrates applications of ACKBG in brain-machine interfacing via a sharable CKB in cognitive computing, semantic computing, and machine learning.

Big data are extremely large-scaled data in terms of quantity, complexity, semantics, distribution, and processing costs in computer science, cognitive informatics, web-based computing, cloud computing, and computational intelligence. Censuses and elections are a typical paradigm of big data engineering in modern digital democracy and social networks. This paper analyzes the mechanisms of voting systems and collective opinions using big data analysis technologies. A set of numerical and fuzzy models for collective opinion analyses is presented for applications in social networks, online voting, and general elections. A fundamental insight on the collective opinion equilibrium is revealed among electoral distributions and in voting systems. Fuzzy analysis methods for collective opinions are rigorously developed and applied in poll data mining, collective opinion determination, and quantitative electoral data processing.

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.

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.

Many fundamental theories and doctrines of micro- and macro-economics are not formally studied in economics. This paper presents a rigorous treatment and explanation of a set of fundamental empirical theories of economics. It is recognized that the adaptive equilibrium of market systems is rooted in the negative feedbackmechanisms of demands and supplies. A mathematical model of economic equilibrium developed in this paper provides a formal proof of Adam Smith's hypothesis of the 'invisible hand'. An economic equilibrium theorem is derived to deal with more complicated multivariable equilibrium problems that could not be handled in conventional economic theories. Then, a set of mathematical models is developed for dynamic cost analyses and the estimation of economical outcomes of engineering projects.

Cognitive informatics (CI) is the transdisciplinary enquiry of cognitive and information sciences that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, and their engineering applications via an interdisciplinary approach. CI develops a coherent set of fundamental theories and denotational mathematics, which form the foundation for most information and knowledge based science and engineering disciplines such as computer science, cognitive science, neuropsychology, systems science, cybernetics, software engineering, knowledge engineering, and computational intelligence. This paper reviews the central doctrine of CI and its applications. The theoretical framework of CI is described on the architecture of CI and its denotational mathematic means. A set of theories and formal models of CI is presented in order to explore the natural and computational intelligence. A wide range of applications of CI are described in the areas of cognitive computers, cognitive properties of knowledge, simulations of human cognitive behaviors, cognitive complexity of software, autonomous agent systems, and computational intelligence.

Cognitive Informatics is a cutting-edge and multidisciplinary research area that tackles the fundamental problems shared by modern informatics, computing, software engineering, AI, cybernetics, cognitive science, neuropsychology, medical science, systems science, philosophy, linguistics, economics, management science, and life sciences. This editorial introduces the emerging field of cognitive informatics and its applications in cognitive computing, abstract intelligence, computational mathematics, and computational intelligence. The themes and structure of this special issue on cognitive informatics are described, and then, focuses of the selected papers in this special issue are highlighted.

Cognitive Informatics is a cutting-edge and multidisciplinary research area that tackles the fundamental problems shared by modern informatics, computing, software engineering, AI, cybernetics, cognitive science, neuropsychology, medical science, systems science, philosophy, linguistics, economics, management science, and life sciences. This editorial introduces the emerging field of cognitive informatics and its applications in cognitive computing, abstract intelligence, computational mathematics, and computational intelligence. The themes and structure of this special issue on cognitive informatics are described, and then, focuses of the selected papers in Part II of this special issue are highlighted.

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 fundamental finding in computer science is that software, an artifact of human creativity, is not constrained by the laws and properties known in the physical world. Thus, a natural question we have to ask is "what are the constraints that software obeys?" This paper attempts to demonstrate that software obeys the laws of informatics and mathematics. This paper explores a comprehensive set of informatics and semantic properties and laws of software as well as their mathematical models. In order to provide a rigorous mathematical treatment of both the abstract and concrete semantics of software, a new type of formal semantics known as the deductive semantics is developed. The deductive models of semantics, semantic function, and semantic environment at various composing levels of programs are formally described. The findings of this paper can be applied to perceive the basic characteristics of software and the development of fundamental theories that deal with the informatics and semantic properties of software.

The human brain is the most complicated organ in the universe and a new frontier yet to be explored by an interdisciplinary approach. This paper attempts to develop logical and cognitive models of the brain by using cognitive informatics and formal methodologies. This paper adopts a memory-based approach to explore the brain and to demonstrate that memory is the foundation for any kind of natural or artificial intelligence. Logical structures of memories are explored, and cognitive models of the brain are proposed. Cognitive mechanisms of the brain, including hypotheses and theories on the thinking engine of the brain, long-term memory establishment, and roles of perceptive eye movement, and sleep in long-term memory development, are investigated. The models and theories can be applied to explain a wide range of fundamental phenomena in psychology, cognitive science, physiology, computing, and neural science.

A variety of life functions and cognitive processes (CPs) have been identified in cognitive informatics, psychology, cognitive science, and neurophilosophy. This paper attempts to develop a layered reference model of the brain (LRMB) that formally and rigorously explains the functional mechanisms and CPs of natural intelligence. A comprehensive and coherent set of mental processes and their relationships is identified in LRMB that encompasses 37 CPs at six layers known as the sensation, memory, perception, action, metacognitive, and higher cognitive layers from the bottom-up. The LRMB reference model provides an integrated framework for modeling the brain and the mind. LRMB also enables future extension and refinement of the CPs within the same hierarchical framework. LRMB can be applied to explain a wide range of physiological, psychological, and cognitive phenomena in cognitive informatics, particularly the relationships and interactions between the inherited and the acquired life functions, as well as those of the subconscious and conscious CPs.

A survey has been designed to seek the practical foundation of base process activities (BPAs) in the software industry and to support research in modelling the software engineering processes. A superset of BPAs compatible with the current software process models, such as SPICE (ISO 15504), CMM, ISO 9000 and BOOTSTRAP, were identified for the construction of the questionnaires. This paper reports the survey findings on BPAs in software engineering processes. A summary of the current software engineering process techniques and practices modelled by 83 BPAs in 10 processes and three categories is given. Each BPA is benchmarked on attributes of mean importance and ratios of significance, practice and effectiveness. Based on the benchmarks, and by comparing with the current practice of the reader's organization, recommendations can be given on which specific areas need to have processes established first, and which areas should be highest priority for process improvement.