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Software science is a discipline that studies the formal properties and mathematical models of software, general methodologies for rigorous and efficient software development, and coherent theories and laws underpinning software behaviors and software engineering practices. This paper
presents a general mathematical model of software (GMMS). It rev...
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... 7. The composition of the architecture of a concrete digital clock system, Clock §.ArchitectureSM, is formally described according to Corollary 3 based on the conceptual model as given in Figure 2 as follows: ...
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This chapter introduces software process simulation as a means to amend and complement empirical studies, for example, to evaluate changing contexts and to analyze process dynamics. It introduces two types of simulation models, namely continuous and discrete-event models, as well as their combination in hybrid models. In addition, this chapter desc...
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... IM has led to the recent proofs of the world top-ten hardest mathematical problems known as the Goldbach Conjecture [56], [57] and the Twin-Prime Conjecture [58], [59], which have unleashed the intelligent power of AI* systems driven by IM. Paradigms of AI* systems and technologies [60], [61], [62], [63], [64], [65], [66], [67], [68] developed in the author's lab will be demonstrated, which overperform traditional pretrained or pre-programmed AI technologies based on exhaustive CPU power and/or brutal big-data training. ...
This talk presents the latest advances in theories of Complex Intelligent Systems (CIS) and basic research breakthroughs towards Autonomous AI (AI*). It is revealed that human and machine intelligence is a unique cognitive function that transforms information to actions or knowledge. The former is recognized as behavioral intelligence (Ib), while the latter is perceptive intelligence (Ip). AI* is underpinned by the framework of Intelligent Mathematics (IM) encompassing Inference Algebra, System Algebra, Concept Algebra, Semantic Algebra, Real-Time Process Algebra (RTPA), Image Frame Algebra, Visual Semantic Algebra, Big-Data Algebra, and Fuzzy Probability Algebra, etc. AI* theories have revealed the key bottleneck of, and fundamental challenges to, traditional AI technologies. IM has unleashed the intelligent power of AI* systems such as a) Real-time trustworthiness of AS, b) A patent on training-free facial image recognition, and c) Cognitive Computers/Robots beyond conventional pre-trained or pre-programmed AI systems.
... According to concept algebra [17] and sematic algebra [18] in Intelligent Mathematics (IM) [1], the semantics of natural languages expressions embodies the denotational intention and extension of language expressions at the word, phrase, sentence, paragraph, and essay levels, which are carried by syntactical entities (nouns or noun phrases), behaviors (verbs or verb phrases), and modifiers (adjectives, adverbs, etc.) [18]. The cognitive linguistic framework of cognitive computing [15,20,[33][34][35][36][37][38][39][40][41][42][43][44][45][46][47] forms a theoretical foundation for fake news recognition by machine knowledge learning [24] and differential semantic analyses [18]. Definition 1. ...
... Testing results generated by the AFNR system However, the AFNR system powered by the DSA algorithm has shown an outstanding accuracy of 70.01%. Compared to the neural network technology used by the DataCup'19 teams, the autonomous AFNR system has taken advantages enabled by the following features: a) AFNR is an autonomous methodology that is free of training underpinned by rigorous software science theories [32][33][34] and intelligent mathematics [1]; b) Running the DSA algorithm may only require minimum hardware support as described in Sect. 4.1; and c) AFNR is implemented in Python and completely transparent to developers and users. ...
It has been well understood that fake news recognition is a persistent challenge to cognitive computing and autonomous systems in general, and to Artificial Intelligence (AI), machine knowledge learning, and computational linguistics in particular. This work develops an Autonomous Fake News Recognition (AFNR) system by cognitive computing theories underpinned by Intelligent Mathematics (IM) such as concept algebra and semantic algebra. A training-free methodology and a formal algorithm for Differential Sematic Analysis (DSA) are designed in Real-Time Process Algebra (RTPA) and implemented in MATLAB. The AFNR system is implemented in the Anaconda environment with Python, the natural language toolkit (NLTK), and an English parser-Spacy. Compared to the classical data-driven neural network methodologies, AFNR and DSA have demonstrated a significant improvement against the level of accuracy over the randomly selected and large-scale benchmark of a fake news database. The DSA methodology for fake news recognition has enabled autonomous machine knowledge learning and semantic comprehension towards differential and robust semantic analyses for fake news in natural languages. The AFNR system has reached an accuracy level of 70.1%, which over performs the top ranked teams in DataCup'19 with the highest reported accuracy of 55.0%.
... Applications of C, CR, and IM will be demonstrated by pilot projects and experiments in novel intelligent computing systems [139], [140], [141], [142], [143], [144], [145], [146], [147], [148], [149], [150], [151], [152], [153], [154], [155], [156], [157], [158], [159], [160], [161], [162], [163], [164], [165], [166], [167], [168], [169], [170], [171], [172], [173], [174], [175], [176], [177], [178]. ...
Intelligence Science (IS) is an emerging discipline that studies profound mechanisms and theories of abstract intelligence and its paradigms such as natural, abstract, artificial, machinable, and computational intelligence [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21]. IS are underpinned by 3-pillars encompassing the theory of Abstract Intelligence (I) [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], Intelligent Mathematics (IM) [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], and Autonomous AI (AAI) [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83]. In the framework of IS, a) I studies the essences of abstract, natural, and artificial intelligence across the neural, cognitive, functional, and mathematical levels from the bottom up [1], [28], [41]; b) IM provides rigorous manipulations of the cognitive entities in both the brain and AAI spanning from formal concepts, semantics, knowledge, causalities, inferences, and consciousness [40], [45], [46]; and c) AAI enables a symbiotic brain-machine inferencing engine triggered by the emergence of Cognitive Computers (C) [1] for unleashing machine intelligence enabled by Brain-Inspired Systems (BIS) [84], [85], [86], [87], [88], [89], [90], [91], [92], [93], [94], [95], [96], [97], [98], [99], [100] powered by IM.
It is discovered that the generation of I in the brain or AI systems for causal induction and formal reasoning is neither preprogramed based on empirical knowledge nor pretrained from big data [101], [102], [103], [104], [105], [106], [107], [108], [109], [110], [111], [112], [113], [114], [115], [116], [117], [118], [119], [120], [121] , [122], [123], [124], [125], [126], [127], [128], [129], [130], [131]. This keynote lecture presents fundamental theories and ground-breaking technologies for designing and implementing AAI, Cognitive Robots (CR) [132], [133], [134], [135], [136], [137], [138], and autonomous intelligence generating methodologies. Applications of C, CR, and IM will be demonstrated by pilot projects and experiments in novel intelligent computing systems [139], [140], [141], [142], [143], [144], [145], [146], [147], [148], [149], [150], [151], [152], [153], [154], [155], [156], [157], [158], [159], [160], [161], [162], [163], [164], [165], [166], [167], [168], [169], [170], [171], [172], [173], [174], [175], [176], [177], [178].
... It will focus on three themes: a) The fundamental mechanisms of cognitive information representations and manipulations in the brain for cognitive computing; b) The contemporary IM theories for building the theoretical framework of IS towards autonomous and real-time intelligence generation by Cognitive Computers (κC) [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50]; and c) The architecture of C towards full AAI for unleashing machine intelligence enabled by Brain-Inspired Systems (BIS) [14], [51], [73] driven by IM. Applications of C and IM will be demonstrated by pilot projects and experiments in novel intelligent computing systems [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88], [89]. ...
Latest advances in Intelligence Science (IS) [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26] underpinned by breakthroughs in Intelligent Mathematics (IM) [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50] have enabled the convergence of contemporary information, computation, and electronic sciences and technologies. This trend has led to novel theories and disruptive technologies across the fields of Cognitive Informatics (CI) [6], [13] and Cognitive Computing (IC) [1] as fundamental theories of intelligence science and Autonomous AI (AAI) [12].
This keynote lecture presents the latest advances in electronics, information, and computing theories underpinned by cognitive informatics [5], [10], [13], cognitive computing [1], [25] and their IM foundations. It will focus on three themes: a) The fundamental mechanisms of cognitive information representations and manipulations in the brain for cognitive computing; b) The contemporary IM theories for building the theoretical framework of IS towards autonomous and real-time intelligence generation by Cognitive Computers (κC) [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50]; and c) The architecture of C towards full AAI for unleashing machine intelligence enabled by Brain- Inspired Systems (BIS) [14], [51], [73] driven by IM. Applications of C and IM will be demonstrated by pilot projects and experiments in novel intelligent computing systems [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88], [89].
Keywords — Intelligence science, Cognitive Computers (kC), Intelligent Mathematics (IM), Autonomous AI (AAI), cognitive robots, brain-inspired systems, intelligent computing, applications.
... Software is a form of abstract computational intelligence that is generated by instructive behaviors as a chain of embedded functions on executable computing structures as typed tuples (Wang, 2008a(Wang, , 2014b. The latest advances in computer science, intelligence science, and intelligent mathematics have triggered the emergence of software science (Wang, 2014b(Wang, , 2021d. ...
... Software is a form of abstract computational intelligence that is generated by instructive behaviors as a chain of embedded functions on executable computing structures as typed tuples (Wang, 2008a(Wang, , 2014b. The latest advances in computer science, intelligence science, and intelligent mathematics have triggered the emergence of software science (Wang, 2014b(Wang, , 2021d. ...
... GMMS (Wang, 2014b) reveals that software is not only an interactive dispatch structure at the top level driven by trigger, timing, and interrupt events (E), but also a set of embedded relational processes at the lower level of subsystems and components. ...
Cognitive computers (κC) are intelligent processors advanced from data and information processing to autonomous knowledge learning and intelligence generation. This study presents a retrospective and prospective review of the odyssey toward κC empowered by transdisciplinary basic research and engineering advances. A wide range of fundamental theories and innovative technologies for κC is explored, and a set of underpinning intelligent mathematics (IM) is created. The architectures of κC for cognitive computing and Autonomous Intelligence Generation (AIG) are designed as a brain-inspired cognitive engine. Applications of κC in autonomous AI (AAI) are demonstrated by pilot projects. This study reveals that AIG will no longer be a privilege restricted only to humans via the odyssey to κC toward training-free and self-inferencing computers.
... It is recognized that Cognitive Informatics (CI) [1]. [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19] is a transdisciplinary field that studies the internal information processing mechanisms of the brain [20], [21], [22], the underlying abstract intelligence (I) theories [23], [24], [25], [26], intelligent mathematics (IM) [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], and their engineering applications in cognitive computing, computational intelligence, and cognitive systems [56] [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88], [89], [90], [91], [92], [93], [94], [95], [96], [97], [98], [99], [100], [101]. Cognitive Computing (CC) [3], [4], [15], [16], [27], [28], [50] is cutting-edge paradigms of intelligent computing methodologies based on CI, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. ...
... On the basis of LRMB, the nature of intelligence may be rigorously reduced onto lower-layer cognitive objects such as data, information, and knowledge in the following subsections. According to LRMB, the theoretical bottleneck and technical challenges towards C are the lack of a fully autonomous intelligent engine at the top layer constrained by current pretrained AI [101] and preprogramed computing technologies [64]. Therefore, from brain science point of view, the next generation of Cognitive Computers (C) [4] is a brain-inspired intelligent computer for Autonomous Intelligence Generation (AIG) underpinned by an Intelligent Operating System (IOS) [20] mimicking the brain. ...
... Autonomous systems (AS) are brain-inspired systems that are advanced AI for run-time intelligent generation [80]. The ultimate goal of AS is to enable Autonomous AI (AAI) [100] that may think and act beyond traditional pre-trained and preprogrammed AI solutions [64,100]. AS enable nondeterministic behaviors at run-time closer to that of humans at the level of cognitive intelligence. ...
IEEE ICCI*CC'23: Stanford University, August 19-21, 2023.
Themes: Fundamental Challenges to Basic Research on the Theories and Methodologies of AI and Cognitive/Intelligent Computing.
• What is the next generation of intelligent computers underpinned by discoveries in intelligence science: General cognitive computers vs. pretrained AI systems? [Ref. 1: The Odyssey to Cognitive Computers.]
• May the classic preprogrammed (von Neumann) computers deal with infinitive and unpredictable real-world problems at run-time? [Ref. 2: Cognitive Computing and Autonomous AI (AAI).]
• Will the pretrained AI systems (such as Chat-GPT) be able to exhaustively cover the entire state space of machine reasoning beyond the coverage of their training models: Enumerational knowledge (empirical) vs. human knowledge (Induction/deduction-based)? [Ref. 3: What can’t Chat-GPT do?]
• Will intelligence science enable AI systems to learn human inference mechanisms rather than let them to collect infinitive factors and unstructured knowledge by so called large-language models? How are the essential forms of human intelligence and wisdom generated beyond big data and empirical learning? [Ref. 4: Intelligent Mathematics.]
... Cognitive Informatics (CI) [1]. [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19] is a transdisciplinary field that studies the internal information processing mechanisms of the brain [20], [21], [22], the underlying abstract intelligence (σI) theories [23], [24], [25], [26], intelligent mathematics (IM) [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], and their engineering applications in cognitive computing, computational intelligence, and cognitive systems [56] [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88], [89], [90], [91], [92], [93], [94], [95], [96], [97], [98], [99], [100], [101], [174], [175], [176], [177], [178], [179], [180], [181], [182], [183], [184], [185]. ...
Basic research in Cognitive Informatics (CI) and Cognitive Computing (CC) provides fundamental theories of intelligence science for Autonomous AI (AAI) and cognitive systems. The field of CI and CC has led to general AI technologies triggered by the transdisciplinary advances in brain, intelligence, computer, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary report of the plenary panel (Part II) on "Recent Advances in Cognitive Informatics and Cognitive Computing towards Autonomous Systems" in the 21st IEEE International CI/CC Conference (ICCI*CC'22). Strategic CI/CC applications are presented in cognitive systems, AAI, cognitive robots, intelligent vehicles, AI knowledge learning, autonomous intelligence generation, cognitive digital twins, and safety-and-mission-critical systems.
... Challenges to software requirement analyses and specifications by cognitive computing are multi-folds due to the increasing complexity of software, the lack of formal theories and mathematical means, and the difficulty for rigorously modeling software system specifications. A rigorous methodology for dealing with system architecture, structural models, and behavioral models of software systems is rooted in software science theories [2], [3], [26] across entire software engineering processes. This approach may lead to a wide range of breakthroughs in basic research in software science [1], Intelligent Mathematics (IM) [4], [5], [27], [28], [29], [30], [33], formal models of software, and intelligent tools for transferring these theories to industrial practice in software engineering. ...
... In the cognitive computing approach to software engineering powered by IMs, system specifications are formally modeled in two dimensions known as the sets of structural and functional models [2]. Then, any dynamic software system is formally embodied by interactions between the functional and structural dimensions [1], [2], [26]. ...
... In the cognitive computing approach to software engineering powered by IMs, system specifications are formally modeled in two dimensions known as the sets of structural and functional models [2]. Then, any dynamic software system is formally embodied by interactions between the functional and structural dimensions [1], [2], [26]. This generic software system specification methodology in software science enables any software system S| § be modeled by a tailored Cartesian product of the sets of Process Models (PMs) and Structure Models (SMs) [2], [15]. ...
Autonomous software requirement analysis and specifications is not only an ultimate goal of cognitive computing, but also a persistent challenge to theories and technologies of software engineering. A cognitive computing model is demanded to autonomously elicit and rigorously refine software requirements in order to generate a set of formal specifications. This paper presents a novel methodology for the design of a cognitive computing method for Software Requirement Elicitation and Specifications (SRES) based on the latest advances in software science and intelligent mathematics. SRES is implemented as an interactive system for capturing software requirements and generating formal specifications. The SRES methodology and experiments are demonstrated for solving real-world and complex software engineering problems enabled by cognitive computing theories underpinned by intelligent mathematics.
... The cognitive objects in forms of data, information, knowledge, and intelligence are a result of abstraction as a gifted ability of human brain [1], [2], [4], [7], [8], [10], [11], [17], [18], [23], [25], [26], [29], [42], [48], [53], [57], [58], [60], [61], [65], [67], [68], [71], [72], [75], [76], [78], [83], [86], [96], [101], [107], [108]. Abstraction is a basic cognitive process of the brain for generating hierarchical knowledge according to the Layered Reference Model of the Brain (LRMB) [45], [46], [49], [68], [80], [85], [86], [90], [91], [92]. ...
... Abstraction is a basic cognitive process of the brain for generating hierarchical knowledge according to the Layered Reference Model of the Brain (LRMB) [45], [46], [49], [68], [80], [85], [86], [90], [91], [92]. Abstraction is not only a cognitive process of knowledge acquisition and learning, but also a powerful means of philosophy and mathematics [44], [53], [58], [75], [76], [78], [98]. Abstraction plays a centric role in human cognition, thinking, and reasoning [118], [124], because all types of cognitive objects represented in the brain are in abstract forms. ...
... Mathematics is indispensable means of abstract science of numbers, quantity, space, and time, as well as their applications in all other disciplines of sciences, engineering, society, and humanities. In order to efficiently and rigorously deal with complex problems in abstract intelligence, brain science, cognitive informatics, knowledge science, and system science, a set of contemporary mathematics has been developed collectively known as Intelligent Mathematics (IM) [12], [32], [35], [37], [44], [48], [52], [54], [55], [57], [60], [64], [66], [69], [70], [73], [76], [79], [81], [82], [83], [85], [87], [93], [94], [95], [111], [114], [125], [126], [127], [128], [129], which indicated that human scientifical knowledge has been mainly archived in mathematical forms. ...
The emergence of abstract sciences as a counterpart of classic concrete sciences is presented in this work. The framework of abstract sciences encompasses data, information, knowledge, and intelligence sciences from the bottom up. It is found that intelligence is the ultimate level of cognitive objects generated in human brains aggregated from data (sensory), information (cognition), and knowledge (comprehension). However, there is a lack of rigorous studies and coherent theories towards the theoretical framework of abstract sciences as the counterpart of classical concrete sciences. This paper explores the cognitive and mathematical models of abstract mental objects in the brain. The taxonomy and cognitive foundations of them are explored. A set of mathematical models of data, information, knowledge, and intelligence is formally created in intelligent mathematics. Based on the cognitive and mathematical models of the cognitive objects, formal properties and relationship of contemporary data, information, knowledge, and intelligence sciences are rigorously explained.
... It is recognized that Cognitive Informatics (CI) [1]. [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19] is a transdisciplinary field that studies the internal information processing mechanisms of the brain [20], [21], [22], the underlying abstract intelligence (I) theories [23], [24], [25], [26], intelligent mathematics (IM) [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], and their engineering applications in cognitive computing, computational intelligence, and cognitive systems [56] [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88], [89], [90], [91], [92], [93], [94], [95], [96], [97], [98], [99], [100], [101]. Cognitive Computing (CC) [3], [4], [15], [16], [27], [28], [50] is cutting-edge paradigms of intelligent computing methodologies based on CI, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. ...
... On the basis of LRMB, the nature of intelligence may be rigorously reduced onto lower-layer cognitive objects such as data, information, and knowledge in the following subsections. According to LRMB, the theoretical bottleneck and technical challenges towards C are the lack of a fully autonomous intelligent engine at the top layer constrained by current pretrained AI [101] and preprogramed computing technologies [64]. Therefore, from brain science point of view, the next generation of Cognitive Computers (C) [4] is a brain-inspired intelligent computer for Autonomous Intelligence Generation (AIG) underpinned by an Intelligent Operating System (IOS) [20] mimicking the brain. ...
... Autonomous systems (AS) are brain-inspired systems that are advanced AI for run-time intelligent generation [80]. The ultimate goal of AS is to enable Autonomous AI (AAI) [100] that may think and act beyond traditional pre-trained and preprogrammed AI solutions [64,100]. AS enable nondeterministic behaviors at run-time closer to that of humans at the level of cognitive intelligence. ...
A presentation of the front-matters and keynotes of 2022 IEEE 21st International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC 2022).
