16244688
Formal Description of Time Management in Real-Time Operating Systems
This paper describes the formal specification of the time management subsystem of a real-time operating system. Real-time process algebra (RTPA) is adopted to formally specify the system. The architecture, static behaviors, and dynamic behaviors of a CPU time manager are systematically specified that form an abstract model of the system. The formal specifications are implemented in C, on which the system performance can be tested and verified
21184887
The cognitive processes of abstraction and formal inferences
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 paper describes the cognitive processes of abstraction and formal inferences such as deduction, induction, abduction, and analogy. The hierarchy of abstraction and the descriptivity of abstract means at different levels are analyzed. A set of mathematical models of formal inference methodologies are developed. Formal descriptions of the five cognitive processes of abstraction and inferences are presented using real-time process algebra (RTPA). Applications of abstraction and formal inferences in dealing with complicated problems in large-scale software system development in software engineering are discussed.
40360434
Measurement of the cognitive functional complexity of software
One of the central problems in software engineering is its inherited complexity. It is recognized that cognitive informatics plays an important role in understanding the fundamental characteristics of software. This paper models the cognitive weights of basic control structures of software, and develops a new concept of cognitive functional size for measuring software complexity. Comparative case studies between the cognitive functional size and physical sizes of 20 programs are conducted. It is found that for a given design, although the physical sizes of software may vary in a wide range, the cognitive functional sizes are much stable and independent from implementation technologies and programming languages. Therefore, the cognitive functional size provides a novel, stable and practical software complexity measurement and is helpful to explain the fundamental nature of software complexity in the phase of design, implementation, or maintenance in software engineering.
44372041
Software Engineering Process Benchmarking
This paper proposes a Software Engineering Process (SEP) benchmarking methodology and a benchmark-gap analysis technique to assist industrial practitioners in quantitative software engineering. This work adopts the comprehensive SEP Assessment model (SEPRM), as a foundation to SEP benchmarking. In this approach, a number of conventional benchmarking challenges may be overcome. Case studies are presented to demonstrate the usage of the benchmarking technologies and supporting tools.
7087864
Formal description of the cognitive process of decision making
Decision making is one of the basic cognitive processes of human behaviors by which a preferred option or a course of actions is chosen from among a set of alternatives based on certain criteria. Decision theories are widely applied in a number of disciplines encompassing cognitive science, computer science, management science, economics, sociology, psychology, political science, and statistics. The studies on decision making can be categorized into two classes: descriptive and normative theories. A number of decision strategies have been proposed from different angles and application domains such as the maximum expected utility and Bayesian method. However, there is still a lack of a fundamental and mathematical decision model and a rigorous cognitive process for decision making. This paper presents a decision making process on the basis of the layered reference model of the brain (LRMB). The cognitive process of decision making is modeled as a sequence of Cartesian-product based selections. A rigorous description of the decision process in real-time process algebra (RTPA) is presented. Different decision making strategies are comparatively analyzed. The result shows these strategies can be well fit in the formally described decision process. The cognitive process of decision making may be applied in a wide range of decision-based systems, such as cognitive informatics, software agent systems, expert systems, and decision support systems.
29450095
Cognitive models of the brain
The human brain is the most complicated organ in the universe and a new frontier yet to be explored in an interdisciplinary approach. Investigation of the brain is a unique problem that requires recursive mental power to explore the brain using the brain. This paper attempts to develop functional 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 natural intelligence. Structures of memories are explored and cognitive models of the natural intelligence 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 sleep in long-term memory development, are investigated. The models and theories are applied to explain a number of fundamental physiological and psychological phenomena.
14524485
Boundary finding with correspondence using statistical shape models
We propose an approach for boundary finding where the correspondence of a subset of boundary points to a model is simultaneously determined. Global shape parameters derived from the statistical variation of object boundary points in a training set are used to model the object. A Bayesian formulation, based on this prior knowledge and the edge information of the input image, is employed to find the object boundary with its subset points in correspondence with boundaries in the training set or the mean boundary. We compared the use of a generic smoothness prior and a uniform independent prior with the training set prior in order to demonstrate the power of this statistical information. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approach, including the validation of the dependence of the method on image quality, different initialization and prior information.
1917143
Wireless Sensing, Actuation and Control - With Applications to Civil Structures
Structural monitoring and control have been subjects of interests in structural engineering for quite some time. Structural sensing and control technologies can benefit in terms of installation cost and time from wireless communication and embedded computing. The hardware and software requirements pose an interesting, interdisciplinary research challenge. This paper describes a low-cost wireless sensing system that is judiciously designed for large-scale applications in civil structures. Laboratory and field tests have been conducted to validate the performance of the prototype system for measuring vibration responses. By incorporating an actuation signal generation interface, the wireless sensing system has the capabilities to perform structural actuation and support structural control applications. Structural control tests have been performed to validate the wireless sensing and actuation system.
6558099
A Generative Method for Textured Motion: Analysis and Synthesis
Natural scenes contain rich stochastic motion patterns which are characterized by the movement of a large number of small elements, such as falling snow, raining, flying birds, firework and waterfall. In this paper, we call these motion patterns textured motion and present a generative method that combines statistical models and algorithms from both texture and motion analysis. The generative method includes the following three aspects. 1). Photometrically, an image is represented as a superposition of linear bases in atomic decomposition using an overcomplete dictionary, such as Gabor or Laplacian. Such base representation is known to be generic for natural images, and it is low dimensional as the number of bases is often 100 times smaller than the number of pixels. 2). Geometrically, each moving element (called moveton), such as the individual snowflake and bird, is represented by a deformable template which is a group of several spatially adjacent bases. Such templates are learned through clustering. 3). Dynamically, the movetons are tracked through the image sequence by a stochastic algorithm maximizing a posterior probability. A classic second order Markov chain model is adopted for the motion dynamics. The sources and sinks of the movetons are modeled by birth and death maps. We adopt an EM-like stochastic gradient algorithm for inference of the hidden variables: bases, movetons, birth/death maps, parameters of the dynamics. The learned models are also verified through synthesizing random textured motion sequences which bear similar visual appearance with the observed sequences.
12928468
Distributed goal recognition algorithms for modular robots
Modular robots are systems composed of a number of independent units that can be reconfigured to fit the task at hand. When the modules are computationally independent, they form a large distributed system with no central controller. We are concerned with the ability of such modular robots to easily recognize the achievement (or lack thereof) of a given goal configuration. We present algorithms for a class of 2D and 3D modular robots, along with correctness and running time analysis. We have successfully implemented the 2D algorithm on the second-generation Crystalline Atomic robot, a self-reconfigurable modular robot under development in our laboratory and we present implementation details and experimental results.
16439255
Privacy Aware Market Basket Data Set Generation: A Feasible Approach for Inverse Frequent Set Mining
Association rule mining has received a lot of attention in the data mining community and several algorithms were proposed to improve the performance of association rule or frequent itemset mining. The IBM Almaden synthetic data generator has been commonly used for performance evaluation. One recent work shows that the data generated is not good enough for benchmarking as it has very different characteristics from real-world data sets. Hence there is a great need to use real-world data sets as benchmarks. However, organizations hesitate to provide their data due to privacy concerns. Recent work on privacy preserving association rule mining addresses this issue by modifying real data sets to hide sensitive or private rules. However, modifying individual values in real data may impact on other, non-sensitive rules. In this paper, we propose a feasible solution to the NPcomplete problem of inverse frequent set mining. Since solving this problem by linear programming techniques is very computationally prohibitive, we apply graph-theoretical results to divide the original itemsets into components that preserve maximum likelihood estimation. We then use iterative proportional fitting method to each component. The technique is experimentally evaluated with two real data sets and one synthetic data set. The results show that our approach is effective and efficient for reconstructing market basket data set from a given set of frequent itemsets while preserving sensitive information.