46137989
On information and knowledge representation in the brain
The cognitive models of information representation and the capacity of human memory are fundamental research areas in cognitive informatics, which help to reveal the mechanism and potential of the brain. This paper develops the object-attribute-relation (OAR) model for describing information representation and storage in the brain. According to the OAR model, the human memory and knowledge are represented by relations, i.e. connections of synapses between neurons, rather than by the neurons themselves as the traditional container metaphor described. Based on the OAR model, the memory capacity of the human brain is calculated as in the order of 10/sup 8432/ bits. The determination of the magnitude of human memory capacity is not only theoretically significant in cognitive informatics, but also practically useful to estimate the human potential, as well as the gap between the natural and machine intelligence.
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.
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.
21455681
A layered reference model of the brain
Based on the advances of research in cognitive informatics and related fields, this paper attempts to develop a layered reference model of the brain that explains the functional mechanisms and cognitive processes of the natural intelligence. A variety of life functions and cognitive processes have been identified in cognitive informatics, cognitive science, neuropsychology, and neurophilosophy. In order to formally and rigorously describe a comprehensive and coherent set of mental processes and their relationships, an integrated reference model of the brain is established, which encompasses 37 cognitive processes at six layers known as the sensation, memory, perception, action, meta and higher cognitive layers from bottom up. The reference model of the brain may be applied to explain a wide range of physiological, psychological, and cognitive phenomena in cognitive informatics, particularly relationships between the inherited and the acquired life functions, as well as the subconscious and conscious cognitive processes.
15352869
Error Resilient Video Coding using Redundant Pictures
Coding of redundant pictures is supported in the latest international video coding standard H.264 (also known as MPEG-4 part 10 or AVC). This paper proposes a standard-compliant way to encode and decode redundant pictures for improved error resilience. The method is based on a combination of picture-level reference picture selection, reference picture list ordering, and a hierarchical allocation of redundant pictures, which can efficiently prevent temporal error propagation without relying on feedback information. Simulation results show that the method outperforms the optimal loss-aware rate-distortion optimized intra refresh method. The proposed algorithm has been adopted into the H.264 joint model.
10936975
Quantifying Evolving Processes in Multimodal 3D Medical Images
Quantitative measurements of changes in evolving brain pathology, such as multiple sclerosis lesions and brain tumors, are important for clinicians to perform pertinent diagnoses and to help in patient follow-up. Lesions or tumors can vary over time in size, shape, location and composition because of natural pathological processes or the effect of a drug treatment or therapy. In the past, people have used as a quantitative measurement the change in total or regional lesion/tumor volume. In this paper we propose a new model to quantify changes in evolving processes in multimodal 3D medical images. We believe this model reflects changes in pathology more accurately because it simultaneously takes into account information in multiple imaging modalities and the location of lesion/tumor voxels. We demonstrate the effectiveness of this model with experiments on synthetic lesion data.
215142
A Study of Internet Packet Reordering
Packet reordering is a well-known phenomenon that the order of packets is inverted in the Internet. Previous studies indicates reordering can affect the performance of both the network and the packets receiver. Nevertheless, they get different results about the prevalence of reordering in the Internet. In this paper, we firstly present a methodology for single-point reordering measurement at a TCP receiver, including the algorithm and its implementation. Then we show the results of our three-week observation of reordering from a set of 10,647 Internet Web sites in China. In addition, we discuss a method to distinguish reordering and loss by making use of the distribution of their time lag and packet lag. Finally, we study the pertinence of sites experiencing reordering according to the network topology and propose a novel and relatively reliable approach to infer reorder-generating spots in the Internet.
8204880
Privacy aware data generation for testing database applications
Testing of database applications is of great importance. A significant issue in database application testing consists in the availability of representative data. In this paper, we investigate the problem of generating a synthetic database based on a-priori knowledge about a production database. Our approach is to fit general location model using various characteristics (e.g., constraints, statistics, rules) extracted from the production database and then generate the synthetic data using model learnt. The generated data is valid and similar to real data in terms of statistical distribution, hence it can be used for functional and performance testing. As characteristics extracted may contain information which may be used by attacker to derive some confidential information about individuals, we present our disclosure analysis method which applies cell suppression technique for identity disclosure analysis and perturbation for value disclosure.
14934450
Combining Statistical and Knowledge-Based Spoken Language Understanding in Conditional Models
Spoken Language Understanding (SLU) addresses the problem of extracting semantic meaning conveyed in an utterance. The traditional knowledge-based approach to this problem is very expensive --- it requires joint expertise in natural language processing and speech recognition, and best practices in language engineering for every new domain. On the other hand, a statistical learning approach needs a large amount of annotated data for model training, which is seldom available in practical applications outside of large research labs. A generative HMM/CFG composite model, which integrates easy-to-obtain domain knowledge into a data-driven statistical learning framework, has previously been introduced to reduce data requirement. The major contribution of this paper is the investigation of integrating prior knowledge and statistical learning in a conditional model framework. We also study and compare conditional random fields (CRFs) with perceptron learning for SLU. Experimental results show that the conditional models achieve more than 20% relative reduction in slot error rate over the HMM/CFG model, which had already achieved an SLU accuracy at the same level as the best results reported on the ATIS data.