A Cognitive Science Framework for the Analysis of Knowledge-Based Systems

The paper presents a cognitive science framework for the analysis of knowledge-based systems,including people, media. simulation and expert systems, resulting in a practical model for the procedures ofknowledge engineering. Starting with the construct of a social organization model driven by anticipationand thed differentiating this into pesonal scientists with diverse relations to people and their internal andexternal communication, it provides powerful and general model of society. people, and the roles of peoplein society. This model extends naturally ic the role of conventional media in the knowledge processes ofsociety and the new roles of computer-based simulation and expert systems. In particular it provides amodel of knowledge transfer that enables the processes of knowledge engineering to be analyzed andautomated.

Research on Cognitive Cooperation in Multi-Agent Systems

The accomplishment of a complex problem usually involves cooperation between participators with different knowledge background concerned. This paper identifies interdependency between different sub problems （through problem decomposition） as the major factor that influences cooperative relations in multi-Agent systems, based on which we propose an efficient means to measure cooperation coefficient （degree） between different Agents. Then cognitive cooperation between Agents is analyzed which aims at collecting the wisdom of the cognitive community for a systematic solution to the overall problem.

Relay Selection and Power Allocation in Amplify-and-Forward Cognitive Radio Systems Based on Spectrum Sharing

In this paper, we consider a spectrum sharing scheme that is a joint optimization of relay selection and power allocation at the secondary transmitter, which aims to achieve the maximum possible throughput for the secondary user. This paper considers the scenario where the primary user is incapable of supporting its target signal-to-noise ratio (SNR). More especially, the secondary transmitter tries to assist the primary user with achieving its target SNR by cooperative amplify-and-forward (AF) relaying with two-phase. By exhaustive search for all candidate secondary transmitters, an optimal secondary transmitter can be selected, which not only can satisfy the primary user’s target SNR, but also maximize the secondary user’s throughput. The optimal secondary transmitter acts as a relay for the primary user by allocating a part of its power to amplify-and-forward the primary signal over the primary user’s licensed spectrum bands. At the same time, as a reward, the optimal secondary transmitter uses the remaining power to transmit its own signal over the remaining licensed spectrum bands. Thus, the secondary user obtains the spectrum access opportunities. Besides, there is no interference between the primary user and the secondary user. We study the joint optimization of relay selection and power allocation such that the secondary user’s throughput is maximized on the condition that it satisfies the primary user’s target SNR. From the simulation, it is shown that the joint optimization of relay selection and power allocation provides a significant throughput gain compared with random relay selection with optimal power allocation (OPA) and random relay selection with water-filling power allocation (WPA). Moreover, the simulation results also shown that our spectrum sharing scheme obtains the win-win solution for the primary system and the secondary system.

Improving Audit Judgment and Decision Making With Dual Systems Cognitive Model

This article proposes a dual system cognitive reasoning model to enhance audit judgment and decision making. The model is built on the fact that the total cognitive capacity of an individual comprises two systems of reasoning--System 1 that is unconscious, associative, implicit, more emotional and less controlled, and so forth; and System 2 that is conscious, explicit, deliberate and rule-governed, and so forth. The benefits of the proposed model that integrates these two complementary and compensatory systems are first illustrated with an example in audit planning, and second explained how the model could overcome the deficiencies of heuristics specifically in an audit context.

A Fuzzy Expert System Architecture for Intelligent Tutoring Systems:A Cognitive Mapping Approach

An Intelligent Tutoring System (ITS) is a computer based instruction tool that attempts to provide individualized instructions based on learner’s educational status. Advances in development of these systems have rose and fell since their emergence. Perhaps the main reason for this is the absence of appropriate framework for ITS development. This paper proposes a framework for designing two main parts of ITSs. Besides development framework, the second main reason for lack of significant advances in ITS development is its development cost. In general, this cost for instructional material is quite high and it becomes more in ITS development. The proposed method can significantly reduce the development cost. The cost reduction mainly is because of characteristics of applied mapping techniques. These maps are human readable and easily understandable by people who are not aware of knowledge representation techniques. The proposed framework is implemented for a graduate course at a technical university in Asia. This experiment provides an individualized instruction which is the main designing purpose of the ITSs.

A New Control Strategy for Modeling Wind Energy Systems Using Fuzzy Cognitive Maps

Wind energy is currently a fast-growing interdisciplinary field that encompasses many different branches of engineering and science. Modeling and controlling wind energy systems are difficult and challenging problems. The basic structure of wind turbines and some wind control system methods are briefly reviewed. The need for using advanced theories from fuzzy and intelligent systems in studying wind energy systems is identified and justified. FCMs （fuzzy cognitive maps） are used to model wind energy systems. Simulation studies are performed and obtained results are discussed. A new mathematical approach has been proposed to model dynamical complex systems, the DYFUKN （dynamic fuzzy knowledge networks）. Many open problems in the areas of modeling and controlling wind energy systems are outlined.

A Spectrum Occupancy Model for Primary Users in Cognitive Radio Systems

There are abundant research results related to cognitive radio systems (CR systems), but using queueing models to portray CR systems is a new research trend. In this paper, a single-server retrial cognitive radio system with a linear retrial rate has been considered. The system has two types of users: primary users and secondary users. Secondary users have no effect on primary users because primary users have preemptive precedence. As a result, our purpose is to examine some performance indicators such as the expected queue length for primary users, the probability of the system being idle or occupied by a secondary user, and the probability of the system being busy. This paper begins by deriving the expressions for the generating functions based on the balance equations, so that we can calculate our goal conveniently.

Improving the Reliability of Unmanned Aircraft System Wireless Communications through Cognitive Radio Technology

Unmanned Aircraft System networks are a special type of networks where high speeds of the nodes, long distances and radio spectrum scarcity pose a number of challenges. In these networks, the strength of the transmitted/received signals varies due to jamming, multipath propagation, and the changing distance among nodes. High speeds cause another problem, Doppler Effect, which produces a shifting of the central frequency of the signal at the receiver. In this paper we discuss a modular system based on cognitive to enhance the reliability of UAS networks.

Investigation of system structure and information processing mechanism for cognitive skywave over-the-horizon radar

Based on the cognitive radar concept and the basic connotation of cognitive skywave over-the-horizon radar（SWOTHR）, the system structure and information processingmechanism about cognitive SWOTHR are researched. Amongthem, the hybrid network system architecture which is thedistributed configuration combining with the centralized cognition and its soft/hardware framework with the sense-detectionintegration are proposed, and the information processing framebased on the lens principle and its information processing flowwith receive-transmit joint adaption are designed, which buildand parse the work law for cognition and its self feedback adjustment with the lens focus model and five stages informationprocessing sequence. After that, the system simulation andthe performance analysis and comparison are provided, whichinitially proves the rationality and advantages of the proposedideas. Finally, four important development ideas of futureSWOTHR toward ＂high frequency intelligence information processing system＂ are discussed, which are scene information fusion, dynamic reconfigurable system, hierarchical and modulardesign, and sustainable development. Then the conclusion thatthe cognitive SWOTHR can cause the performance improvement is gotten.

A Hybrid Reconfigurable Antenna Design for Cognitive Radio System—Combination of Sensing and Reconfigurable Antennas

This paper presents a modification of an existing cognitive antenna design which contains two antennas: one is a sensing that covers the band from 1 - 11 GHz and the other is a hybrid triangular reconfigurable antenna. Two antennas are combined together to create a new design which called Cognitive Antenna. The new shape of the reconfigurable antenna will affect the cognitive antenna so as to work at two different frequencies via rotation by 180°. At each rotation, the antenna takes single frequency from number of possible frequencies that sensing antenna would find.

The Effect of Brief, Low-Intensity Stretching Gymnastics on Autonomic Nervous System Activity and Cognitive Function

Purpose: Stretching exercise and gymnastics both have beneficial effects, such as improvement of autonomic nervous system activity and mood. Additionally, studies on the effects of exercise on cognitive function have been conducted covering a wide range of age groups and have attracted much attention. However, conventional studies have set up programs with implementation times of 20 to 30 minutes. Therefore, shorter stretching programs are needed in order to fit them more easily into one’s free time. We examined the effects of a short 7-minute stretching gymnastics regime on the autonomic nervous system activity and cognitive function in 21 healthy participants. Methods: In this study, the participants performed a 10-minute cognitive task, followed by either Stretch Well Gymnastics, Stretch Band Gymnastics, or Radio Gymnastics sessions on different days. The participants then performed the cognitive task again. Heart rate was measured continuously throughout the experiment and we analyzed the heart rate variability. The cognitive tasks completed by all of the participants were evaluated for inhibitory control and cognitive flexibility. Results: A significant increase was shown in the sympathetic nerve activity during the Stretch Well Gymnastics, compared to the Radio Gymnastics and Stretch Band Gymnastics. Parasympathetic nerve levels were significantly increased after the gymnastics, compared to during the gymnastics, although there were no significant differences between any of the tasks. Additionally, in both the Stroop task and the number-Letter task, reaction times were faster in all of the sessions. In particular, the Stroop task showed the highest values for the Radio Gymnastics sessions, with marginally significantly lower scores for the Stretch Well Gymnastics sessions. Conclusion: The results showed that these heart rate variability responses supported the effects of autonomic activity associated with conventional low-intensity exercise. Additionally, stretching gymnastics for less than 10 minutes showed a positive effect on inhibitory function and cognitive flexibility.

A Cognitive Memory-Augmented Network for Visual Anomaly Detection

With the rapid development of automated visual analysis,visual analysis systems have become a popular research topic in the field of computer vision and automated analysis.Visual analysis systems can assist humans to detect anomalous events(e.g.,fighting,walking alone on the grass,etc).In general,the existing methods for visual anomaly detection are usually based on an autoencoder architecture,i.e.,reconstructing the current frame or predicting the future frame.Then,the reconstruction error is adopted as the evaluation metric to identify whether an input is abnormal or not.The flaws of the existing methods are that abnormal samples can also be reconstructed well.In this paper,inspired by the human memory ability,we propose a novel deep neural network(DNN)based model termed cognitive memory-augmented network(CMAN)for the visual anomaly detection problem.The proposed CMAN model assumes that the visual analysis system imitates humans to remember normal samples and then distinguishes abnormal events from the collected videos.Specifically,in the proposed CMAN model,we introduce a memory module that is able to simulate the memory capacity of humans and a density estimation network that can learn the data distribution.The reconstruction errors and the novelty scores are used to distinguish abnormal events from videos.In addition,we develop a two-step scheme to train the proposed model so that the proposed memory module and the density estimation network can cooperate to improve performance.Comprehensive experiments evaluated on various popular benchmarks show the superiority and effectiveness of the proposed CMAN model for visual anomaly detection comparing with the state-of-the-arts methods.The implementation code of our CMAN method can be accessed at https://github.com/CMANcode/CMAN_pytorch.

Towards a Theoretical Framework of Autonomous Systems Underpinned by Intelligence and Systems Sciences

Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence,cognition,computer,and systems sciences.This paper explores the intelligent and mathematical foundations of autonomous systems.It focuses on structural and behavioral properties that constitute the intelligent 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.The properties of system autonomy are formally analyzed towards a wide range of applications in computational intelligence and systems engineering.Emerging paradigms of autonomous systems including brain-inspired systems,cognitive robots,and autonomous knowledge learning systems are described.Advances in autonomous systems will pave a way towards highly intelligent machines for augmenting human capabilities.

IDSS: Designing to Extend the Cognitive Limits

The paper presents the conceptual and operational basis of the creation of IDSS based on our recent research experience. In this paper, an intelligent decision support system, IDSS is defined as: any interactive system that is specially designed to improve the decision making of its user by extending the user's cognitive decision making abilities. As a result, this view of man-machine joint cognitive system stresses the need to use computational technology to aid the user in the decision making process. And the human's role is to achieve total systems's objectives. The paper outlines the designing procedure in successive steps. First, the decision maker's cognitive needs for decision support are identified. Second, the computationally realizable support functions are defined that could be provided by IDSS. Then, the specific techniques that would best fill the decision needs are discussed. And finally, for system implementation the modern computational technology infrastructure is emphasized.

Anderson's Cognitive Theory and Learning Strategy Studies in Second Language Acquisition

Second language acquisition can not be understood without addressing the interaction between language and cognition. Cognitive theory can extend to describe learning strategies as complex cognitive skills. Theoretical developments in Anderson’s production systems cover a broader range of behavior than other theories, including comprehension and production of oral and written texts as well as comprehension, problem solving, and verbal learning.Thus Anderson’s cognitive theory can be served as a rationale for learning strategy studies in second language acquisition.

Cognitive Software Engineering: A Research Framework and Roadmap

The work of software engineers is inherently cognitive. Integral to their duties is understanding and developing several artifacts. Each one is based on a specific model and a given level of abstraction. What distinguishes Software Engineering is the logical complexity of some artifacts (especially programs), the high dependency among them, and the fact that the success of the software project also depends on the human and social factors, which characterize the engineers as individuals and as a group. The complexity of the daily tasks within a software development team motivates the investigation on the relevance of automating the software professionals’ cognitive processes in order to make their work easier and more efficient. The success of this endeavor is expected to emerge as Cognitive Software Engineering. For this aim, the present article suggests a research framework and roadmap, which build on the current state of the art. Some future directions in the Cognitive Software Engineering are presented.

Contribution to Enterprise Intelligent Systems Architecture: Assumptions, Expectations and a Proposal

Currently, enterprise intelligent systems are built without expressed assumptions likely to enable harmonizing the field and correctly attributing the intelligence label to enterprise systems within the way they are built. In the present paper we propose three base assumptions for an enterprise intelligent system architecture as related to 1) Cognitive Enterprise, 2) Embodied Cognition and 3) Agent Paradigm. The aim is to open up possibility to deal with intelligence at the early stages of enterprise architecture and related disciplines such as system engineering and software development. In addi-tion, we suggest possible expectations from Enterprise Intelligent Systems Architecture and propose an architectural frame based on the cognitive architecture CogAff. Compared with similar works, we noted differences in the fact that our work takes into consideration the cognitive aspect of the firm and the general aspect of intelligence.

Peer selecting model based on FCM for wireless distributed P2P files sharing systems

IIn order to improve the performance of wireless distributed peer-to-peer(P2P)files sharing systems,a general system architecture and a novel peer selecting model based on fuzzy cognitive maps(FCM)are proposed in this paper.The new model provides an effective approach on choosing an optimal peer from several resource discovering results for the best file transfer.Compared with the traditional min-hops scheme that uses hops as the only selecting criterion,the proposed model uses FCM to investigate the complex relationships among various relative factors in wireless environments and gives an overall evaluation score on the candidate.It also has strong scalability for being independent of specified P2P resource discovering protocols.Furthermore,a complete implementation is explained in concrete modules.The simulation results show that the proposed model is effective and feasible compared with min-hops scheme,with the success transfer rate increased by at least 20% and transfer time improved as high as 34%.

Kai Xin San ameliorates scopolamine-induced cognitive dysfunction

Kai Xin San(KXS, containing ginseng, hoelen, polygala, and acorus), a traditional Chinese herbal compound, has been found to regulate cognitive dysfunction; however, its mechanism of action is still unclear. In this study, 72 specific-pathogen-free male Kunming mice aged 8 weeks were randomly divided into a vehicle control group, scopolamine group, low-dose KXS group, moderate-dose KXS group, high-dose KXS group, and positive control group. Except for the vehicle control group and scopolamine groups(which received physiological saline), the doses of KXS(0.7, 1.4 and 2.8 g/kg per day) and donepezil(3 mg/kg per day) were gastrointestinally administered once daily for 2 weeks. On day 8 after intragastric treatment, the behavioral tests were carried out. Scopolamine group and intervention groups received scopolamine 3 mg/kg per day through intraperitoneal injection. The effects of KXS on spatial learning and memory, pathological changes of brain tissue, expression of apoptosis factors, oxidative stress injury factors, synapse-associated protein, and cholinergic neurotransmitter were measured. The results confirmed the following.(1) KXS shortened the escape latency and increased residence time in the target quadrant and the number of platform crossings in the Morris water maze.(2) KXS increased the percentage of alternations between the labyrinth arms in the mice of KXS groups in the Y-maze.(3) Nissl and terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling staining revealed that KXS promoted the production of Nissl bodies and inhibited the formation of apoptotic bodies.(4) Western blot assay showed that KXS up-regulated the expression of anti-apoptotic protein Bcl-2 and inhibited the expression of pro-apoptotic protein Bax. KXS up-regulated the expression of postsynaptic density 95, synaptophysin, and brain-derived neurotrophic factor in the cerebral cortex and hippocampus.(5) KXS increased the level and activity of choline acetyltransferase, acetylcholine, superoxide dismutase, and glutathione peroxidase, and reduced the level and activity of acetyl cholinesterase, reactive oxygen species, and malondialdehyde through acting on the cholinergic system and reducing oxidative stress damage. These results indicate that KXS plays a neuroprotective role and improves cognitive function through reducing apoptosis and oxidative stress, and regulating synapse-associated protein and cholinergic neurotransmitters.