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Nong Ye

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Publication statistics

Pub. period:1991-2005
Pub. count:13
Number of co-authors:10

Co-authors

Number of publications with 3 favourite co-authors:

Gavriel Salvendy:5
Fred Dech:1
Joseph Giordano:1

Productive colleagues

Nong Ye's 3 most productive colleagues in number of publications:

Gavriel Salvendy:148
Fred Dech:4
Baijun Zhao:3

Calendar

May 20

CTS 2013 - The 2013 Intl. Conf. on Collaboration Techn. and Systems

May 22

Knowing (by) Designing 2013

May 27

13th Intl. Conf. on New Interfaces for Musical Expression

May 19

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Publications by Nong Ye (bibliography)

2005

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Ye, Nong and Farley, Toni (2005): A Scientific Approach to Cyberattack Detection. In IEEE Computer, 38 (11) pp. 55-61.

2001

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Ye, Nong, Giordano, Joseph and Feldman, John (2001): A process control approach to cyber attack detection. In Communications of the ACM, 44 (8) pp. 76-82.

1999

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Banerjee, Amarnath, Banerjee, Prashant, Ye, Nong and Dech, Fred (1999): Assembly Planning Effectiveness using Virtual Reality. In Presence: Teleoperators and Virtual Environments, 8 (2) pp. 204-217.

1997

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Ye, Nong (1997): Introduction. In International Journal of Human-Computer Interaction, 9 (1) p. 1.

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Ye, Nong (1997): Neural Networks Approach to User Modeling and Intelligent Interface: A Review and Reappraisal. In International Journal of Human-Computer Interaction, 9 (1) pp. 3-23.

“This article provides background and an overview of the neural networks approach to user modeling and intelligent interface. Activities in user modeling and intelligent interface are discussed. Intelligent architectures of various neural network models are introduced to illustrate how neural networks solve problems and learn knowledge of problem solving. The introduction of neural networks helps in associating intelligent capabilities of neural networks with the activities in user modeling and intelligent interface. The practical utility of neural networks in supporting user modeling and intelligent interface is demonstrated by reviewing neural networks developed in this area. Issues relating to the design of neural networks in various contexts of user modeling and intelligent interface are also addressed.”

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Ye, Nong and Zhao, Baijun (1997): Automatic Setting of Article Format Through Neural Networks. In International Journal of Human-Computer Interaction, 9 (1) pp. 81-100.

“The automatic format setting of journal articles for reducing the workload of computer users involves two processes: automatic acquisition of article format and automatic recall of article format. Several neural networks have been explored to implement the two processes. The advantages and disadvantages of these neural networks are evaluated in comparison with capabilities of conventional computer programs. A heteroassociative back-propagation network has been developed for the automatic acquisition process. This network excels over computer programs because of its abilities in learning and generalizing implicit knowledge from examples. A bidirectional associative memory network, a Boltzman network, and an autoassociative back-propagation network have been investigated for the automatic recall process. None of them excel over computer programs in terms of recall accuracy.”

1996

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Ye, Nong and Salvendy, Gavriel (1996): An Objective Approach to Exploring Skill Differences in Strategies of Computer Program Comprehension. In Behaviour and Information Technology, 15 (3) pp. 139-147.

“An experiment was conducted to examine skill differences in the control strategy for computer program comprehension. A computer program along with its hierarchy of program plans was provided to 10 intermediate and 10 novice computer programmers. Each program plan is known as a program segment to the subjects. A random list of plan goals was also provided to the subjects. The subjects were asked to match each program segment with its goal while they were comprehending the program. Several measures of the subjects' performance and control strategy were collected and analysed. The results indicated the use of an overall top-down strategy by both intermediates and novices for program comprehension. Novices' control strategies involved more opportunistic elements than experts' in the overall top-down process of program comprehension. Those differences in the control strategy between intermediates and novices result in better performance in intermediates than novices.”

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Hennings, Leah K. and Ye, Nong (1996): Interaction of Screen Distances, Screen Letter Heights and Source Document Distances. In Interacting with Computers, 8 (4) pp. 311-322.

“An experiment was conducted to investigate the main and interaction effects of screen distances (600 mm and 900 mm), screen letter heights (for visual angles of 16, 20, and 30 minutes of arc), and source document distances (same as the screen distance and closer than the screen distance) on visual performance and subjective preference in text proofreading tasks. Thirty subjects in different age groups participated in the experiment. The experimental results revealed that the subjects' performance improved from the letter height of 16 minutes of arc to the letter height of 20 minutes of arc for the 600 mm screen distance, but from 20 minutes of arc to 16 minutes of arc for the 900 mm screen distance. The subjects generally preferred a screen distance slightly higher than 600 mm, a letter height at the 22 minutes of arc, and a small difference between the visual angle of screen letters and the visual angle of source letters.”

1994

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Ye, Nong and Salvendy, Gavriel (1994): Quantitative and Qualitative Differences Between Experts and Novices in Chucking Computer Software Knowledge. In International Journal of Human-Computer Interaction, 6 (1) pp. 105-118.

“This study investigated quantitative and qualitative differences between experts and novices in knowledge structure, specifically in their chucking of computer software knowledge. An experiment was conducted to collect pairwise relatedness ratings of 23 concepts in C computer programming from 10 experts and 10 novices. Correlation analyses of relatedness ratings were performed to examine expert-novice differences in overall knowledge structure. The intergroup correlation coefficients of experts were greater than the intragroup correlation coefficients of novices and the intergroup correlation coefficients between experts and novices. Hence, there existed structural differences of knowledge between experts and novices. To examine how experts differ from novices quantitatively and qualitatively in their knowledge chucking, the relatedness ratings of experts were averaged within the group to give a single set of relatedness ratings, and the relatedness ratings of novices were also averaged within the group to give a single set of relatedness ratings. Then the hierarchical clustering analyses were performed on the set of relatedness ratings for the experts group and on the set of relatedness ratings for the novices group, respectively. The hierarchical clustering of C concepts made by experts and the hierarchical clustering of C concepts made by novices indicated that novices had more but smaller knowledge chunks than experts for the common set of knowledge, and that the rough chucking of knowledge made by novices was qualitatively similar to the rough chucking of knowledge made by experts, although on a more detailed level qualitative differences still existed.”

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Ye, Nong and Brinkman, Donald J. (1994): Test and Evaluation of Knowledge Transfer between Task Domains. In: Proceedings of the Human Factors and Ergonomics Society 38th Annual Meeting 1994. pp. 1123-1127.

“To examine the transfer of knowledge representation between task domains, working engineers and pharmacists were asked to provide pairwise dissimilarity ratings of concepts in a test domain. The test domain was independent of engineering and pharmacy and was relatively new to both subject groups. A quantitative technique based on the multidimensional scaling and directional statistics was used to test and evaluate group differences in knowledge representation. The results indicated that there was no statistically significant difference between two groups in knowledge representation of the test domain. That is, knowledge representation was domain-specific and was not transferable between task domains.”

1993

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Ye, Nong and Salvendy, Gavriel (1993): Introducing Problem Solving Strategies of Users into the Interface Design. In: Proceedings of the Fifth International Conference on Human-Computer Interaction 1993. pp. 873-878.

“Human problem solving strategies must be taken into consideration of human-computer interface design to avoid unnecessary training time, job disorientation, and job dissatisfaction of users in computerized job environments. An experiment was conducted to investigate differences of expert and novice computer programmers in program comprehension strategies. Both expert and novice programmers used a knowledge retrieval strategy first to recognize familiar program segments and a problem reasoning strategy then to comprehend unfamiliar program segments. Experts controlled their program comprehension process in a more systematic manner than novices. Human-computer interface designs for compatibility with user problem solving strategies, such as a hierarchical grouping of familiar interaction elements and a non-hierarchical grouping of unfamiliar interaction elements, are discussed.”

1991

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Ye, Nong and Salvendy, Gavriel (1991): Cognitive Engineering Based Knowledge Representation in Neural Networks. In Behaviour and Information Technology, 10 (5) pp. 403-418.

“A model of a human neural knowledge processing system is presented that suggests the following. First, an entity in the outside world tends to be locally encoded in neural networks so that the conceptual information structure is mirrored in its physical implementation. Second, the knowledge of problem solving is implemented in a quite implicit way in the internal structure of the neural network (a functional group of associated hidden neurons and their connections to entity neurons) not in individual neurons or connections. Third, the knowledge system is organized and implemented in a modular fashion in neural networks according to the local specialization of problem solving where a module of neural network implements an inter-related group of knowledge such as a schema, and different modules have similar processing mechanisms, but differ in their input and output patterns. A neural network module can be tuned just as a schema structure can be adapted for changing environments. Three experiment were conducted to try to validate the suggested cognitive engineering based knowledge structure in neural networks through computer simulation. The experiments, which were based on a task of modulo arithmetic, provided some insights into the plausibility of the suggested model of a knowledge processing system.”

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Ye, Nong and Salvendy, Gavriel (1991): An Adaptive Interface Design Using Neural Networks. In: Proceedings of the Fourth International Conference on Human-Computer Interaction 1991. pp. 435-439.

“The neural networks' ability to learn by examples is combined into the design of an adaptive interface. This adaptive menu based interface between the user and the UNIX system can accommodate differences in user's experience about the system and individual differences among users.”

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