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Journal articles on the topic 'Computer Performance Evaluation'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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1

Bradley, J. T. "Computer Performance Evaluation: Preface." Computer Journal 54, no. 5 (November 2, 2010): 641–42. http://dx.doi.org/10.1093/comjnl/bxq076.

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2

Eeckhout, Lieven. "Computer Architecture Performance Evaluation Methods." Synthesis Lectures on Computer Architecture 5, no. 1 (December 22, 2010): 1–145. http://dx.doi.org/10.2200/s00273ed1v01y201006cac010.

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3

Ilyas, M., and H. Mouftah. "Performance evaluation of computer communications networks." IEEE Communications Magazine 23, no. 4 (April 1985): 18–29. http://dx.doi.org/10.1109/mcom.1985.1092558.

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4

Patil, Shruti, and David J. Lilja. "Statistical methods for computer performance evaluation." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 1 (August 24, 2011): 98–106. http://dx.doi.org/10.1002/wics.192.

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5

TOUYAMA, TAKAYOSHI, and SUSUMU HORIGUCHI. "PERFORMANCE EVALUATION OF PRACTICAL PARALLEL COMPUTER MODEL LogPQ." International Journal of Foundations of Computer Science 12, no. 03 (June 2001): 325–40. http://dx.doi.org/10.1142/s0129054101000515.

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The present super computer will be replaced by a massively parallel computer consisting of a large number of processing elements which satisfy the continuous increasing depend for computing power. Practical parallel computing model has been expected to develop efficient parallel algorithms on massively parallel computers. Thus, we have presented a practical parallel computation model LogPQ by taking account of communication queues into the LogP model. This paper addresses the performance of a parallel matrix multiplication algorithm using LogPQ and LogP models. The parallel algorithm is implemented on Cray T3E and the parallel performances are compared with on the old machine CM-5. This shows that the communication network of T3E has superior buffering behavior than CM-5, in which we don't need to prepare extra buffering on T3E. Although, a little effect remains for both of the send and receive bufferings. On the other hand, the effect of message size remains, which shows the necessity of the overhead and gap proportional to the message size.
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6

Zhang, Lei, and Douglas G. Down. "APEM — Approximate Performance Evaluation for Multi-Core Computers." Journal of Circuits, Systems and Computers 28, no. 01 (October 15, 2018): 1950004. http://dx.doi.org/10.1142/s021812661950004x.

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Mean Value Analysis (MVA) has long been a standard approach for performance analysis of computer systems. While the exact load-dependent MVA algorithm is an efficient technique for computer system performance modeling, it fails to address multi-core computer systems with Dynamic Frequency Scaling (DFS). In addition, the load-dependent MVA algorithm suffers from numerical difficulties under heavy load conditions. The goal of our paper is to find an efficient and robust method which is easy to use in practice and is also accurate for performance prediction for multi-core platforms. The proposed method, called Approximate Performance Evaluation for Multi-core computers (APEM), uses a flow-equivalent performance model designed specifically to address multi-core computer systems and identify the influence on the CPU demand of the effect of DFS. We adopt an approximation technique to estimate resource demands to parametrize MVA algorithms. To validate the application of our method, we investigate three case studies with extended TPC-W benchmark kits, showing that our method achieves better accuracy compared with other commonly used MVA algorithms. We compare the three different performance models, and we also extend our approach to multi-class models.
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7

Pratama S, Ajif Yunizar, Kusdarnowo Hantoro, and Rani Suryani. "Decision Making Using Hierarchical Analytical Processes to Evaluate Computer Management Performance." Journal of Informatic and Information Security 3, no. 1 (July 29, 2022): 13–22. http://dx.doi.org/10.31599/jiforty.v3i1.1201.

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The presence of PC computers since the 80s with its rapid and very widespread growth can be seen as a one of the most significant developments in the field of information and communication technology. This study aims to propose decision making using the Analytical Hierarchy Process approach to evaluate computer computers with respect to using the order of preference of the user. To get the most desirable features that affect the decision to choose a computer to be identified. This is realized through a survey conducted on the target group, experienced experts in the telecommunications sector and literature study. The AHP method was then used in the evaluation procedure. Analytical Hierarchy Process (AHP) was applied to determine the relative weight of the evaluation criteria applied to rank computer alternatives. The results of this case study illustrate the effectiveness of the proposed computer management performance selection.
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8

Antonyuk, Y. M., B. A. Shyyak, and M. I. Antonyuk. "PERFORMANCE EVALUATION OF COMPUTER CAMPUS NETWORK SEGMENTS." Telecommunication and information technologies, no. 4 (2019): 111–19. http://dx.doi.org/10.31673/2412-4338.2019.041119.

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Wang, Yanli, Shiming Bai, Jinzhuo Jia, and Haijun Chen. "Performance Evaluation of Defoamer under Computer Technology." Journal of Physics: Conference Series 2037, no. 1 (September 1, 2021): 012037. http://dx.doi.org/10.1088/1742-6596/2037/1/012037.

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10

Kabayama, A. M., and L. G. Trabasso. "Performance evaluation of 3D computer vision techniques." Journal of the Brazilian Society of Mechanical Sciences 24, no. 3 (July 2002): 234–38. http://dx.doi.org/10.1590/s0100-73862002000300013.

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11

Obaidat, Mohammad S. "Performance Evaluation of Computer and Telecommunication Systems." SIMULATION 84, no. 2-3 (February 2008): 59–60. http://dx.doi.org/10.1177/0037549708092168.

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12

Obaidat, Mohammad S. "Performance Evaluation of Computer and Telecommunication Systems." SIMULATION 70, no. 4 (April 1998): 252. http://dx.doi.org/10.1177/003754979807000405.

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13

Birta, Louis G., and Mohammad S. Obaidat. "Performance Evaluation of Computer and Telecommunication Systems." SIMULATION 70, no. 5 (May 1998): 346. http://dx.doi.org/10.1177/003754979807000508.

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14

Obaidat, Mohammad S. "Performance Evaluation of Computer and Telecommunications Systems." SIMULATION 72, no. 5 (May 1999): 280–82. http://dx.doi.org/10.1177/003754979907200501.

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15

Porotskiy, S. M., and A. E. Fateev. "System and real performance evaluation of computer." ACM SIGMETRICS Performance Evaluation Review 20, no. 2 (November 1992): 43–46. http://dx.doi.org/10.1145/141858.141865.

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16

Awan, Irfan. "Performance modelling and evaluation of computer systems." Journal of Computer and System Sciences 72, no. 7 (November 2006): 1119–20. http://dx.doi.org/10.1016/j.jcss.2005.12.011.

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17

Antonyuk, Yaroslav M. "Performance evaluation of computer campus network segments." Upravlâûŝie sistemy i mašiny, no. 5(277) (October 2018): 62–68. http://dx.doi.org/10.15407/usim.2018.05.062.

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18

DASSEN, WILLEM R. M., KAREL DULK, ANTON P. M. GORGELS, PEDRO BRUGADA, and HEIN J. J. WELLENS. "Evaluation of Pacemaker Performance Using Computer Simulation." Pacing and Clinical Electrophysiology 8, no. 6 (November 1985): 795–805. http://dx.doi.org/10.1111/j.1540-8159.1985.tb05897.x.

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19

Smelyanskii, R. L. "Analytical performance evaluation of distributed computer systems." Computational Mathematics and Modeling 5, no. 4 (1994): 288–91. http://dx.doi.org/10.1007/bf01130311.

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20

Buriboev, Abror, and Azamjon Muminov. "Computer State Evaluation Using Adaptive Neuro-Fuzzy Inference Systems." Sensors 22, no. 23 (December 5, 2022): 9502. http://dx.doi.org/10.3390/s22239502.

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Several crucial system design and deployment decisions, including workload management, sizing, capacity planning, and dynamic rule generation in dynamic systems such as computers, depend on predictive analysis of resource consumption. An analysis of the computer components’ utilizations and their workloads is the best way to assess the performance of the computer’s state. Especially, analyzing the particular or whole influence of components on another component gives more reliable information about the state of computer systems. There are many evaluation techniques proposed by researchers. The bulk of them have complicated metrics and parameters such as utilization, time, throughput, latency, delay, speed, frequency, and the percentage which are difficult to understand and use in the assessing process. According to these, we proposed a simplified evaluation method using components’ utilization in percentage scale and its linguistic values. The use of the adaptive neuro-fuzzy inference system (ANFIS) model and fuzzy set theory offers fantastic prospects to realize use impact analyses. The purpose of the study is to examine the usage impact of memory, cache, storage, and bus on CPU performance using the Sugeno type and Mamdani type ANFIS models to determine the state of the computer system. The suggested method is founded on keeping an eye on how computer parts behave. The developed method can be applied for all kinds of computing system, such as personal computers, mainframes, and supercomputers by considering that the inference engine of the proposed ANFIS model requires only its own behavior data of computers’ components and the number of inputs can be enriched according to the type of computer, for instance, in cloud computers’ case the added number of clients and network quality can be used as the input parameters. The models present linguistic and quantity results which are convenient to understand performance issues regarding specific bottlenecks and determining the relationship of components.
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21

Jo, Jung Bok. "Study on Fuzzy Queueing Theory and Its Application to Performance Evaluation of Computer Systems." Journal of Japan Society for Fuzzy Theory and Systems 8, no. 5 (1996): 825. http://dx.doi.org/10.3156/jfuzzy.8.5_825_1.

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22

Lenard, Mary Jane, Susan Wessels, and Cindi Khanlarian. "Gender Differences In Attitudes Toward Computers And Performance In The Accounting Information Systems Class." American Journal of Business Education (AJBE) 3, no. 2 (February 1, 2010): 23–30. http://dx.doi.org/10.19030/ajbe.v3i2.382.

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Using a model developed by Young (2000), this paper explores the relationship between performance in the Accounting Information Systems course, self-assessed computer skills, and attitudes toward computers. Results show that after taking the AIS course, students experience a change in perception about their use of computers. Females’ self-evaluation of their computer skills improved significantly after the course, while males’ self-evaluation showed no change. Females also received significantly higher grades compared to males. There was a significant relationship between confidence and self-assessed computer skills. This study shows that the AIS course is successful in developing students’ confidence in using computers. Training that results in accurate self-assessment of skills may influence the choice of careers and one’s future success in those careers.
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23

Vdovjak, Krešimir, Josip Balen, and Krešimir Nenadić. "Experimental Evaluation of Desktop Operating Systems Networking Performance." International journal of electrical and computer engineering systems 11, no. 2 (June 24, 2020): 67–76. http://dx.doi.org/10.32985/ijeces.11.2.2.

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The rapid advancement of network, communication and Internet technology resulted with always-on, always-connected, device-independent and remote online working, business, education and entertainment environment. Consequently, users are searching for solutions and technologies that enable fast and reliable wide area network connection and the typical solution is through using personal computers connected with ethernet cable to network equipment and infrastructure that supports gigabit ethernet connection. Besides the complex network infrastructure that can influence performance, the bottleneck can also be caused by insufficient hardware, operating system and software resources on clients’ machines. Therefore, in this paper a networking performance evaluation of three globally most common and most used versions of Windows operating systems; namely Windows 7TM, Windows 8.1TM and Windows 10TM, on two identical computer systems, is conducted. Networking performance measurements are performed with three different benchmarks: namely iPerf, D-ITG and NetStress. Performance evaluation results showed that a newer versions of an operating system bring certain networking performance improvements but by sacrificing other performances.
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24

MARUOKA, Akira, and Hirokazu HIRANO. "On performance evaluation of Personal Computer in CFD." Wind Engineers, JAWE 2001, no. 86 (2001): 33–38. http://dx.doi.org/10.5359/jawe.2001.33.

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25

Lazowska, E. D., J. Zahorjan, and K. C. Sevcik. "Computer System Performance Evaluation Using Queueing Network Models." Annual Review of Computer Science 1, no. 1 (June 1986): 107–37. http://dx.doi.org/10.1146/annurev.cs.01.060186.000543.

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26

Kemper, Peter, and William H. Sanders. "Modelling techniques and tools for computer performance evaluation." Performance Evaluation 63, no. 6 (June 2006): 521–23. http://dx.doi.org/10.1016/j.peva.2005.06.004.

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27

Field, Tony, Peter Harrison, Jeremy Bradley, and Uli Harder. "Modelling techniques and tools for computer performance evaluation." Performance Evaluation 54, no. 2 (October 2003): 77–78. http://dx.doi.org/10.1016/s0166-5316(03)00092-0.

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28

Guan, Lin, Xingang Wang, Irfan Awan, and Waltenegus Dargie. "Performance modeling and evaluation of heterogeneous computer networks." Journal of Computer and System Sciences 77, no. 5 (September 2011): 835–36. http://dx.doi.org/10.1016/j.jcss.2010.08.009.

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29

Xenos, Michalis, and Thanos Papadopoulos. "COMPUTER AIDED EVALUATION OF HIGHER EDUCATION TUTORS’ PERFORMANCE." Studies in Educational Evaluation 33, no. 2 (June 2007): 175–96. http://dx.doi.org/10.1016/j.stueduc.2007.04.005.

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30

Takagi, H., and M. Murata. "International seminar on computer networks and performance evaluation." Computer Networks and ISDN Systems 11, no. 1 (January 1986): 59–68. http://dx.doi.org/10.1016/0169-7552(86)90029-2.

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31

Anisimov, V. G., E. G. Anisimov, T. N. Saurenko, and V. P. Los’. "Performance Evaluation of Antivirus Systems for Computer Networks." Automatic Control and Computer Sciences 56, no. 8 (December 2022): 883–87. http://dx.doi.org/10.3103/s0146411622080016.

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32

Ryabko, Boris, and Anton Rakitskiy. "Theoretical Approach to Performance Evaluation of Supercomputers." Journal of Circuits, Systems and Computers 27, no. 04 (December 6, 2017): 1850062. http://dx.doi.org/10.1142/s0218126618500627.

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In this paper, we extend an information-theoretic approach of computer performance evaluation to supercomputers. This approach is based on the notion of computer Capacity which can be estimated relying solely on the description of computer architecture. We describe the method of calculating Computer Capacity for supercomputers including the influence of the architecture of communication network. The suggested approach is applied to estimate the performance of three of the top 10 supercomputers (according to TOP500 June-2016 list) which are based on Haswell processors. For greater objectivity of results, we compared them relatively to values of another supercomputer which is based an Ivy Bridge processors (this microarchitecture differs from Haswell). The obtained results are compared with values of TOP500 LINPACK benchmark and theoretical peak and we arrive at conclusions about the applicability of the presented theoretical approach (nonexperimental) for performance evaluation of real supercomputers. In particular, it means that the estimations of the computer capacity can be used at the design stage of the development of supercomputers.
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33

Le Boudec, Jean-Yves. "Performance Evaluation." ACM SIGMETRICS Performance Evaluation Review 49, no. 4 (June 2, 2022): 20–23. http://dx.doi.org/10.1145/3543146.3543151.

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Lectures that use probability or statistics often appear complex to students, sometimes because the underlying stochastic models are not explicited. Writing a stochastic simulation program is a common exercise in a performance evaluation course, and we can view a simulation program as an implementation of a stochastic model. Thus, students who took such a course are trained to fully specify stochastic models, and this can help them fully understand probabilistic statements. We give examples in the context of random graphs as well as in the interpretation of residual scores in machine learning. Last we observe that Palm calculus, which is at the heart of classical queuing theory, can be used to provide important insights into sampling problems encountered in data collection tasks.
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34

Le Boudec, Jean-Yves. "Performance Evaluation." ACM SIGMETRICS Performance Evaluation Review 49, no. 4 (June 2, 2022): 20–23. http://dx.doi.org/10.1145/3543146.3543151.

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Lectures that use probability or statistics often appear complex to students, sometimes because the underlying stochastic models are not explicited. Writing a stochastic simulation program is a common exercise in a performance evaluation course, and we can view a simulation program as an implementation of a stochastic model. Thus, students who took such a course are trained to fully specify stochastic models, and this can help them fully understand probabilistic statements. We give examples in the context of random graphs as well as in the interpretation of residual scores in machine learning. Last we observe that Palm calculus, which is at the heart of classical queuing theory, can be used to provide important insights into sampling problems encountered in data collection tasks.
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35

You, Yong Bin, Shan Shan Zhao, Rui Jun Jing, and Li Yan Liu. "Embedded Computer Performance Evaluation Model Based on Weight Contact Coefficient." Advanced Materials Research 951 (May 2014): 208–13. http://dx.doi.org/10.4028/www.scientific.net/amr.951.208.

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Because the embedded computer performance evaluation indexes are uncertainty, the better method of embedded computer performance evaluation is proposed by applying the set pair analysis theory. First, identical degree, different degree, and contrary degree are respectively determined by the optimal program, intermediate program and worst program, and the weight of performance evaluation indexes are determined by order relation analysis. Finally, the weight contact coefficient is obtained, and the sort of embedded computer performance is determined by situation of weight contact coefficient. The experimental results show that this method can effectively evaluate the embedded computer performance, and the evaluation results are in line with the actual situation.
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36

Parncutt, Richard. "The reliability/ validity of cognitive/emotional approaches to the evaluation of musical performance: Implications for competition juries." Chopin Review, no. 1 (April 27, 2023): 38–51. http://dx.doi.org/10.56693/cr.102.

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Procedures to evaluate the quality of musical performance, like psychological tests generally, vary in reliability and validity. How can both be optimised? The subjective world of jurors comprises input (sensations) and output (thoughts and emotions). Cognitive approaches to performance evaluation, in which different aspects are analytically considered and the results combined, are more reliable than emotional approaches, which are ultimately based on holistic ‘gut reactions’. However, emotional approaches may be more valid. Both depend on the serial order in which performances are presented to evaluators, suggesting a need for independent, computer-controlled procedures in which jurors evaluate performances in different random orders. Jurors can be influenced by performers’ appearance and movements, as well as knowledge about past performances, whether or not they believe they should be; additional blind evaluations (sound only) could help. Evaluations may depend primarily on specific features such as right-hand melodic phrasing in romantic piano music. It would be interesting to systematically track the emotional state of jurors during a competition in order to better understand the interaction between their thoughts and emotions. In general, traditional approaches to performance evaluation might be supplemented (not replaced) by psychologically inspired, computer-based procedures. A ‘superjury’ could compare evaluations from different methods or ‘subjuries’.
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37

Ya, Guan Xiang, Xi Deng, Kai Yuan Zheng, and Xiang Xing Shen. "Applied Technology in the Performance of University Computer Labs." Advanced Materials Research 859 (December 2013): 503–6. http://dx.doi.org/10.4028/www.scientific.net/amr.859.503.

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As information technology and traditional teaching methods of continuous integration, how to improve the management level of university computer labs, has been a concern in the community and academic research focus. Building an information management and evaluation system with applied technology are important ways to improve computer lab performance management. Focus on the Internet environment of computer equipment working state information collection and preservation, the application of statistical methods of analysis and evaluation of lab Performance. The research method is also applies to information collection and evaluation of student’s E-learning behavior and so on. Summed up the evaluation and building for lab performance of the innovation and value.
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38

Beg, Muhammad Arif, Zhanmin Zhang, and W. Ronald Hudson. "Development of Pavement Type Evaluation Procedure for Texas Department of Transportation." Transportation Research Record: Journal of the Transportation Research Board 1699, no. 1 (January 2000): 23–32. http://dx.doi.org/10.3141/1699-04.

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A rational procedure is presented for evaluating alternative pavement types for roadway projects. Agency costs, user delay costs, and performance levels are important factors for comparing alternative pavement strategies. These factors are included in the evaluation procedure. Economic evaluations are based on life-cycle cost analysis. Cost-effectiveness analysis is also included in the procedure and uses area under the performance curve as a measure of the effectiveness of a pavement design strategy. Limitations in economic evaluations and the role of other miscellaneous factors in pavement type selection are also discussed. A computer program, TxPTS, was developed to automate the evaluation procedure. Case studies are presented demonstrating the use of the program.
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39

Welch, Wayne W., and Ronald E. Anderson. "The Performance of Performance Assessment in a Large-Scale Study of Computer Education." Journal of Educational Computing Research 11, no. 2 (September 1994): 107–19. http://dx.doi.org/10.2190/3bq6-why7-kueu-gqkv.

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A performance assessment of word processing competencies was implemented as part of the International Evaluation of Achievement (IEA) of computer education. A national sample of eighth and eleventh grade students was given a series of word processing tasks and analyses of their performance was examined to determine the useability, reliability, and validity of the process. The assessment met generally acceptable standards for scale reliability and persuasive evidence of validity was obtained, for example, success was related to opportunity to learn word processing and eleventh grade students outperformed eighth grade students. Other findings were that girls outperformed boys on the performance assessment and no relationships were found between scores on the measure and level of parent education or availability of computers in the home.
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40

Obaidat, Mohammad S. "Advances in Performance Evaluation of Computer and Telecommunication Systems." SIMULATION 83, no. 2 (February 2007): 135–37. http://dx.doi.org/10.1177/0037549707080606.

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41

Obaidat, Mohammad S. "Guest Editorial: Performance Evaluation of Computer and Telecommunication Systems." SIMULATION 85, no. 8 (August 2009): 483–84. http://dx.doi.org/10.1177/0037549709340606.

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42

Ranganathan, A., and S. J. Upadhyaya. "Performance evaluation of rollback-recovery techniques in computer programs." IEEE Transactions on Reliability 42, no. 2 (June 1993): 220–26. http://dx.doi.org/10.1109/24.229490.

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43

Hong, Namhee Kim, Sung-Gul Hong, and Sung-Pil Chang. "Computer-supported evaluation for seismic performance of existing buildings." Advances in Engineering Software 34, no. 2 (February 2003): 87–101. http://dx.doi.org/10.1016/s0965-9978(02)00106-0.

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44

Schifrin, Barry S., and Malcolm G. Munro. "Contemporaneous, Computer-Assisted, Faculty-Group Evaluation of Resident Performance." Obstetrics & Gynecology 101, Supplement (April 2003): 20S. http://dx.doi.org/10.1097/00006250-200304001-00042.

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SCHIFRIN, B. "Contemporaneous, computer-assisted, faculty-group evaluation of resident performance." Obstetrics & Gynecology 101, no. 4 (April 2003): S20. http://dx.doi.org/10.1016/s0029-7844(02)02794-1.

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46

Ziavras, Sotirios G., Haim Grebel, Anthony T. Chronopoulos, and Florent Marcelli. "A new-generation parallel computer and its performance evaluation." Future Generation Computer Systems 17, no. 3 (November 2000): 315–33. http://dx.doi.org/10.1016/s0167-739x(00)00082-0.

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47

Obaidat, Mohammad S. "Advances in performance evaluation of computer and telecommunications networking." Computer Communications 25, no. 11-12 (July 2002): 993–96. http://dx.doi.org/10.1016/s0140-3664(02)00013-0.

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48

Stancheva, E., and J. Pavlov. "Computer Network Integrated Manufacturing: Performance Evaluation Using Distributed Simulation." IFAC Proceedings Volumes 22, no. 6 (July 1989): 311–16. http://dx.doi.org/10.1016/s1474-6670(17)54392-x.

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49

Krishnaswamy, U., and I. D. Scherson. "A framework for computer performance evaluation using benchmark sets." IEEE Transactions on Computers 49, no. 12 (2000): 1325–38. http://dx.doi.org/10.1109/12.895853.

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50

Maydell, Ursula. "Editorial: Special Issue On Performance Evaluation Of Computer Systems." INFOR: Information Systems and Operational Research 23, no. 3 (August 1985): 213–14. http://dx.doi.org/10.1080/03155986.1985.11731966.

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