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Table 1: Summary of the cases.

in Process Patterns for Software Systems In-house Integration and Merge – Experiences from Industry
by Rikard L, Ivica Crnković, Stig Larsson
"... In PAGE 2: ... What the cases have in common though is that the systems have a significant history of development and maintenance. The cases are summarized in Table1 . They are labeled A, B, etc.... ..."

Table 1: Summary of the cases.

in Concretizing the Vision of a Future Integrated System – Experiences from Industry
by Rikard L, Ivica Crnkovic, Stig Larsson
"... In PAGE 2: ... 4. The Cases Table1 presents the cases very briefly; the report accompanying the present paper contains more details [5]. The cases are labeled A, B, etc.... ..."

Table 34. Average Payment, Finalised Upper Limb Joint Injury Claims

in NSW Motor Accidents Scheme CTP Claim Frequency, Injuries and Costs
by Prepared By The
"... In PAGE 59: ... Claim Cost At June 1995, 5,309 upper limb joint injury claims were finalised. Table34 shows the average payment made on these finalised claims according to year of accident and time taken to make the final payment. Table 34.... ..."

Table 1. Correlation between human and machine similarity judgments.

in An Intrinsic Information Content Metric for Semantic Similarity in WordNet
by Nuno Seco, Tony Veale, Jer Hayes
"... In PAGE 2: ... In addition to these we also used Latent Semantic Analysis (LSA) to perform sim- ilarity judgments by means of a web interface available at the LSA website3. Table1 presents the similarity obtained using the chosen algo- rithms and their correlation coefficient ( ) with the human judg- ments. The first column states the algorithm used in obtaining sim- ilarity scores and the second the correlation between the algorithm and human ratings.... ..."

Table 4 A taxonomy of taxonomies pertaining to human-machine systems. ____________________________________________________ 1. Classification by Application DTIC (2000)

in Prepared for
by Douglas A. Wiegmann, Esa Rantanen 2002
"... In PAGE 11: ... Interim Summary: Evaluation of the Existing Taxonomies The above review is by no means comprehensive, as it can be safely concluded that there exists as many taxonomies as there are purposes, and many widely used taxonomies are not necessarily published. This review, however, allowed for a general overview of the classification science and the many attempts to bring the diverse of human-machine interactions within a manageable framework ( Table4 ). The review also revealed several shortcomings of the general taxonomic approach, as far as our objectives are concerned.... ..."

Table 1. Pairwise correlations of performance of human, machine and control disambigua-

in Word Sense Disambiguation by Human Subjects: Computational and Psycholinguistic Applications
by Thomas E. Ahlswede, David Lotand

Table 16. Function human and machine stage configurations for operator roles.

in '15. Supplementary Notes
by Christopher Nowakowski, Paul Green, Mark Kojima, Christopher Nowakowski, Paul Green, Mark Kojima, Pertormlng Urganlzation Heport
"... In PAGE 44: ... M: The machine was solely responsible for the stage. After each stage of each function was rated, the operator apos;s role in that function was classified as one of the following using the criteria shown in Table16 : direct performer, manual controller, supervisory controller, or executive controller. The entire list of 113 functions and their allocations as determined by Mitta, Kelly, and Folds (1996) can be found in Appendix C.... ..."

Table 1. The ANOVA analysis of performance di erences between the EEGS-based gene selection methods and their non-EEGS-based counterparts, respectively. A lower than 0:001 p-value and an interval [L;U] excluding 0 both indicate that the EEGS-based gene selection method and its non-EEGS-based counterpart performed signi cantly di erent. The dataset is CAR and the numbers of selected genes are 20;40;60 and 80. #Genes = 20 #Genes = 40 #Genes = 60 #Genes = 80

in SELECTING GENES WITH DISSIMILAR DISCRIMINATION STRENGTH FOR SAMPLE CLASS PREDICTION
by Zhipeng Cai, Y Goebel, Mohammad R. Salavatipour, Yi Shi, Lizhe Xu, Guohui Lin

Table 33. Upper Limb Joint Injury,

in NSW Motor Accidents Scheme CTP Claim Frequency, Injuries and Costs
by Prepared By The

Table 4: Anthropometric parameter values for the upper limb.

in A Neural Model Of Cerebellar Learning For Arm Movement Control: Cortico-Spino-Cerebellar Dynamics
by J.L. Contreras-Vidal, Stephen Grossberg, Daniel Bullock
"... In PAGE 19: ... The product of viscosity and angular velocity is important in achieving stability of the limb. We used typical estimates of segment masses (mi) and segment lengths (li) and inertial characteristics from anthropometric data (see Table4 ) of Zatsirosky and Seluyanov (1983) and Karst and Hasan (1991). In our simulations, the shoulder and the elbow are restricted to one rotational degree of freedom ( exion-extension).... ..."
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