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Journal articles on the topic 'Omnispective analysis and reasoning'

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

Simmons, Barbara. "Clinical reasoning: concept analysis." Journal of Advanced Nursing 66, no. 5 (May 2010): 1151–58. http://dx.doi.org/10.1111/j.1365-2648.2010.05262.x.

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2

Tang, Hongmei, Wenzhong Tang, Ruichen Li, Yanyang Wang, Shuai Wang, and Lihong Wang. "Analysis of Knowledge Graph Path Reasoning Based on Variational Reasoning." Applied Sciences 12, no. 12 (June 17, 2022): 6168. http://dx.doi.org/10.3390/app12126168.

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Knowledge graph (KG) reasoning improves the perception ability of graph structure features, improving model accuracy and enhancing model learning and reasoning capabilities. This paper proposes a new GraphDIVA model based on the variational reasoning divergent autoencoder (DIVA) model. The network structures and calculation processes of the models are analyzed. The GraphSAGE algorithm is introduced into the path reasoning module to solve the inability of the original model to perceive the features of the graph structure, which leads to a decline in the accuracy rate. Hence, GraphDIVA can achieve a higher accuracy rate with fewer learning iterations. The experiments show the efficiency and effectiveness of our model and proves that our method has a better effect on the accuracy rate and training difficulty than the baseline model on the FB15k-237 and NELL-995 benchmark datasets.
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3

Alaoui, Larbi, and Antonio Penta. "Cost-Benefit Analysis in Reasoning." Journal of Political Economy 130, no. 4 (April 1, 2022): 881–925. http://dx.doi.org/10.1086/718378.

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4

Schupbach, Jonah N. "Robustness Analysis as Explanatory Reasoning." British Journal for the Philosophy of Science 69, no. 1 (March 1, 2018): 275–300. http://dx.doi.org/10.1093/bjps/axw008.

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5

Evans, Jonathan St B. T. "Rational Analysis of Illogical Reasoning." Contemporary Psychology 44, no. 6 (December 1999): 461–63. http://dx.doi.org/10.1037/002095.

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6

KARA, LEVENT BURAK, and THOMAS F. STAHOVICH. "Causal reasoning using geometric analysis." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, no. 5 (November 2002): 363–84. http://dx.doi.org/10.1017/s0890060402165036.

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We describe an approach that uses causal and geometric reasoning to construct explanations for the purposes of the geometric features on the parts of a mechanical device. To identify the purpose of a feature, the device is simulated with and without the feature. The simulations are then translated into a “causal-process” representation, which allows qualitatively important differences to be identified. These differences reveal the behaviors caused and prevented by the feature and thus provide useful cues about the feature's purpose. A clear understanding of the feature's purpose, however, requires a detailed analysis of the causal connections between the caused and prevented behaviors. This presents a significant challenge because one has to understand how a behavior that normally takes place affects (or is affected by) another behavior that is normally absent. This article describes techniques for identifying such elusive relationships. These techniques employ a set of rules that can determine if one behavior enables or disables another that is spatially and temporally far away. They do so by geometrically examining the traces of the causal processes in the device's configuration space. Using the results of this analysis, our program can automatically generate text output describing how the feature performs its function.
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7

Cockcroft, Peter D. "Clinical Reasoning and Decision Analysis." Veterinary Clinics of North America: Small Animal Practice 37, no. 3 (May 2007): 499–520. http://dx.doi.org/10.1016/j.cvsm.2007.01.011.

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8

Sukirwan, Darhim, and T. Herman. "Analysis of students’ mathematical reasoning." Journal of Physics: Conference Series 948 (January 2018): 012036. http://dx.doi.org/10.1088/1742-6596/948/1/012036.

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9

Sirovich, L. "Probabilistic Reasoning in Data Analysis." Science Signaling 4, no. 192 (September 20, 2011): tr14. http://dx.doi.org/10.1126/scisignal.2001980.

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10

Ringer, Fritz K. "Causal Analysis in Historical Reasoning." History and Theory 28, no. 2 (May 1989): 154. http://dx.doi.org/10.2307/2505033.

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11

Orłowska, Ewa. "Semantic analysis of inductive reasoning." Theoretical Computer Science 43 (1986): 81–89. http://dx.doi.org/10.1016/0304-3975(86)90167-2.

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12

Et al., Ishaq Nuriadin. "Analysis of Students Mathematical Reasoning Abilities on Number Topics." Psychology and Education Journal 58, no. 1 (January 15, 2021): 4750–55. http://dx.doi.org/10.17762/pae.v58i1.1634.

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Mathematical reasoning skills are critical for students to solve math problems. The research objective was to determine the reasoning ability of junior high school students in solving number problems. This research is a qualitative descriptive study with case studies. The research subjects were 27 students consisting of 14 female students and 13 male students. A total of 10 questions were tested containing five multiple-choice questions, and five description questions. From the results of data analysis, 3% of students had high reasoning abilities, 43% of students had moderate reasoning abilities, 30% of students had low reasoning abilities, 13% of students had meagre reasoning abilities.
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13

Pearce, Roger S. "Assessing Analysis and Reasoning in Bioethics." Bioscience Education 12, no. 1 (December 2008): 1–5. http://dx.doi.org/10.3108/beej.12.c2.

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14

Sin, Bo Mi. "An Analysis of Teachers' Statistical Reasoning." Journal of Curriculum and Evaluation 21, no. 2 (May 2018): 103–28. http://dx.doi.org/10.29221/jce.2018.21.2.103.

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15

Pei, Daowu, and Aiying Zhang. "Truth degree analysis of fuzzy reasoning." Journal of Intelligent & Fuzzy Systems 26, no. 3 (2014): 1439–52. http://dx.doi.org/10.3233/ifs-130828.

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16

Hall, Andreas, Paula Ahonen-Rainio, and Kirsi Virrantaus. "Knowledge and Reasoning in Spatial Analysis." Transactions in GIS 18, no. 3 (August 14, 2013): 464–76. http://dx.doi.org/10.1111/tgis.12049.

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17

Spurgeon, K., W. H. Tang, Z. J. Richardson, Q. H. Wu, and G. Moss. "Dissolved gas analysis using evidential reasoning." IEE Proceedings - Science, Measurement and Technology 152, no. 3 (May 1, 2005): 110–17. http://dx.doi.org/10.1049/ip-smt:20049029.

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18

Shih-Chieh Chang, Wen-Ben Jone, and Shi-Sen Chang. "TAIR: testability analysis by implication reasoning." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 19, no. 1 (2000): 152–60. http://dx.doi.org/10.1109/43.822627.

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19

HIRSCH, DAVID E., SHELDON R. SIMON, TOM BYLANDER, MICHAEL A. WEINTRAUB, and PETER SZOLOVITS. "USING CAUSAL REASONING IN GAIT ANALYSIS." Applied Artificial Intelligence 3, no. 2-3 (January 1989): 337–56. http://dx.doi.org/10.1080/08839518908949930.

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20

ATTARDO, D. H. "Reasoning-Based Heuristics for Meaning Analysis." Literary and Linguistic Computing 7, no. 1 (January 1, 1992): 48–63. http://dx.doi.org/10.1093/llc/7.1.48.

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21

Gao, Jin Sheng, and Chang Le Zhou. "A reasoning system for image analysis." MATEC Web of Conferences 63 (2016): 04031. http://dx.doi.org/10.1051/matecconf/20166304031.

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22

Kezunovic, M., I. Rikalo, C. W. Fromen, and D. R. Sevcik. "Expert system reasoning streamlines disturbance analysis." IEEE Computer Applications in Power 7, no. 2 (April 1994): 15–19. http://dx.doi.org/10.1109/67.273781.

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23

Lo, Kueiming, Chongyuan Yin, and Jian Luo. "Satisfiability Degree Analysis and Deductive Reasoning." IEEE Intelligent Systems 31, no. 4 (July 2016): 30–42. http://dx.doi.org/10.1109/mis.2016.62.

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24

Mirza, Noeman A., Noori Akhtar-Danesh, Charlotte Noesgaard, Lynn Martin, and Eric Staples. "A concept analysis of abductive reasoning." Journal of Advanced Nursing 70, no. 9 (March 13, 2014): 1980–94. http://dx.doi.org/10.1111/jan.12379.

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25

Lisciandra, Chiara. "Robustness analysis versus reliable process reasoning." Metascience 24, no. 1 (November 14, 2014): 37–41. http://dx.doi.org/10.1007/s11016-014-9927-2.

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26

Armando, Alessandro, David Basin, Jorge Cuellar, Michaël Rusinowitch, and Luca Viganò. "Automated Reasoning for Security Protocol Analysis." Journal of Automated Reasoning 36, no. 1-2 (December 8, 2005): 1–3. http://dx.doi.org/10.1007/s10817-005-9014-x.

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27

Cerutti, Federico, Matthias Thimm, and Mauro Vallati. "An experimental analysis on the similarity of argumentation semantics." Argument & Computation 11, no. 3 (November 10, 2020): 269–304. http://dx.doi.org/10.3233/aac-200907.

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In this paper we ask whether approximation for abstract argumentation is useful in practice, and in particular whether reasoning with grounded semantics – which has polynomial runtime – is already an approximation approach sufficient for several practical purposes. While it is clear from theoretical results that reasoning with grounded semantics is different from, for example, skeptical reasoning with preferred semantics, we investigate how significant this difference is in actual argumentation frameworks. As it turns out, in many graphs models, reasoning with grounded semantics actually approximates reasoning with other semantics almost perfectly. An algorithm for grounded reasoning is thus a conceptually simple approximation algorithm that not only does not need a learning phase – like recent approaches – but also approximates well – in practice – several decision problems associated to other semantics.
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28

Ha, Minsu, Yustika Sya’bandari, Ai Nurlaelasari Rusmana, Rahmi Qurota Aini, and Sarah Meilani Fadillah. "COMPREHENSIVE ANALYSIS OF THE FORT INSTRUMENT: USING DISTRACTOR ANALYSIS TO EXPLORE STUDENTS’ SCIENTIFIC REASONING BASED ON ACADEMIC LEVEL AND GENDER DIFFERENCE." Journal of Baltic Science Education 20, no. 6 (December 10, 2021): 906–23. http://dx.doi.org/10.33225/jbse/21.20.906.

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Scientific reasoning ability is essential to get developed in the current digital age, particularly in the process of judgement and decision-making in complex problems. Few studies have conducted an in-depth exploration of scientific reasoning ability, especially in relation to the confidence level and gender. The scientific reasoning ability of Indonesian upper-secondary school and university students were examined and compared with previous recorded data of US students. In this study, the data were collected from 372 university and 528 upper-secondary education students in Indonesia. Students’ scientific reasoning ability was measured using a scientific formal reasoning test (FORT). In addition, confidence level and metacognitive data was collected through self-reported measures. Two-way ANOVA was performed to compare mean differences between groups based on academic level and gender and to observe interaction between the variables. Students’ confidence level in selecting the correct answer and distractor answer was analyzed using an independent t-test. The results reveal that many Indonesian students selected specific distractors with relatively high confidence. Moreover, upper-secondary school students and female students selected more distractors than the groups’ counterparts. Finally, the factors related to Indonesian students’ responses to the scientific formal reasoning were discussed. Keywords: confidence level, distractor analysis, gender differences, scientific (formal) reasoning test, scientific reasoning ability, Indonesian student
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29

Sari, Elok Kartika, and Rooselyna Ekawati. "Analysis of Primary Students’ Spatial Literacy on Reasoning." Jurnal Riset Pendidikan dan Inovasi Pembelajaran Matematika (JRPIPM) 2, no. 1 (April 4, 2019): 15. http://dx.doi.org/10.26740/jrpipm.v2n1.p015-022.

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Spatial thinking is critical to mathematical thinking and achievement, important not only in mathematics learning and but also important in education research. In order to have similar opportunity in supporting primary students’ spatial literacy on reasoning, it needs a clearer picture of primary students’ spatial literacy on reasoning in solving Mathematical Literacy Tasks (MLT) as the aim of this study. The characteristic of MLT satisfies the PISA framework and considering Context, Content (shape and space), Process as well as level of problem. Researcher only focuses on three MLT problems. The result is about description of analyzing three participants’ solution of MLT based on how they use kinds of spatial literacy on reasoning strategies. Dimensional reasoning is effective but the students didn’t use it often, the students seem like unfamiliar with these strategies. The responses suggest that because of the teacher didn’t use dimensional strategies in the class, so the student commonly didn’t use it often. Therefore, students need experiences that supporting their reasoning of spatial objects and its relationship and teacher can help students by using learning models, methods, or strategies in the class so their mathematical literacy (spatial literacy on reasoning) better than before.
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30

Alaattin Pusmaz, Özlem Engin,. "An Analysis of High School Student's Understanding and Reasoning of Average Concept." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1 (February 5, 2021): 187–201. http://dx.doi.org/10.17762/turcomat.v12i1.273.

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The aim of this study is to identify high school student's understanding of average concept and the reasoning types they appeal to solve average problems. The case study approach was used in this study and the participants were selected by purposeful sampling. The participants consisted of five 9th grade and four 10th grade students, studying at a high school in Istanbul. In order to identify student's understanding of average, a test consisting of 5 open-ended problems were used and semi-structured interviews were held with each of the students. The data were analyzed by thematic analysis approach. For data analysis, framework proposed by Mokros and Russel (1995) was used to determine student's understanding of average and Lithner's (2008) framework was used to reveal their reasoning types. Results showed that students mostly understood average as mathematical point of balance. Creative mathematically founded reasoning and algorithmic reasoning was used the most. Creative reasoning is effective in reaching the right answer. In solutions where creative reasoning is used, students generally also have the idea of representativeness. The type of problem influences the reasoning process. Inadequacy of student's prior mathematics knowledge hinders both their understanding of the average and their reasoning skills.
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31

Nurdin, Nurdin, Ita Sarmita Samad, and Sardia Sardia. "LOGICAL REASONING ANALYSIS BASED ON HIPPOCRATES PERSONALITY TYPES." Aksioma 9, no. 2 (September 25, 2020): 57–73. http://dx.doi.org/10.22487/aksioma.v9i2.219.

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Abstract: The theory distinguishes human based on four different personality types such as: sanguine, choleric, melancholic, and phlegmatic. Different types of personality caused by differences in the dominant fluid in the body. These differences will result in terms of behavior, ways of thinking and to get along. The type of this research that is descriptive qualitative which it is describing the logical reasoning based on Hippocrates personality types. The logical reasoning is analyzed through the four types of personality in relation to mathematical problem solving. The Analysis is done based on the logical reasoning indicator/ subindicator and the steps of problem solving stated by Polya. The result shows that there is a reasoning difference on each type of personalities. The difference can be terms of the strenght or the weakness. Sanguine is quicker in understanding problems and communicating results, choleric is more accelerated in work, melancholic is more perfect at work, and phlegmatic is superior in terms of accuracy. Keywords: Logical reasoning, Hippocrates, sanguine, choleric, melancholic, phlegmatic
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32

Alexander, Perry. "Task Analysis and Design Plans in Formal Specification Design." International Journal of Software Engineering and Knowledge Engineering 08, no. 02 (June 1998): 223–52. http://dx.doi.org/10.1142/s0218194098000133.

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This paper presents BENTON, a prototype system demonstrating task analysis and multi-agent reasoning applied to formal specification synthesis. BENTON transforms specifications written as attribute-value pairs into Larch Modula-3 interface language and Larch Shared Language specifications. BENTON decomposes the software specification design task into synthesis, analysis and evaluation subtasks. Each subtask is assigned a specific design method based on problem and domain characteristics. This task analysis is achieved using blackboard knowledge sources and multi-agent reasoning employing design plans to implement different problem solving methods. Knowledge sources representing different problem solving methodologies monitor blackboard spaces and activate when they are applicable. When executed, Design plans send subtasks to agents that select from available problem solving methodologies. BENTON agents and knowledge sources use case-based reasoning, schemata-based reasoning and procedure execution as their fundamental reasoning methods. This paper presents an overview of the BENTON design model, its agent architecture and plan execution capabilities, and two annotated examples of BENTON problem solving activities.
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33

Da Rocha, José Carlos Ferreira, Alaine M. Guimarães, and Valter L. Estevam Jr. "Probabilistic logic reasoning for subjective interestingness analysis." Revista Brasileira de Computação Aplicada 11, no. 1 (April 15, 2019): 59–66. http://dx.doi.org/10.5335/rbca.v11i1.8820.

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This paper presents an approach that uses probabilistic logic reasoning to compute subjective interestingness scores for classification rules. In the proposed approach, domain knowledge is represented as a probabilistic logic program that encodes information from experts and statistical reports. The computation of interestingness scores is performed by a procedure that applies linear programming to reasoning regarding the probabilities of interest. It provides a mechanism to calculate probability-based subjective interestingness scores. Further, a sample application illustrates the use of the described approach.
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34

Khoeriah, Ina Ana, Irvan Permana, and Didit Ardianto. "Science Reasoning: A Review and Bibliometric Analysis." Jurnal Penelitian Pendidikan IPA 8, no. 2 (April 30, 2022): 423–28. http://dx.doi.org/10.29303/jppipa.v8i2.1135.

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Improving critical thinking in science classes can be accomplished by developing reasoning thinking approaches. The goal of studying science reasoning in science classes is to see how much of this strategy is used in solving science problems and developing Higher Order Thinking Skills (HOTS) that are based on critical thinking. Between 2015 and 2021, a total of 100 articles were retrieved from the Google Scholar database and the Publishing or Perish (PoP) program. Mendeley desktop, a referencing management software, was then used to handle the selected references. Once the database was handled, this research employed VOS viewer software to categorize and visualize it. In general, this study provides a solid foundation for further research into "science reasoning."
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35

Schlatter, Erika, Inge Molenaar, and Ard W. Lazonder. "Learning scientific reasoning: A latent transition analysis." Learning and Individual Differences 92 (December 2021): 102043. http://dx.doi.org/10.1016/j.lindif.2021.102043.

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36

ÇOBAN, Halil, and Erdoğan TEZCİ. "Mathematical Reasoning: Bibliometric Analysis of the Literature." OPUS Journal of Society Research 19, no. 45 (January 27, 2022): 1. http://dx.doi.org/10.26466/opusjsr.1062867.

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37

Kawamura, H., A. Tani, H. Naknao, and M. Yamada. "Critical Analysis of Strucutres by Qualitative Reasoning." Computer-Aided Civil and Infrastructure Engineering 7, no. 2 (March 1992): 131–42. http://dx.doi.org/10.1111/j.1467-8667.1992.tb00424.x.

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38

Cambria, Erik, Amir Hussain, and Alessandro Vinciarelli. "Affective reasoning for big social data analysis." IEEE Transactions on Affective Computing 8, no. 4 (October 1, 2017): 426–27. http://dx.doi.org/10.1109/taffc.2017.2763218.

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39

Bosse, Tibor, Catholijn M. Jonker, and Jan Treur. "Formalization and Analysis of Reasoning by Assumption." Cognitive Science 30, no. 1 (January 2, 2006): 147–80. http://dx.doi.org/10.1207/s15516709cog0000_51.

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40

Pratami, A. R., T. Widhiyanti, and A. Widodo. "Analysis on senior high school’s reasoning skill." Journal of Physics: Conference Series 1157 (February 2019): 022027. http://dx.doi.org/10.1088/1742-6596/1157/2/022027.

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41

Dillig, Isil, Thomas Dillig, and Alex Aiken. "Reasoning about the unknown in static analysis." Communications of the ACM 53, no. 8 (August 2010): 115–23. http://dx.doi.org/10.1145/1787234.1787259.

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42

Chaturvedi, Iti, Ranjan Satapathy, Sandro Cavallari, and Erik Cambria. "Fuzzy commonsense reasoning for multimodal sentiment analysis." Pattern Recognition Letters 125 (July 2019): 264–70. http://dx.doi.org/10.1016/j.patrec.2019.04.024.

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43

Lee, M. H. "Qualitative circuit models in failure analysis reasoning." Artificial Intelligence 111, no. 1-2 (July 1999): 239–76. http://dx.doi.org/10.1016/s0004-3702(99)00032-6.

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44

Wos, Larry. "The problem of reasoning by case analysis." Journal of Automated Reasoning 11, no. 2 (1993): 289–91. http://dx.doi.org/10.1007/bf00881909.

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45

KÖNIG, ESTHER. "A Hypothetical Reasoning Algorithm for Linguistic Analysis." Journal of Logic and Computation 4, no. 1 (1994): 1–19. http://dx.doi.org/10.1093/logcom/4.1.1.

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46

Byrnes, James P., and Willis F. Overton. "Reasoning about logical connectives: A developmental analysis." Journal of Experimental Child Psychology 46, no. 2 (October 1988): 194–218. http://dx.doi.org/10.1016/0022-0965(88)90057-4.

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47

Daya Sagar, B. S., and Jean Serra. "Spatial information retrieval, analysis, reasoning and modelling." International Journal of Remote Sensing 31, no. 22 (December 4, 2010): 5747–50. http://dx.doi.org/10.1080/01431161.2010.512315.

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48

Xia, Meimei, Jian Chen, and Xiao‐Jun Zeng. "Data envelopment analysis based on team reasoning." International Transactions in Operational Research 27, no. 2 (August 28, 2017): 1080–100. http://dx.doi.org/10.1111/itor.12447.

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49

Reed, Nancy E., and Paul E. Johnson. "Analysis of expert reasoning in hardware diagnosis." International Journal of Man-Machine Studies 38, no. 2 (February 1993): 251–80. http://dx.doi.org/10.1006/imms.1993.1012.

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50

Zhang, Lei, and Kai-Yuan Cai. "Optimal fuzzy reasoning and its robustness analysis." International Journal of Intelligent Systems 19, no. 11 (2004): 1033–49. http://dx.doi.org/10.1002/int.20035.

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