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Journal articles on the topic 'Logic evaluation'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

STÄRK, ROBERT F. "Why the constant ‘undefined’? Logics of partial terms for strict and non-strict functional programming languages." Journal of Functional Programming 8, no. 2 (March 1998): 97–129. http://dx.doi.org/10.1017/s0956796898002974.

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In this article we explain two different operational interpretations of functional programs by two different logics. The programs are simply typed λ-terms with pairs, projections, if-then-else and least fixed point recursion. A logic for call-by-value evaluation and a logic for call-by-name evaluation are obtained as as extensions of a system which we call the basic logic of partial terms (BPT). This logic is suitable to prove properties of programs that are valid under both strict and non-strict evaluation. We use methods from denotational semantics to show that the two extensions of BPT are adequate for call-by-value and call-by-name evaluation. Neither the programs nor the logics contain the constant ‘undefined’.

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2

Greene, Jennifer C. "Logic and evaluation theory." Evaluation and Program Planning 38 (June 2013): 71–73. http://dx.doi.org/10.1016/j.evalprogplan.2012.03.017.

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3

Ozeki, Satoshi, Chris L. S. Coryn, and Daniela C. Schröter. "Evaluation logic in practice." Evaluation and Program Planning 76 (October 2019): 101681. http://dx.doi.org/10.1016/j.evalprogplan.2019.101681.

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4

T. Kamol, Saydazimov. "Problem of Explanation and Understanding in the Evaluation Logic." International Journal of Psychosocial Rehabilitation 24, Special Issue 1 (February 28, 2020): 966–73. http://dx.doi.org/10.37200/ijpr/v24sp1/pr201241.

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5

Scriven, Michael. "The logic of evaluation and evaluation practice." New Directions for Evaluation 1995, no. 68 (September 1995): 49–70. http://dx.doi.org/10.1002/ev.1019.

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6

Renger, Ralph, Shandiin Wood, Simon Williamson, and Stefanie Krapp. "Systemic Evaluation, Impact Evaluation and Logic Models." Evaluation Journal of Australasia 11, no. 2 (September 2011): 24–30. http://dx.doi.org/10.1177/1035719x1101100204.

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7

Deborah, Lazarus Jegatha, Ramachandran Baskaran, and Arputharaj Kannan. "Deontic Logic Based Ontology Alignment Technique for E-Learning." International Journal of Intelligent Information Technologies 8, no. 3 (July 2012): 56–72. http://dx.doi.org/10.4018/jiit.2012070104.

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The recent explosion in web services usage and information technology has led to the challenging issue of Ontology Construction and Alignment in order to enhance the semantics of web documents in E-Learning scenarios. In such a circumstance, it is necessary to provide an effective solution for Ontology Alignment that can help in student performance evaluation. The authors propose a rule-based solution for evaluating the students using Ontology Alignment techniques. For this purpose, they make use of the rules based on Deontic Logic and hence make a positive attempt in analyzing the presence of some of the non-dominant categorization of words like determiners, time clauses and modal verbs occurring in the text documents using Standard Deontic rules. The experiments have been carried out on evaluating the learning of C programming language using an E-Learning framework. From these evaluations, it has been observed that the proposed evaluation technique improves the accuracy of student evaluation significantly.

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8

Moggi, Eugenio. "A SEMANTICS FOR EVALUATION LOGIC." Fundamenta Informaticae 22, no. 1,2 (1995): 117–52. http://dx.doi.org/10.3233/fi-1995-22126.

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Lin, Ching-Torng, Hero Chiu, and Yi-Hong Tseng. "Agility evaluation using fuzzy logic." International Journal of Production Economics 101, no. 2 (June 2006): 353–68. http://dx.doi.org/10.1016/j.ijpe.2005.01.011.

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DOURRA, HUSSEIN, and PEPE SIY. "STOCK EVALUATION USING FUZZY LOGIC." International Journal of Theoretical and Applied Finance 04, no. 04 (August 2001): 585–602. http://dx.doi.org/10.1142/s0219024901001188.

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We use fuzzy logic engineering tools to detect human behavior in the finance arena, specifically in the technical analysis field. Since technical analysis theory consists of indicators used by experts to evaluate stock prices, the new proposed method maps these indicators into new inputs that can be fed into a fuzzy logic system. This system can create an optimum computerized model to evaluate stock price movement. This method relies on human psychology to predict human behavior when certain price movements or certain price formations occur. The success of the system is measured by comparing system output versus stock price movement. The new stock evaluation method is proven to exceed market performance and it can be an excellent tool in the technical analysis field. The flexibility of the system is also demonstrated.

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11

Scriven, Michael, and Chris L. S. Coryn. "The logic of research evaluation." New Directions for Evaluation 2008, no. 118 (2008): 89–105. http://dx.doi.org/10.1002/ev.263.

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12

Brousselle, Astrid, and François Champagne. "Program theory evaluation: Logic analysis." Evaluation and Program Planning 34, no. 1 (February 2011): 69–78. http://dx.doi.org/10.1016/j.evalprogplan.2010.04.001.

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13

Picciotto, Robert. "The logic of evaluation professionalism." Evaluation 17, no. 2 (April 2011): 165–80. http://dx.doi.org/10.1177/1356389011403362.

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14

Lloyd, J. W., and J. C. Shepherdson. "Partial evaluation in logic programming." Journal of Logic Programming 11, no. 3-4 (October 1991): 217–42. http://dx.doi.org/10.1016/0743-1066(91)90027-m.

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15

Luis Roca, J. "Reliability evaluation of logic circuits." Microelectronics Reliability 25, no. 2 (January 1985): 257–60. http://dx.doi.org/10.1016/0026-2714(85)90009-5.

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16

Meng, Shang, Pei Feng Wang, and Ji Chao Wang. "Application of Fuzzy Logic in the Network Security Risk Evaluation." Advanced Materials Research 282-283 (July 2011): 359–62. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.359.

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This paper introduces the development history of fuzzy logic and its application in network security evaluation. The main content includes fuzzy comprehensive method of evaluating network security risk, and entropy weight coefficient method in network security risk evaluation. At last, it points that the application of fuzzy logic in network security evaluation is a new research field and many problems remain to be solved.

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17

Morell, Jonathan A. "Linking Management and Evaluation: Project Schedules as Program Models." American Journal of Evaluation 40, no. 4 (August 21, 2018): 472–89. http://dx.doi.org/10.1177/1098214018784198.

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Project schedules are logic models that focus on the timing of program activities. Value derives from the fact that schedule changes are not random. Why they occur, and how long they last, can reveal information that would not be easily revealed with other approaches to evaluation. Also, using project schedules as logic models forges a strong link between evaluation and program management. This article addresses three questions: How do models function in research? What makes project schedules a form of logic model? and How can evaluation data be gleaned from schedules? When using implementation schedules as a logic model, two guidelines must be observed. First, data collection must take place at regular intervals rather than be synchronized to program activities. Second, contact with stakeholders must respect their time commitments. The methodology will be illustrated with a case example from evaluating a “Peer to Peer” program to minimize unsafe electronic device use.

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18

MAHER, MICHAEL J., ANDREW ROCK, GRIGORIS ANTONIOU, DAVID BILLINGTON, and TRISTAN MILLER. "EFFICIENT DEFEASIBLE REASONING SYSTEMS." International Journal on Artificial Intelligence Tools 10, no. 04 (December 2001): 483–501. http://dx.doi.org/10.1142/s0218213001000623.

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For many years, the non-montonic reasoning community has focussed on highly expressive logics. Such logics have turned out to be computationally expensive, and have given little support to the practical use of non-monotonic reasoning. In this work we discuss defeasible logic, a less-expressive but more efficient non-monotonic logic. We report on two new implemented systems for defeasible logic: a query answering system employing a backward-chaining approach, and a forward-chaining implementation that computes all conclusions. Our experimental evaluation demonstrates that the systems can deal with large theories (up to hundreds of thousands of rules). We show that defeasible logic has linear complexity, which contrasts markedly with most other non-monotonic logics and helps to explain the impressive experimental results. We believe that defeasible logic, with its efficiency and simplicity, is a good candidate to be used as a modeling language for practical applications, including modelling of regulations and business rules.

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19

ARECES, CARLOS, DIEGO FIGUEIRA, SANTIAGO FIGUEIRA, and SERGIO MERA. "THE EXPRESSIVE POWER OF MEMORY LOGICS." Review of Symbolic Logic 4, no. 2 (June 2011): 290–318. http://dx.doi.org/10.1017/s1755020310000389.

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We investigate the expressive power of memory logics. These are modal logics extended with the possibility to store (or remove) the current node of evaluation in (or from) a memory, and to perform membership tests on the current memory. From this perspective, the hybrid logic ℋℒ (↓), for example, can be thought of as a particular case of a memory logic where the memory is an indexed list of elements of the domain.This work focuses in the case where the memory is a set, and we can test whether the current node belongs to the set or not. We prove that, in terms of expressive power, the memory logics we discuss here lie between the basic modal logic ${\cal K}$ and ℋℒ (↓). We show that the satisfiability problem of most of the logics we cover is undecidable. The only logic with a decidable satisfiability problem is obtained by imposing strong constraints on which elements can be memorized.

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20

Letts, Lori, and Lynda Dunal. "Tackling Evaluation: Applying a Programme Logic Model to Community Rehabilitation for Adults with Brain Injury." Canadian Journal of Occupational Therapy 62, no. 5 (December 1995): 268–77. http://dx.doi.org/10.1177/000841749506200506.

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Programme evaluation has become an important component of many rehabilitation initiatives in both institutional and community environments. Of key importance is a need for information on developing an evaluation framework to determine and address programme processes and outcomes. This paper presents a programme logic model used to guide the evaluation of a community rehabilitation programme for adults with brain injury. Programme logic models are visual models that provide a framework to organize and integrate information about programme inputs, processes and outcomes. The model is also used in this programme to incorporate the client-centred values of the programme. It was developed through the consensus of the entire team, and was used to plan implementation and outcome evaluations. A similar programme logic model will be useful to occupational therapists and other health care professionals planning programme evaluations of their services.

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21

Ng, K. W., and K. T. Lau. "Improved PAL-2N logic with complementary pass-transistor logic evaluation tree." Microelectronics Journal 31, no. 1 (January 2000): 55–59. http://dx.doi.org/10.1016/s0026-2692(99)00089-0.

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22

GÜRSEL, Güney, and Kemal Hakan GÜLKESEN. "CONTRIBUTION OF FUZZY LOGIC TO EVALUATION." AJIT-e Online Academic Journal of Information Technology 8, no. 27 (May 15, 2017): 7–18. http://dx.doi.org/10.5824/1309-1581.2017.2.001.x.

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23

Barlybayev, Alibek, Altynbek Sharipbay, Gulden Ulyukova, Talgat Sabyrov, and Batyrkhan Kuzenbayev. "Student's Performance Evaluation by Fuzzy Logic." Procedia Computer Science 102 (2016): 98–105. http://dx.doi.org/10.1016/j.procs.2016.09.375.

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24

Mammadli, Sadig. "Fuzzy Logic Based Loan Evaluation System." Procedia Computer Science 102 (2016): 495–99. http://dx.doi.org/10.1016/j.procs.2016.09.433.

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25

Antoy, Sergio. "Evaluation Strategies for Functional Logic Programming." Electronic Notes in Theoretical Computer Science 57 (December 2001): 1–16. http://dx.doi.org/10.1016/s1571-0661(04)00265-8.

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26

Nahman, J. "Fuzzy logic based network reliability evaluation." Microelectronics Reliability 37, no. 8 (August 1997): 1161–64. http://dx.doi.org/10.1016/s0026-2714(97)00018-8.

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27

Antoy, Sergio. "Evaluation strategies for functional logic programming." Journal of Symbolic Computation 40, no. 1 (July 2005): 875–903. http://dx.doi.org/10.1016/j.jsc.2004.12.007.

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28

Alpuente, María, Moreno Falaschi, and Germán Vidal. "Partial evaluation of functional logic programs." ACM Transactions on Programming Languages and Systems 20, no. 4 (July 1998): 768–844. http://dx.doi.org/10.1145/291891.291896.

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29

Blair, J. Anthony. "Informal logic and reasoning in evaluation." New Directions for Evaluation 1995, no. 68 (September 1995): 71–80. http://dx.doi.org/10.1002/ev.1020.

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30

Halabi, Dana, and Adnan Shaout. "Website Tenders Evaluation Using Fuzzy Logic." International Journal of Information Systems in the Service Sector 11, no. 2 (April 2019): 32–50. http://dx.doi.org/10.4018/ijisss.2019040103.

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Developing a new website for a government institution is one of common tenders issued by various ministries and government institutions in the world. The country of Jordan usually has at least 22 different ministries and government institutions at a given time. Each one of these entities has its official website which is revamped every three years because of the rapid changes in information and technology. In most cases the ministries and institutions issue a tender to the public, then after collecting tender offers from different companies, only one offer would be selected. The selection process would choose the most appropriate contractor to deliver the project with respect to quality, time and cost. This article presents a new fuzzy logic system for tender evaluation which is based on both the technical qualification of the bidder company and its experience. The proposed system has shown better performance evaluation when compared to the traditional evaluation method currently used by the government.

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31

Dujmovic, Jozo J. "Continuous Preference Logic for System Evaluation." IEEE Transactions on Fuzzy Systems 15, no. 6 (December 2007): 1082–99. http://dx.doi.org/10.1109/tfuzz.2007.902041.

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32

Arnold, Mary E., and Brooke D. Nott. "What’s Going On? Developing Program Theory for Evaluation." Journal of Youth Development 5, no. 2 (June 1, 2010): 71–82. http://dx.doi.org/10.5195/jyd.2010.222.

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This paper outlines the process undertaken to develop a program logic model into a program theory that could be tested through a large scale evaluation. Logic models are widely promoted to establish program intention and outcomes, thus setting the stage for effective program evaluation. However, the causal links in program logic models are often assumptive in nature, and lack the specificity of conditions needed in order for the program to achieve its planned success. The authors share a process of observation and focus groups that allowed them to probe some of the links of the program logic model. The result presented is an “enhanced” logic model that reveals possible moderators of program success, which leads to the development of evaluation questions that will be used in a subsequent large-scale evaluation. The value of using an enhanced logic model for program evaluation leading to program policy changes and improvement is underscored.

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Befani, Barbara, Corinna Elsenbroich, and Jen Badham. "Diagnostic evaluation with simulated probabilities." Evaluation 27, no. 1 (January 2021): 102–15. http://dx.doi.org/10.1177/1356389020980476.

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As policy makers require more rigorous assessments for the strength of evidence in Theory-Based evaluations, Bayesian logic is attracting increasing interest; however, the estimation of probabilities that this logic (almost) inevitably requires presents challenges. Probabilities can be estimated on the basis of empirical frequencies, but such data are often unavailable for most mechanisms that are objects of evaluation. Subjective probability elicitation techniques are well established in other fields and potentially applicable, but they present potential challenges and might not always be feasible. We introduce the community to a third way: simulated probabilities. We provide proof of concept that simulation can be used to estimate probabilities in diagnostic evaluation and illustrate our case with an application to health policy.

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34

Oliveira, Sonja, and Martin Sexton. "Conflict, contradiction, and concern: judges' evaluation of sustainability in architectural awards." Architectural Research Quarterly 20, no. 4 (December 2016): 325–32. http://dx.doi.org/10.1017/s1359135517000069.

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This paper explores how judges evaluate sustainability of buildings in RIBA award settings in the UK. A qualitative approach drawing on institutional theory is used to understand the ways judges legitimate particular evaluative views. Aesthetics and Sustainability focused logics are found to guide the evaluative legitimation process. An Aesthetics focused logic is characterised by reliance on expertise, ‘professionalism’ and perceptions of fair practice, whereas a Sustainability focused logic prioritises moral responsibility, scientific evidence and personal experience. Evaluating sustainability was characterised by conflict, concern and contradiction between and within the logics. Evaluation here is seen as a process of constant negotiation between conflicting priorities and managing wider expectations. Different perceptions of what counts in terms of judges’ understandings of legitimacy whether from the profession or wider society were found to provide a way of compliance or commitment to a particular evaluative view of sustainability.Implications of the findings are threefold. First, the analysis shows a way of studying sustainability evaluation in the architectural domain that shifts the literature's dominant focus on technical features to a consideration of the social context, the profession, awards and other evaluative issues including aesthetics. Second, the discussion draws attention to the importance of examining understandings of sustainability evaluation in view of the legitimacy sources judges draw on from the profession, the awards or wider society to justify particular decisions. Finally the study reveals some of the often overlooked difficulties in evaluating sustainability including negotiating conflicts between competing values including aesthetics versus sustainability, challenges in judging scientific evidence and the need for expert legitimation regarding sustainability decisions. The paper concludes with a discussion of the contribution of empirical and theoretical analysis of sustainability evaluation for research in awards as well as the built environment more generally including the study of uncertainty in evaluative design practice.

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35

Franchella, Miriam. "Towards a Re-Evaluation of Julius König's Contribution to Logic." Bulletin of Symbolic Logic 6, no. 1 (March 2000): 45–66. http://dx.doi.org/10.2307/421075.

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AbstractJulius König is famous for his mistaken attempt to demonstrate that the continuum hypothesis was false. It is also known that the only positive result that could have survived from his proof is the paradox which bears his name. Less famous is his 1914 book Neue Grundlagen der Logik, Arithmetik und Mengenlehre. Still, it contains original contributions to logic, like the concept of metatheory and the solution of paradoxes based on the refusal of the law of bivalence. We are going to discover them by analysing the content of the book.

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36

Mamakou, Xenia J., Dimitris K. Kardaras, and Eleutherios A. Papathanassiou. "Evaluation of websites’ compliance to legal and ethical guidelines: A fuzzy logic–based methodology." Journal of Information Science 44, no. 4 (March 1, 2017): 425–42. http://dx.doi.org/10.1177/0165551517697610.

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Privacy issues are a top priority in web design. However, websites’ evaluation methods do not consider legal and ethical issues. This article proposes a fuzzy logic–based methodology for evaluating websites’ compliance with legal and ethical principles. Using fuzzy Delphi and fuzzy numbers, the methodology develops the Fuzzy Legal and Ethical Compliance Index (FLECI) that addresses the inherited vagueness of the evaluation process and calculates websites’ conformity to legal and ethical guidelines. To illustrate the proposed methodology, this research collects data and then evaluates and classifies 100 websites with respect to their privacy policies using fuzzy equivalence. This article provides a foundation for the development of comprehensive website evaluation methods that include privacy and ethical issues in their evaluations. Future research can investigate the applicability of the proposed methodology and the fuzzy numbers calculated in this article in websites across industries and cultural activities.

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37

Snow, M. Elizabeth, and Nancy Snow. "Interactive Logic Models: Using Design and Technology to Explore the Effects of Dynamic Situations on Program Logic." Evaluation Journal of Australasia 17, no. 2 (June 2017): 20–28. http://dx.doi.org/10.1177/1035719x1701700204.

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Logic models are commonly used by evaluators to illustrate relationships among a program's inputs, activities, outputs, and outcomes. They are useful in helping intended users develop programs, communicate a program's theory of change, and design evaluations. However, as static documents, logic models can be challenging to build, work with, and present to stakeholders, especially for large and complex programs. Challenged by this inadequacy of static logic models, a program evaluator turned to a graphic designer and a software developer for help. Together, this interdisciplinary group developed web-based software (Dylomo) that allows evaluators to create logic models that better communicate the logic within the model. In this paper, we describe the process by which this interdisciplinary group created this new technology—including a user-testing experience at the Canadian Evaluation Society Conference in Canada in June 2016—to build and present logic models that use interactivity and allow program evaluators to more easily demonstrate the logic within a complex program and to visually explore the potential effects of changes within the program's landscape. This software is freely available on the web, so readers can apply it to their own evaluation practice.

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38

ZUO, Jing-long, De-long CUI, and Gui-lan YU. "Credit evaluation model based on fuzzy logic." Journal of Computer Applications 29, no. 12 (February 25, 2010): 3201–3. http://dx.doi.org/10.3724/sp.j.1087.2009.03201.

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39

Alviano, Mario. "Efficient recursive aggregate evaluation in logic programming." Intelligenza Artificiale 5, no. 2 (2011): 207–15. http://dx.doi.org/10.3233/ia-2011-0023.

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40

Arlin Cooper, J. "Constrained mathematics evaluation in probabilistic logic analysis." Reliability Engineering & System Safety 60, no. 3 (June 1998): 199–203. http://dx.doi.org/10.1016/s0951-8320(98)00003-9.

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41

Ambuel, Jack, Seishi Ninomiya, and Nobuo Takahashi. "Fuzzy Logic Evaluation of Soybean Plant Shape." Ikushugaku zasshi 47, no. 3 (1997): 253–57. http://dx.doi.org/10.1270/jsbbs1951.47.253.

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42

Iliev, Atanas, and Vangel Fustik. "Fuzzy logic approach for hydroelectric project evaluation." IFAC Proceedings Volumes 36, no. 7 (June 2003): 241–46. http://dx.doi.org/10.1016/s1474-6670(17)35838-x.

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43

Maruyama, Fumihiro, Taeko Kakuda, Yusuke Matsunaga, Nobuaki Kawato, Yoriko Minoda, and Shuho Sawada. "Logic design system with evaluation-redesign mechanism." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 73, no. 5 (1990): 105–13. http://dx.doi.org/10.1002/ecjc.4430730511.

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44

Areias, Miguel, and Ricardo Rocha. "Batched evaluation of linear tabled logic programs." Computer Science and Information Systems 10, no. 4 (2013): 1775–97. http://dx.doi.org/10.2298/csis121129066a.

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Logic Programming languages, such as Prolog, provide a highlevel, declarative approach to programming. Despite the power, flexibility and good performance that Prolog systems have achieved, some deficiencies in Prolog?s evaluation strategy - SLD resolution - limit the potential of the logic programming paradigm. Tabled evaluation is a recognized and powerful technique that overcomes SLD?s susceptibility in dealing with recursion and redundant sub-computations. In a tabled evaluation, there are several points where we may have to choose between different tabling operations. The decision on which operation to perform is determined by the scheduling algorithm. The two most successful tabling scheduling algorithms are local scheduling and batched scheduling. In previous work, we have developed a framework, on top of the Yap Prolog system, that supports the combination of different linear tabling strategies for local scheduling. In this work, we propose the extension of our framework to support batched scheduling. In particular, we are interested in the two most successful linear tabling strategies, the DRA and DRE strategies. To the best of our knowledge, no other Prolog system supports both strategies simultaneously for batched scheduling. Our experimental results show that the combination of the DRA and DRE strategies can effectively reduce the execution time for batched evaluation.

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45

Stinchcomb, Jeanne B. "Using Logic Modeling to Focus Evaluation Efforts." Journal of Offender Rehabilitation 33, no. 2 (July 2001): 47–65. http://dx.doi.org/10.1300/j076v33n02_04.

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46

Li, Siu-Pan, and Barry F. Will. "A Fuzzy Logic System for Visual Evaluation." Environment and Planning B: Planning and Design 32, no. 2 (April 2005): 293–304. http://dx.doi.org/10.1068/b31155.

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Research has shown that views are an important factor in the overall satisfaction of building occupants, but, because they are qualitative and subjective, views are difficult to assess. This paper presents a new approach to analyzing views using a fuzzy logic methodology, with the aim of assessing the amenity of views. It illustrates the process of constructing a system from data collection and fuzzy rules design, to inference and defuzzification procedures. With the verification of the prediction outcomes, it is shown that the evaluation model can successfully mimic a human's subjective preferences for scenic views. The methodologies and the underlying principles of the evaluation model are explained.

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47

Ordoobadi, Sharon M. "Fuzzy logic and evaluation of advanced technologies." Industrial Management & Data Systems 108, no. 7 (August 22, 2008): 928–46. http://dx.doi.org/10.1108/02635570810898008.

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48

Hansen, Mark, Marvin C. Alkin, and Tanner LeBaron Wallace. "Depicting the logic of three evaluation theories." Evaluation and Program Planning 38 (June 2013): 34–43. http://dx.doi.org/10.1016/j.evalprogplan.2012.03.012.

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49

Fuller, David A., Sacha A. Bocic, and Leopoldo E. Berstossi. "Towards efficient partial evaluation in logic programming." New Generation Computing 14, no. 2 (June 1996): 237–59. http://dx.doi.org/10.1007/bf03037500.

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50

Leuschel, Michael, and Germán Vidal. "Fast offline partial evaluation of logic programs." Information and Computation 235 (April 2014): 70–97. http://dx.doi.org/10.1016/j.ic.2014.01.005.

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