Kumova, Bora İsmail
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Kumova, B.
Kumova, BI
Kumova, Bİ
Kumova, Bora
Kumova, Bora I.
Kumova, Bora İ.
Kumova, Bora Ismail
Kumova, Bora Ý.
Kumova, BY
Kumova, B. Ý.
Kumova, BI
Kumova, Bİ
Kumova, Bora
Kumova, Bora I.
Kumova, Bora İ.
Kumova, Bora Ismail
Kumova, Bora Ý.
Kumova, BY
Kumova, B. Ý.
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03.04. Department of Computer Engineering
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51
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1.44
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2.83
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18
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| Journal | Count |
|---|---|
| Lecture Notes in Computer Science | 4 |
| 2015 IEEE International Conference On Fuzzy Systems (Fuzz-IEEE 2015) | 1 |
| 2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 | 1 |
| 2017 International Conference on Computer Science and Engineering (UBMK) | 1 |
| 5th Workshop on Dynamics of Knowledge and Belief and the 4th Workshop KI and Kognition | 1 |
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Master Thesis Development of Fuzzy Syllogistic Algorithms and Applications Distributed Reasoning Approaches(Izmir Institute of Technology, 2010) Çakır, Hüseyin; Kumova, Bora İsmailA syllogism, also known as a rule of inference or logical appeals, is a formal logical scheme used to draw a conclusion from a set of premises. It is a form of deductive reasoning that conclusion inferred from the stated premises. The syllogistic system consists of systematically combined premises and conclusions to so called figures and moods. The syllogistic system is a theory for reasoning, developed by Aristotle, who is known as one of the most important contributors of the western thought and logic. Since Aristotle, philosophers and sociologists have successfully modelled human thought and reasoning with syllogistic structures. However, a major lack was that the mathematical properties of the whole syllogistic system could not be fully revealed by now. To be able to calculate any syllogistic property exactly, by using a single algorithm, could indeed facilitate modelling possibly any sort of consistent, inconsistent or approximate human reasoning. In this work generic fuzzifications of sample invalid syllogisms and formal proofs of their validity with set theoretic representations are presented. Furthermore, the study discuss the mapping of sample real-world statements onto those syllogisms and some relevant statistics about the results gained from the algorithm applied onto syllogisms. By using this syllogistic framework, it can be used in various fields that can uses syllogisms as inference mechanisms such as semantic web, object oriented programming and data mining reasoning processes.Master Thesis Fuzzy-Syllogistic Reasoning(Izmir Institute of Technology, 2015) Zarechnev, Mikhail; Kumova, Bora İsmailA syllogism is a formal logical scheme used to infer a conclusion from a set of premises. In a categorical syllogism, there are only two premises and each premise and conclusion is given in form a of quantity-quantified relationship between two objects. Different order of objects in premises produce a classification known as syllogistic figures. Ordered combinations of 3 quantifiers with a certain figure, known as moods, provide 256 combinations in total. However, only 25 of them are valid, i.e. conclusion follows from premises. The classical syllogistic system allows to model human thought as reasoning with syllogistic structures. However, a major lack is that there is still no systems that allow to arrive at a decision of syllogisms automatically. This work is an attempt to design a fully algorithmic approach that allows to calculate properties of a whole syllogistic system and provide automated reasoning for given data sets. Since there is a limitation of the classical syllogistic system such as fixed number of crisp quantifiers, advanced fuzzy-quantifiers were introduced to bypass this restriction. Based on the classical syllogistic concept extended by fuzzy-quantifiers, an algorithm for fuzzy-syllogistic reasoning was proposed and integrated into a software system developed for this purpose. Possible applications of syllogistic reasoning, in particular, ontology-based fuzzy-syllogistic reasoning were also discussed.Conference Object On-board applications development via symbolic user interfaces(Springer, 2014) Kumova, Bora İsmailbecerik is a functional language consisting of symbolic commands for managing and composing applications. Application commands consist of symbols that are associated with reading sensor values, computing those values and executing actuator values. It is the result of a co-design of mechatronic functionality and robotic behaviour. The requirements given for mechatronic functionality were those of simple robotics kits that are used in school education or as toys. The requirements given for the behaviour were to provide a reflexive one, consisting of triggering simple computations and actuations from simple sensor values. becerik currently lives as a leJOS application on NXT robots and enables developing simple applications using the standard display and buttons of the NXT brick. In this paper we introduce the symbolic user interfaces of becerik. © 2014 Springer International Publishing Switzerland.Article Citation - WoS: 1Citation - Scopus: 1Dynamically Adaptive Partition-Based Interest Management in Distributed Simulation(Elsevier Ltd., 2006) Kumova, Bora İsmailPerformance and scalability of distributed simulations depends primarily on the effectiveness of the employed interest management (IM) schema that aims at reducing the overall computational and messaging effort on the shared data to a necessary minimum. Existing IM approaches, which are based on variations or combinations of two principle data distribution techniques, namely region-based and grid-based techniques, perform poorly if the simulation develops an overloaded host. In order to facilitate distributing the processing load from overloaded areas of the shared data to less loaded hosts, the partition-based technique is introduced that allows for variable-size partitioning the shared data. Based on this data distribution technique, an IM approach is sketched that is dynamically adaptive to access latencies of simulation objects on the shared data as well as to the physical location of the objects. Since this re-distribution is decided depending on the messaging effort of the simulation objects for updating data partitions, any load balanced constellation has the additional advantage to be of minimal overall messaging effort. Hence, the IM schema dynamically resolves messaging overloading as well as overloading of hosts with simulation objects and therefore facilitates dynamic system scalability.Conference Object Making Accident Data Compatible With Its-Based Traffic Management: Turkish Case(Intelligent Transport Systems, 2010) Duvarcı, Yavuz; Geçer Sargın, Feral; Kumova, Bora İsmail; Çınar, Ali Kemal; Selvi, ÖmerOne of the most important reasons of the high rate of accidents would largely lend itself to ineffective data collection and evaluation process since the necessary information cannot be obtained effectively from the traffic accidents reports (TAR). The discord and dealing with non-relevant data may appear at four levels: (1) Country and Cultural, (2) Institutional and organizational, (3) Data collection, (4) Data analysis and Evaluation. The case findings are consistent with this knowledge put forward in the literature; there is a transparency problem in coordination between the institutions as well as the inefficient TAR data, which is open to manipulation; the problem of under-reporting and inappropriate data storage prevails before the false statistical evaluation methods. The old-fashioned data management structure causes incompatibility with the novel technologies, avoiding timely interventions in reducing accidents and alleviating the fatalities. Transmission of the data to the interest agencies for evaluation and effective operation of the ITS-based systems should be considered. The problem areas were explored through diagnoses at institutional, data collection, and evaluation steps and the solutions were determined accordingly for the case city of Izmir.Conference Object Citation - WoS: 5Citation - Scopus: 8The Fuzzy Syllogistic System(Springer Verlag, 2010) Kumova, Bora İsmail; Çakır, HüseyinA categorical syllogism is a rule of inference, consisting of two premisses and one conclusion. Every premiss and conclusion consists of dual relationships between the objects M, P, S. Logicians usually use only true syllogisms for deductive reasoning. After predicate logic had superseded syllogisms in the 19th century, interest on the syllogistic system vanished. We have analysed the syllogistic system, which consists of 256 syllogistic moods in total, algorithmically. We have discovered that the symmetric structure of syllogistic figure formation is inherited to the moods and their truth values, making the syllogistic system an inherently symmetric reasoning mechanism, consisting of 25 true, 100 unlikely, 6 uncertain, 100 likely and 25 false moods. In this contribution, we discuss the most significant statistical properties of the syllogistic system and define on top of that the fuzzy syllogistic system. The fuzzy syllogistic system allows for syllogistic approximate reasoning inductively learned M, P, S relationships.Conference Object Citation - Scopus: 2A Survey of Robotic Agent Architectures(Institute of Electrical and Electronics Engineers, 2017) Kumova, Bora İsmail; Heye, Samuel BachaRobotic agents consist of various compositions of properties that are found in their mechatronics, behavioural and cognitive architectures. Common properties of each architecture type serve as criteria for assessing the degree of intelligence of most embodied agent models. Although embodied intelligence has long been accepted for robotic agents, the literature is short on combined evaluations that discuss all properties of all architecture types in one framework. Here we provide a review of existing taxonomies for each type of architecture and attempt to combine them all in a single taxonomy for robotic agents.Conference Object Citation - WoS: 6Citation - Scopus: 11Dynamically Adaptive Partition-Based Data Distribution Management(Institute of Electrical and Electronics Engineers Inc., 2005) Kumova, Bora İsmailPerformance and scalability of distributed simulations depends primarily on the effectiveness of the employed data distribution management (DDM) algorithm, which aims at reducing the overall computational and messaging effort on the shared data to a necessary minimum. Existing DDM approaches, which are variations and combinations of two basic techniques, namely region-based and grid-based techniques, perform purely in the presence of load differences. We introduce the partition-based technique that allows for variable-size partitioning shared data. Based on this technique, a novel DDM algorithm is introduced that is dynamically adaptive to cluster formations in the shared data as well as in the physical location of the simulation objects. Since the re-distribution is sensitive to inter-relationships between shared data and simulation objects, a balanced constellation has the additional advantage to be of minimal messaging effort. Furthermore, dynamic system scalability is facilitated, as bottlenecks are avoided.Conference Object Citation - Scopus: 1Approximate Reasoning With Fuzzy-Syllogistic Systems(CEUR Workshop Proceedings, 2015) Kumova, Bora İsmailThe well known Aristotelian syllogistic system consists of 256 moods. We have found earlier that 136 moods are distinct in terms of equal truth ratios that range in τ=[0,1]. The truth ratio of a particular mood is calculated by relating the number of true and false syllogistic cases the mood matches. A mood with truth ratio is a fuzzy-syllogistic mood. The introduction of (n-1) fuzzy existential quantifiers extends the system to fuzzy-syllogistic systems nS, 1<n, of which every fuzzy-syllogistic mood can be interpreted as a vague inference with a generic truth ratio that is determined by its syllogistic structure. We experimentally introduce the logic of a fuzzy-syllogistic ontology reasoner that is based on the fuzzy-syllogistic systems nS. We further introduce a new concept, the relative truth ratio rτ=[0,1] that is calculated based on the cardinalities of the syllogistic cases.Book Part Citation - Scopus: 1Symmetric Properties of the Syllogistic System Inherited From the Square of Opposition(Birkhäuser, 2017) Kumova, Bora İsmailThe logical square Omega has a simple symmetric structure that visualises the bivalent relationships of the classical quantifiers A, I, E, O. In philosophy it is perceived as a self-complete possibilistic logic. In linguistics however its modelling capability is insufficient, since intermediate quantifiers like few, half, most, etc cannot be distinguished, which makes the existential quantifier I too generic and the universal quantifier A too specific. Furthermore, the latter is a special case of the former, i.e. A subset of I, making the square a logic with inclusive quantifiers. The inclusive quantifiers I and O can produce redundancies in linguistic systems and are too generic to differentiate any intermediate quantifiers. The redundancy can be resolved by excluding A from I, i.e. I-2=I-A, analogously E from O, i.e. O-2=O-E. Although the philosophical possibility of A subset of I is thus lost in I-2, the symmetric structure of the exclusive square (2)Omega remains preserved. The impact of the exclusion on the traditional syllogistic system S with inclusive existential quantifiers is that most of its symmetric structures are obviously lost in the syllogistic system S-2 with exclusive existential quantifiers too. Symmetry properties of S are found in the distribution of the syllogistic cases that are matched by the moods and their intersections. A syllogistic case is a distinct combination of the seven possible spaces of the Venn diagram for three sets, of which there exist 96 possible cases. Every quantifier can be represented with a fixed set of syllogistic cases and so the moods too. Therefore, the 96 cases open a universe of validity for all moods of the syllogistic system S, as well as all fuzzy-syllogistic systems S-n, with n-1 intermediate quantifiers. As a by-product of the fuzzy syllogistic system and its properties, we suggest in return that the logical square of opposition can be generalised to a fuzzy-logical graph of opposition, for 2<n.