1. Automatic compiler/interpreter generation from programs for domain-specific languages using semantic inference : doktorska disertacijaŽeljko Kovačević, 2022, doktorska disertacija Opis: Presented doctoral dissertation describes a research work on Semantic Inference, which can be regarded as an extension of Grammar Inference. The main task of Grammar Inference is to induce a grammatical structure from a set of positive samples (programs), which can sometimes also be accompanied by a set of negative samples. Successfully applying Grammar Inference can result only in identifying the correct syntax of a language. But, when valid syntactical structures are additionally constrained with context-sensitive information the Grammar Inference needs to be extended to the Semantic Inference. With the Semantic Inference a further step is realised, namely, towards inducing language semantics. In this doctoral dissertation it is shown that a complete compiler/interpreter for small Domain-Specific Languages (DSLs) can be generated automatically solely from given programs and their associated meanings using Semantic Inference. For the purpose of this research work the tool LISA.SI has been developed on the top of the compiler/interpreter generator tool LISA that uses Evolutionary Computations to explore and exploit the enormous search space that appears in Semantic Inference. A wide class of Attribute Grammars has been learned. Using Genetic Programming approach S-attributed and L-attributed have been inferred successfully, while inferring Absolutely Non-Circular Attribute Grammars (ANC-AG) with complex dependencies among attributes has been achieved by integrating a Memetic Algorithm (MA) into the LISA.SI tool.
Ključne besede: Grammatical Inference, Semantic Inference, Genetic Programming, Attribute Grammars, Memetic Algorithm, Domain-Specific Languages
Objavljeno v DKUM: 17.02.2022; Ogledov: 697; Prenosov: 94
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2. Energy efficient system for detection of elephants with Machine Learning : master's thesisMarko Sagadin, 2020, magistrsko delo Opis: Human-Elephant Conflicts are a major problem in terms of elephant conservation.
According to WILDLABS, an average of 400 people and 100 elephants are killed every year in India alone because of them.
Early warning systems replace the role of human watchers and warn local communities of nearby, potentially life threatening, elephants, thus minimising the Human-Elephant Conflicts.
In this Master's thesis we present the structure of an early warning system, which consists of several low-power embedded systems equipped with thermal cameras and a single gateway.
To detect elephants from captured thermal images we used Machine Learning methods, specifically Convolutional Neural Networks.
The main focus of this thesis was the design, implementation and evaluation of Machine Learning models running on microcontrollers under low-power conditions.
We designed and trained several accurate image classification models, optimised them for on-device deployment and compared them against models trained with commercial software in terms of accuracy, inference speed and size.
While writing firmware, we ported a part of the TensorFlow library and created our own build system, suitable for the libopencm3 platform.
We also implemented reporting of inference results over the LoRaWAN network and described a possible server-size solution.
We finally a constructed fully functional embedded system from various development and evaluation boards, and evaluated its performance in terms of power consumption.
We show that embedded systems with Machine Learning capabilities are a viable solution to many real life problems.
Ključne besede: machine learning, microcontroller, on-device inference, thermal camera, low-power system
Objavljeno v DKUM: 06.01.2021; Ogledov: 883; Prenosov: 142
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4. Fuzzy-sets decision-support system for geotechnical site soundingsDjemalddine M. Boumezerane, Smaïn Belkacemi, Bojan Žlender, 2011, izvirni znanstveni članek Opis: A geotechnical site investigation is an important and complex task that is generally carried out in two steps. The first step, consisting of preliminary soundings, guides the subsequent site characterization. The number of soundings required to adequately characterize a site is set on the basis of an engineering judgement following the preliminary investigation, this is affected by the geological context, the area topography, the project type, and the knowledge of the neighbouring areas. A fuzzy-sets decision-support system, considering parameters that affect the number of soundings required to adequately characterize a site, is proposed. Parameter uncertainties and a lack of information are also considered. On the basis of the available qualitative and quantitative information, the proposed fuzzy system makes it possible to estimate, for a common project, the number of site soundings required to adequately characterize the site. The cases presented show that a Fuzzy Inference System can be used as a systematic decision-support tool for engineers dealing with site characterizations.
Ključne besede: geotechnical investigation, soundings number, fuzzy sets, fuzzy inference, uncertainties
Objavljeno v DKUM: 13.06.2018; Ogledov: 681; Prenosov: 61
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5. Weibull decision support systems in maintenanceKhalid Aboura, Johnson Agbinya, Ali Eskandarian, 2014, izvirni znanstveni članek Opis: Background: The Weibull distribution is one of the most important lifetime distributions in applied statistics. Weibull analysis is the leading method in the world for fitting and analyzing lifetime data. We discuss one of the earliest decision support system for the assessment of a distribution for the parameters of the Weibull reliability model using expert information. We then present a different approach to assess the parameters distribution.
Objectives: The studies mentioned in this paper aimed to construct a distribution of the parameters of the Weibull reliability model and apply it in the domain of Maintenance Optimization.
Method: The parameters of the Weibull reliability model are considered random variables and a distribution for the parameters is assessed using informed judgment in the form of reliability estimates from vendor information, engineering knowledge or experience in the field.
Results: The results are the development of modern maintenance optimization models that can be embodied in decision support systems.
Conclusion: While the information management part is important in the building of maintenance optimization decision systems, the accuracy of the mathematical and statistical algorithms determines the level of success of the maintenance solution.
Ključne besede: Weibull distribution, reliability, inference, maintenance, expert opinion
Objavljeno v DKUM: 23.01.2018; Ogledov: 983; Prenosov: 321
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6. Inference attacks and control on database structuresMuhamed Turkanović, Tatjana Welzer-Družovec, Marko Hölbl, 2015, izvirni znanstveni članek Opis: Today’s databases store information with sensitivity levels that range from public to highly sensitive, hence ensuring confidentiality can be highly important, but also requires costly control. This paper focuses on the inference problem on different database structures. It presents possible treats on privacy with relation to the inference, and control methods for mitigating these treats. The paper shows that using only access control, without any inference control is inadequate, since these models are unable to protect against indirect data access. Furthermore, it covers new inference problems which rise from the dimensions of new technologies like XML, semantics, etc.
Ključne besede: inference, attacks, database, security, semantics
Objavljeno v DKUM: 09.08.2017; Ogledov: 1141; Prenosov: 105
Celotno besedilo (322,91 KB)
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7. Application of metamodel inference with large-scale metamodelsQichao Liu, Jeffrey G. Gray, Marjan Mernik, Barrett Richard Bryant, 2012, izvirni znanstveni članek Ključne besede: model-driven engineering, reverse engineering, gramar inference, metamodel inference, model co-evolution, model transformation
Objavljeno v DKUM: 01.06.2012; Ogledov: 1306; Prenosov: 21
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8. Embedding DSLS into GPLSDejan Hrnčič, Marjan Mernik, Barrett Richard Bryant, 2011, izvirni znanstveni članek Opis: Embedding of Domain-Specific Languages (DSLs) into General-Purpose Languages (GPLs) is oftenused to express domain-specific problems using the domainćs natural syntax inside GPL programs. It speeds up thedevelopment process, programs are more self-explanatory and repeating tasks are easier to handle. End-users ordomain experts know what the desired language syntax would look like, but do not know how to write a grammar andlanguage processing tools. Grammatical inference can be used for grammar extraction from input examples. Amemetic algorithm for grammatical inference, named MAGIc, was implemented to extract grammar from DSLexamples. In this work MAGIc is extended with embedding the inferred DSL into existing GPL grammar.Additionally, negative examples were also incorporated into the inference process. From the results it can be concludedthat MAGIc is successful for DSL embedding and that the inference process is improved with use of negativeexamples.
Ključne besede: memetic algorithms, doamin-specific languages, grammatical inference, embedding
Objavljeno v DKUM: 01.06.2012; Ogledov: 1413; Prenosov: 45
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