DOI: 10.3897/jucs.169476

Authors: Jefferson Martins, Rodrigo Andrade, Luis F. Alves Pereira

Abstract: Skilled software developers play a crucial role in creating energy-efficient code, contributing to the reduction of carbon emissions and promoting sustainable computing practices. In parallel, Large Language Models (LLMs) have recently emerged as powerful programming assistants, providing support for developers in complex coding tasks. In this context, this study investigates the ability of LLMs to generate computationally efficient implementations of classic algorithms - such as Heap Sort and Binary Search - across different programming languages, with a primary focus on optimizing energy consumption, memory usage, and execution time. Our results show that, in most cases, different LLMs are able to generate these algorithm implementations correctly. Furthermore, in the majority of the evaluated scenarios, LLM-generated code demonstrates superior performance over human-written algorithm code with regard to energy consumption, memory efficiency, and execution time.

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DOI: 10.3897/jucs.124087

Authors: Michele Berti, Matheus Camilo da Silva, Sebastiano Saccani, Sylvio Barbon Junior

Abstract: Synthetic data generation is a promising alternative to traditional data anonymization, with Variational Autoencoders (VAEs) excelling at generating high-quality synthetic tabular datasets. However, VAE hyperparameter selection is often computationally expensive or suboptimal. We propose a meta-learning (MtL) method for hyperparameter recommendation, which achieves competitive performance to state-of-the-art Bayesian Optimization (BO) with median AUC values of 0.660 ± 0.038 (MtL) and 0.650 ± 0.041 (BO), showing no statistically significant difference. Notably, our approach reduces configuration time to under three minutes, compared to BO’s multi-hour requirement, while also enabling incremental improvements through new data integration. This combination of efficiency, adaptability, and performance establishes MtL as a practical solution for hyperparameter tuning in synthetic data generation.

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DOI: 10.3897/jucs.75357

Authors: Jorge Ruiz Quiñones, Antonio J. Fernández Leiva

Abstract: Usually, human participation is required in order to provide feedback during the game tuning or balancing process. Moreover, this is commonly an iterative process in which play-testing is required as well as human interaction for gathering all important information to improve and tune the game components’ specification. In this paper, a mechanism is proposed to accelerate this process and reduce significantly the costs of it, contributing with a solution to perform the game parameter tuning and game balancing using search algorithms and artificial intelligence (AI) techniques. The process is executed in a fully automated way, and just requires a game specification written in a particular video game description language. Automated play-testing, and game’s feedback information analysis, are related to perform game parameters’ tuning and balancing, leading to offer a solution for the problem of optimizing a video game specification. Recently, XVGDL, a new language for specifying video games which is based on the eXtensible Markup Language (XML), has been presented. This paper uses XVGDL+, an extension of this lan- guage that incorporates new components to specify, within the video game specification, desirable goals or requirements to be evaluated after each game execution. A prototypical implementation of a Game Engine (termed XGE+) was also presented. This game engine not only enables the execution of an XVGDL+ game specification but also provides feedback information once the game has finished.The paper demonstrates that the combination of XVGDL+ with XGE+ offers a powerful mechanism for helping solving game AI research problems, in this case, the game tuning of video game parameters, with respect to initial optimization goals. These goals, as one of the particularities of the proposal presented here, are included within the game specification, minimizing the input of the process.As a practical proof of it, two experiments have been conducted to optimize a game specification written in XVGDL via a hill climbing local search method, in a fully automated way.

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DOI: 10.3217/jucs-024-11-1582

Authors: Shadi Abudalfa, Moataz Ahmed

Abstract: Microblogging services have been significantly increased nowadays and enabled people to share conveniently their sentiments (opinions) with regard to matters of concerns. Such sentiments have shown an impact on many fields such as economics and politics. Different sentiment analysis approaches have been proposed in the literature to predict automatically sentiments shared in micro-blogs (e.g., tweets). A class of such approaches predicts opinion towards specific target (entity); this class is referred to as target-dependent sentiment classification. Another class, called open domain targeted sentiment classification, extracts targets from the micro-blog and predicts sentiment towards them. In this research work, we propose a new semi-supervised learning technique for developing open domain targeted sentiment classification by using fewer amounts of labelled data. To the best of our knowledge, our model represents the first semi-supervised technique that is proposed for open domain targeted sentiment classification. Additionally, we propose a new supervised learning model for improving accuracy of open domain targeted sentiment classification. Moreover, we show for the first time that SVM HMM is able to improve accuracy of open domain targeted sentiment classification. Experimental results show that our proposed technique outperforms other prominent techniques available in the literature.

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DOI: 10.3217/jucs-023-07-0619

Authors: Jianfeng Qiu, Juan Xie, Fan Cheng, Xuefeng Zhang, Lei Zhang

Abstract: Abstract Social Spider Optimization (SSO) algorithm is a swarm intelligence optimizationalgorithm based on the mating behavior of social spiders. Numerical simulation results have shown that SSO outperformed some classical swarm intelligence algorithms such as ParticleSwarm Optimization (PSO) algorithm and Artificial Bee Colony (ABC) algorithm and so on. However, there are still some deficiencies about SSO algorithm, such as the poor balancebetween exploration and exploitation. To this end, an improved SSO algorithm named wDESSO is proposed for global optimization, which can balance exploration and exploitation effectively.Specifically, a weighting factor changing with iteration is introduced to control and adjust the search scope of SSO algorithm dynamically. After social-spiders have completed their search,a mutation operator is then suggested for jumping out of the potential local optimization, thus can further strengthen the ability of global search. The experimental results on a set of standardbenchmark functions demonstrate the effectiveness of wDESSO in solving complex numerical optimization problems.

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DOI: 10.3217/jucs-023-03-0280

Authors: Evangelos Kalampokis, Areti Karamanou, Efthimios Tambouris, Konstantinos Tarabanis

Abstract: The analysis of Social Media data enables eliciting public behaviour and opinion. In this context, a number of studies have recently explored Social Media's capability to predict the outcome of real-world phenomena. The results of these studies are controversial with elections being the most disputable phenomenon. The objective of this paper is to present a case of predicting the results of the UK 2010 through Twitter. In particular, we study to what extend it is possible to use Twitter data to accurately predict the percentage of votes of the three most prominent political parties namely the Conservative Party, Liberal Democrats, and the Labour Party. The approach we follow capitalises on (a) a theoretical Social Media data analysis framework for predictions and (b) Linked Open Data to enrich Twitter data. We extensively discuss each step of the framework to emphasise on the details that could affect the prediction accuracy.We anticipate that this paper will contribute to the ongoing discussion of understanding to what extend and under which circumstances election results are predictable through Social Media.

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DOI: 10.3217/jucs-022-05-0691

Authors: Mario Paredes-Valverde, Jorge Limon-Romero, Diego Tlapa, Yolanda Baez-Lopez

Abstract: Sentiment analysis aims to extract users' opinions from review documents. Nowadays, there are two main approaches for sentiment analysis: the semantic orientation and the machine learning. Sentiment analysis approaches based on Machine Learning (ML) methods work over a set of features extracted from the users' opinions. However, the high dimensionality of the feature vector reduces the effectiveness of this approach. In this sense, we propose a sentiment classification method based on feature selection mechanisms and ML methods. The present method uses a hybrid feature extraction method based on POS pattern and dependency parsing. The features obtained are enriched semantically through common-sense knowledge bases. Then, a feature selection method is applied to eliminate the noisy and irrelevant features. Finally, a set of classifiers is trained in order to classify unknown data. To prove the effectiveness of our approach, we have conducted an evaluation in the movies and technological products domains. Also, our proposal was compared with well-known methods and algorithms used on the sentiment classification field. Our proposal obtained encouraging results based on the F-measure metric, ranging from 0.786 to 0.898 for the aforementioned domains.

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DOI: 10.3217/jucs-015-04-0877

Authors: Trong Duong, Geun-Sik Jo, Jason Jung, Ngoc Nguyen

Abstract: Most previous research on ontology integration has focused on similarity measure-ments between ontological entities, e.g., lexicons, instances, schemas and taxonomies, resulting in high computational costs of considering all possible pairs between two given ontologies. In this paper, we propose a novel approach to reducing computational complexity in ontology integration. Thereby, we address the importance and types of concepts, for priority matching anddirect matching between concepts, respectively. Identity-based similarity is computed, to avoid comparisons of all properties related to each concept, while matching between concepts. Theproblem of conflict in ontology integration has initially been explored on the instance-level and concept-level. This is useful to avoid many cases of mismatching.

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DOI: 10.3217/jucs-014-15-2532

Authors: Marcos Paulo Mello Araujo, Nadia Nedjah, Luiza Mourelle

Abstract: Synchronous finite state machines are very important for digital sequential designs. Among other important aspects, they represent a powerful way for synchronizing hardware components so that these components may cooperate adequately in the fulfillment of the main objective of the hardware design. In this paper, we propose an evolutionary methodology to solve one of the problems related to the design of finite state machines. We optimally solve the state assignment NP -complete problem using a quantum inspired evolutionary algorithm. This is motivated by the fact that with an optimal state assignment one can physically implement the state machine using a minimal hardware area and response time.

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DOI: 10.3217/jucs-013-09-1309

Authors: Hongbo Liu, Ajith Abraham

Abstract: Recently, Particle Swarm Optimization (PSO) algorithm has exhibited good performance across a wide range of application problems. A quick review of the literature reveals that research for solving the Quadratic Assignment Problem (QAP) using PSO approach has not much been investigated. In this paper, we design a hybrid meta-heuristic fuzzy scheme, called as variable neighborhood fuzzy particle swarm algorithm (VNPSO), based on fuzzy particle swarm optimization and variable neighborhood search to solve the QAP. In the hybrid fuzzy scheme, the representations of the position and velocity of the particles in the conventional PSO is extended from the real vectors to fuzzy matrices. A new mapping is introduced between the particles in the swarm and the problem space in an efficient way. We also attempt to theoretically prove that the variable neighborhood particle swarm algorithm converges with a probability of 1 towards the global optimal. The performance of the proposed approach is evaluated and compared with other four different algorithms. Empirical results illustrate that the approach can be applied for solving quadratic assignment problems effectively.

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DOI: 10.3217/jucs-013-06-0854

Authors: Nuno Rodrigues, Luís Barbosa

Abstract: Program slicing is a well known family of techniques intended to identify and isolate code fragments which depend on, or are depended upon, specific program entities. This is particularly useful in the areas of reverse engineering, program understanding, testing and software maintenance. Most slicing methods, and corresponding tools, target either the imperative or the object oriented paradigms, where program slices are computed with respect to a variable or a program statement.

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DOI: 10.3217/jucs-012-11-1679

Authors: Pascual Julián, Ginés Moreno, Jaime Penabad

Abstract: Multi-adjoint logic programming represents a very recent, extremely flexible attempt for introducing fuzzy logic into logic programming. In this setting, the execution of a goal w.r.t. a given program is done in two separate phases. During the operational one, admissible steps are systematically applied in a similar way to classical resolution steps in pure logic programming, thus returning a computed substitution together with an expression where all atoms have been exploited. This last expression is then interpreted under a given lattice during the so called interpretive phase, hence returning a value which represents the fuzzy component (truth degree) of the computed answer. On the other hand, unfolding is a well known transformation rule widely used in declarative programming for optimizing and specializing programs, among other applications. In essence, it is usually based on the application of operational steps on the body of program rules. The novelty of this paper consists in showing that this process can also be made in terms of interpretive steps. We present two strongly related kinds of unfolding (operational and interpretive), which, apart from exhibiting strong correctness properties (i.e. they preserve the semantics of computed substitutions and truth degrees) they are able to significantly simplify the two execution phases when solving goals.

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DOI: 10.3217/jucs-012-07-0828

Authors: Nuno Rodrigues, Luís Barbosa

Abstract: Program slicing is a well known family of techniques used to identify code fragments which depend on or are depended upon specific program entities. They are particularly useful in the areas of reverse engineering, program understanding, testing and software maintenance. Most slicing methods, usually oriented towards the imperative or object paradigms, are based on some sort of graph structure representing program dependencies. Slicing techniques amount, therefore, to (sophisticated) graph transversal algorithms. This paper proposes a completely different approach to the slicing problem for functional programs. Instead of extracting program information to build an underlying dependencies' structure, we resort to standard program calculation strategies, based on the so-called Bird-Meertens formalism. The slicing criterion is specified either as a projection or a hiding function which, once composed with the original program, leads to the identification of the intended slice. Going through a number of examples, the paper suggests this approach may be an interesting, even if not completely general, alternative to slicing functional programs.

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DOI: 10.3217/jucs-012-04-0408

Authors: Ajith Abraham, Crina Grosan

Abstract: This paper presents three variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the stressor — susceptibility interaction model. A circuit or a system is considered to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after pre-processing and standardization are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm and classification and regression trees. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.

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DOI: 10.3217/jucs-012-04-0395

Authors: Nadia Nedjah, Luiza Mourelle

Abstract: In this paper, we propose a methodology based on genetic programming to automatically generate hardware designs of substitution boxes necessary for many cryptosystems such as DES encryption system. We aim at evolving minimal hardware specifications, which minimise both space (i.e. required gate number), response time (i.e. encryption and decryption time) and dissipated power. We compare our results against existing and well-known designs, which were produced by human designers using conventional methods.

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DOI: 10.3217/jucs-009-02-0173

Authors: Walter Guttmann, Helmuth Partsch, Wolfram Schulte, Ton Vullinghs

Abstract: This paper describes the program transformation system Ultra. The intended use of Ultra is to assist programmers in the formal derivation of correct and efficient programs from high-level descriptive or operational specifications. We illustrate its utility by deriving a version of the Heapsort algorithm from a non-deterministic specification. Ultra supports equational reasoning about functional programs using defining equations, algebraic laws of underlying data structures, and transformation rules. The system does not only support modifying terms, but is also useful for bookkeeping and development navigating tasks. The most salient features of Ultra are its sound theoretical foundation, its extendability, its flexible and convenient way to express transformation tasks, its comfortable user interface, and its lean and portable implementation. Ultra itself is written in the functional language Gofer.

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DOI: 10.3217/jucs-007-05-0410

Authors: Gerhard Goos

Abstract: We consider the state of the art in compiler construction and where to go from here. Main topics are improved exploitation of present (and future) hardware features, the interaction between compiling techniques and processor design, and the use of compiling techniques in application areas such as component-based software engineering and software reengineering.

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