Skip to main content

Research Repository

Advanced Search

Outputs (123)

Measuring and testing the scalability of cloud-based software services (2019)
Presentation / Conference Contribution
Al-Said Ahmad, A., & Andras, P. (2018, October). Measuring and testing the scalability of cloud-based software services. Presented at 2018 Fifth International Symposium on Innovation in Information and Communication Technology (ISIICT), Amman, Jordan

Performance and scalability testing and measurements of cloud-based software services are critically important in the context of rapid growth of cloud computing and supporting the delivery of these services. Cloud-based software services performance... Read More about Measuring and testing the scalability of cloud-based software services.

Cooperation in Repeated Rock-Paper-Scissors Games in Uncertain Environments (2018)
Presentation / Conference Contribution
Andras, P. (2018). Cooperation in Repeated Rock-Paper-Scissors Games in Uncertain Environments. In ALIFE 2018: The 2018 Conference on Artificial Life (404-411). https://doi.org/10.1162/isal_a_00078

Cooperation among selfish individuals provides the fundamentals for social organization among animals and humans. Cooperation games capture this behavior at an abstract level and provide the tools for the analysis of the evolution of cooperation. Her... Read More about Cooperation in Repeated Rock-Paper-Scissors Games in Uncertain Environments.

Random projection neural network approximation (2018)
Presentation / Conference Contribution
Andras, P. (2018). Random projection neural network approximation. In 2018 International Joint Conference on Neural Networks (IJCNN) (1-8). https://doi.org/10.1109/IJCNN.2018.8489215

Neural networks are often used to approximate functions defined over high-dimensional data spaces (e.g. text data, genomic data, multi-sensor data). Such approximation tasks are usually difficult due to the curse of dimensionality and improved method... Read More about Random projection neural network approximation.

New Trends in Databases and Information Systems: ADBIS 2018 Short Papers and Workshops, AI*QA, BIGPMED, CSACDB, M2U, BigDataMAPS, ISTREND, DC, Budapest, Hungary, September, 2-5, 2018, Proceedings (2018)
Presentation / Conference Contribution
(2018, September). New Trends in Databases and Information Systems: ADBIS 2018 Short Papers and Workshops, AI*QA, BIGPMED, CSACDB, M2U, BigDataMAPS, ISTREND, DC, Budapest, Hungary, September, 2-5, 2018, Proceedings. Presented at 22th European Conference on Advances in Databases and Information Systems, ADBIS 2018, Budapest, Hungary

Multifractality and dimensional determinism in local optima networks (2018)
Presentation / Conference Contribution
Thomson, S. L., Verel, S., Ochoa, G., Veerapen, N., & Cairns, D. (2018, July). Multifractality and dimensional determinism in local optima networks. Presented at GECCO '18: Genetic and Evolutionary Computation Conference, Kyoto, Japan

We conduct a study of local optima networks (LONs) in a search space using fractal dimensions. The fractal dimension (FD) of these networks is a complexity index which assigns a non-integer dimension to an object. We propose a fine-grained approach t... Read More about Multifractality and dimensional determinism in local optima networks.

Social learning in repeated cooperation games in uncertain environments (2018)
Journal Article
Andras, P. (2018). Social learning in repeated cooperation games in uncertain environments. Cognitive Systems Research, 51, 24-39. https://doi.org/10.1016/j.cogsys.2018.04.013

Cooperation and social learning are fundamental mechanisms that maintain social organisation among animals and humans. Social institutions can be conceptualised abstractly as cooperation games with social learning. In some cases potential cooperation... Read More about Social learning in repeated cooperation games in uncertain environments.

On the Fractal Nature of Local Optima Networks (2018)
Presentation / Conference Contribution
Thomson, S. L., Verel, S., Ochoa, G., Veerapen, N., & McMenemy, P. (2018). On the Fractal Nature of Local Optima Networks. In Evolutionary Computation in Combinatorial Optimization. EvoCOP 2018 (18-33). https://doi.org/10.1007/978-3-319-77449-7_2

A Local Optima Network represents fitness landscape connectivity within the space of local optima as a mathematical graph. In certain other complex networks or graphs there have been recent observations made about inherent self-similarity. An object... Read More about On the Fractal Nature of Local Optima Networks.

The effect of landscape funnels in QAPLIB instances (2017)
Presentation / Conference Contribution
Thomson, S. L., Ochoa, G., Daolio, F., & Veerapen, N. (2017, July). The effect of landscape funnels in QAPLIB instances. Presented at GECCO '17: Genetic and Evolutionary Computation Conference, Berlin, Germany

The effectiveness of common metaheuristics on combinatorial optimisation problems can be limited by certain characteristics of the fitness landscape. We use the local optima network model to compress the 'inherent structure' of a problem space into a... Read More about The effect of landscape funnels in QAPLIB instances.

Comparing communities of optima with funnels in combinatorial fitness landscapes (2017)
Presentation / Conference Contribution
Thomson, S. L., Daolio, F., & Ochoa, G. (2017, July). Comparing communities of optima with funnels in combinatorial fitness landscapes. Presented at GECCO '17: Genetic and Evolutionary Computation Conference, Berlin, Germany

The existence of sub-optimal funnels in combinatorial fitness landscapes has been linked to search difficulty. The exact nature of these structures --- and how commonly they appear --- is not yet fully understood. Improving our understanding of funne... Read More about Comparing communities of optima with funnels in combinatorial fitness landscapes.

Evolutionary computing in recommender systems: a review of recent research (2016)
Journal Article
Horváth, T., & de Carvalho, A. C. P. L. F. (2017). Evolutionary computing in recommender systems: a review of recent research. Natural Computing, 16(3), 441-462. https://doi.org/10.1007/s11047-016-9540-y

One of the main current applications of intelligent systems is recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. O... Read More about Evolutionary computing in recommender systems: a review of recent research.

Leveraging the Cloud for Large-Scale Software Testing: A Case Study ‑ Google Chrome on Amazon (2014)
Book Chapter
Pakhira, A., & Andras, P. (2014). Leveraging the Cloud for Large-Scale Software Testing: A Case Study ‑ Google Chrome on Amazon. In Cloud Technology: Concepts, Methodologies, Tools, and Applications. IGI Global. https://doi.org/10.4018/978-1-4666-6539-2.ch055

Testing is a critical phase in the software life-cycle. While small-scale component-wise testing is done routinely as part of development and maintenance of large-scale software, the system level testing of the whole software is much more problematic... Read More about Leveraging the Cloud for Large-Scale Software Testing: A Case Study ‑ Google Chrome on Amazon.

Ranking Formal Concepts by Utilizing Matrix Factorization (2014)
Presentation / Conference Contribution
Pisková, L., Horvath, T., & Krajči, S. Ranking Formal Concepts by Utilizing Matrix Factorization. Presented at 12th International Conference on Formal Concept Analysis, Cluj-Napoca, Romania

Formal Concept Analysis often produce huge number of formal concepts even for small input data. Such a large amount of formal concepts, which is intractable to analyze for humans, calls for a kind of
a ranking of formal concepts according to their i... Read More about Ranking Formal Concepts by Utilizing Matrix Factorization.

Buried pipe localization using an iterative geometric clustering on GPR data (2013)
Journal Article
Janning, R., Busche, A., Horváth, T., & Schmidt-Thieme, L. (2014). Buried pipe localization using an iterative geometric clustering on GPR data. Artificial Intelligence Review, 42(3), 403-425. https://doi.org/10.1007/s10462-013-9410-2

Ground penetrating radar is a non-destructive method to scan the shallow subsurface for detecting buried objects like pipes, cables, ducts and sewers. Such buried objects cause hyperbola shaped reflections in the radargram images achieved by GPR. Ori... Read More about Buried pipe localization using an iterative geometric clustering on GPR data.

Factorization Techniques for Predicting Student Performance (2012)
Book Chapter
Thai-Nghe, N., Drumond, L., Horváth, T., Krohn-Grimberghe, A., Nanopoulos, A., & Schmidt-Thieme, L. (2012). Factorization Techniques for Predicting Student Performance. In O. C. Santos, & J. G. Boticario (Eds.), Educational Recommender Systems and Technologies: Practices and Challenges (129-153). IGI Global. https://doi.org/10.4018/978-1-61350-489-5.ch006

Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in e-learning for recommending learning objects (e.g. papers) to students. This chapter introduces state-of-the-art recommender system techni... Read More about Factorization Techniques for Predicting Student Performance.

GRAMOFON: General model-selection framework based on networks (2011)
Journal Article
Buza, K., Nanopoulos, A., Horváth, T., & Schmidt-Thieme, L. (2012). GRAMOFON: General model-selection framework based on networks. Neurocomputing, 75(1), 163-170. https://doi.org/10.1016/j.neucom.2011.02.026

Ensembles constitute one of the most prominent class of hybrid prediction models. One basically assumes that different models compensate each other's errors if one combines them in an appropriate way. Often, a large number of various prediction model... Read More about GRAMOFON: General model-selection framework based on networks.

User Preference Web Search -- Experiments with a System Connecting Web and User (2009)
Journal Article
Gurský, P., Horvath, T., Jirásek, J., Krajči, S., Novotny, R., Pribolová, J., Vaneková, V., & Vojtáš, P. (2009). User Preference Web Search -- Experiments with a System Connecting Web and User. Computing and Informatics, 28(4), 1001-1033

We present models, methods, implementations and experiments with a system enabling personalized web search for many users with different preferences. The system consists of a web information extraction part, a text search engine, a middleware support... Read More about User Preference Web Search -- Experiments with a System Connecting Web and User.

A Model of User Preference Learning for Content-Based Recommender Systems (2009)
Journal Article
Horvath, T. (2009). A Model of User Preference Learning for Content-Based Recommender Systems. Computing and Informatics, 28(4), 1001-1029

This paper focuses to a formal model of user preference learning for
content-based recommender systems. First, some fundamental and special requirements to user preference learning are identified and proposed. Three learning tasks are introduced as... Read More about A Model of User Preference Learning for Content-Based Recommender Systems.

Knowledge Processing for Web Search – An Integrated Model and Experiments (2008)
Presentation / Conference Contribution
Gurský, P., Horvath, T., Jirásek, J., Novotny, R., Pribolová, J., Vaneková, V., & Vojtáš, P. Knowledge Processing for Web Search – An Integrated Model and Experiments. Presented at Symposium on Intelligent and Distributed Computing IDC, Craiova, Romania

We propose a model of a middleware system enabling personalized web
search for users with different preferences. We integrate both inductive and deductive tasks to find user preferences and consequently best objects. The model is based on modeling p... Read More about Knowledge Processing for Web Search – An Integrated Model and Experiments.

Integration of two fuzzy data mining methods (2004)
Journal Article
Horvath, T., & Krajči, S. (2004). Integration of two fuzzy data mining methods. Neural Network World, 14(5), 391-402

The cluster analysis and the formal concept analysis are both used to identify significiant groups of similar objects. Rice & Siff's algorithm for the clustering joins these two methods in the case where the values of an object-attribute model are 1... Read More about Integration of two fuzzy data mining methods.