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2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks

作者： Feeney, Kevin Lewis, David Argyroudis, Patroklos Nolan, Keith O'Sullivan, Declan Knowledge and Data Engineering Group (KDEG) Department of Computer Science and Statistics TCD Ireland CTVR Ireland Knowledge and Data Engineering Group Department of Computer Science and Statistics Ireland CTVR TCD Ireland

ISBN: (纸本)9781424406623

The management of dynamic spectrum access requires the coordination of administrative functions across multiple organizations, from regulators to secondary market operators and commons cooperatives. Policy-based management approaches to dynamic spectrum access must therefore support a wide range of organizational types and handle the relationships between policies authored separately within those organizations. A policy language for administering dynamic access to spectrum resources has been proposed by the DARPA XG initiative. This defines an expressive and extensible language for specifying policies that determine the correct behavior of RIF transceivers as spectrum usage opportunities occur. However, this language does not yet address the mapping of policies onto the complex organizational settings that are likely to emerge in the dynamic spectrum access domain. This paper defines how the DARPA XG policy language can be integrated with a meta-policy management scheme that provides the powerful organizational modeling and policy authoring authority control needed to manage the evolution of policies in these complex organizational settings. The benefits of this integration are illustrated through a case study based on the potential use of dynamic spectrum access licenses granted in Ireland for the DySpan 2007 conference, and the organizational conflicts that even this simple scenario can yield. The practical issues of administering a multi-organizational policy management system are examined through an analysis of the interactions between existing components that are being assembled to demonstrate sophisticated policy management of a cognitive radio system.

关键词： Abstracting

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Cross-domain neural-kernel networks 31

Cross-domain neural-kernel networks

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31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019

作者： Mehrkanoon, Siamak Department of Data Science and Knowledge Engineering Maastricht University Netherlands

A novel cross-domain neural-kernel networks architecture for semi-supervised domain adaption problem is introduced. The proposed model consists of two stream neural-kernel networks corresponding to the source and target domains which are enriched with a coupling term. Each stream neural-kernel networks follows a combination of neural network layer and an explicit feature map constructed by means of random Fourier features. The introduced coupling term aims at enforcing correlations among the output of the intermediate layers of the two stream networks as well as encouraging the two networks to learn shared representation of the data from both source and target domains. Experimental results are given to illustrate the effectiveness of the proposed approaches on real-life datasets. © 2019 for this paper by its authors.

关键词： Network layers

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A generative policy gradient approach for learning to play text-based adventure games 31

A generative policy gradient approach for learning to play t...

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31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019

作者： Raab, René Driessens, Kurt Department of Data Science and Knowledge Engineering Maastricht University Netherlands

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Enhancing brain decoding using attention augmented deep neural networks 29

Enhancing brain decoding using attention augmented deep neur...

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29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021

作者： Abdellaoui, Ismail Alaoui Fernández, Jesús García Sahinli, Caner Mehrkanoon, Siamak Department of Data Science and Knowledge Engineering Maastricht University Netherlands

ISBN: (纸本)9782875870827

Neuroimaging techniques have shown to be valuable when studying brain activity. This paper uses Magnetoencephalography (MEG) data, provided by the Human Connectome Project (HCP), and different deep learning models to perform brain decoding. Specifically, we investigate to which extent one can infer the task performed by a subject based on its MEG data. In order to capture the most relevant features of the signals, self and global attention are incorporated into our models. The obtained results show that the inclusion of attention improves the performance and generalization of the models across subjects. © 2021 ESANN Intelligence and Machine Learning. All rights reserved.

关键词： Magnetoencephalography

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On sorting with a network of two stacks 19

On sorting with a network of two stacks

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19th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems, ATMOS 2019

作者： Mihalák, Matúš Pont, Marc Department of Data Science and Knowledge Engineering Maastricht University Netherlands

ISBN: (纸本)9783959771283

Sorting with stacks is a collection of problems that deal with sorting a sequence of numbers by pushing and popping the numbers to and from a given set of stacks. Multiple concrete decision or optimization questions are formed by restricting the access to the stacks. The motivation comes, e.g., from shunting train wagons in shunting yards, shunting trams in depots, or in stacking cargo containers on cargo ships or storage yards in transshipment terminals. We consider the problem of sorting a permutation of n integers 1, 2, . . ., n using k ≥ 2 stacks. In this problem, elements from the input sequence are pushed one-by-one (in the order of the elements in the sequence) to one of the k stacks. At any time, an element from a stack can be popped and pushed to another stack;such an operation is called a shuffle. Also, at any time, an element can be popped from a stack and placed to the output sequence. We can only place the elements to the output in the increasing order of their value such that at the end the output is the ordered sequence of the elements. The problem asks to minimize the number of shuffles in the process. It is known that for k ≥ 4, the problem is NP-hard, and that there is no approximation algorithm unless P=NP. For k ≥ 3, it is known that at most O(n log n) shuffles are needed for any input sequence. For the case when k = 2, there exist input sequences that require Ω(n2−Ε) shuffles, for any Ε > 0. Nothing substantially more is known for the case of k = 2. In this paper, we study the following variant of the problem with k = 2 stacks: no shuffle and no placement to the output sequence can happen before every element is in one of the two stacks. We show that our problem can be seen as the MinUnCut problem by providing a polynomial-time reduction, and thus we show that there exists a randomized O(√log n)-approximation algorithm and a deterministic O(log n)-approximation algorithm for our problem. © Matúš Mihalák and Marc Pont;licensed under Creative Comm

关键词： Optimization

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Synthesizing personality-dependent body postures using generative adversarial networks 31

Synthesizing personality-dependent body postures using gener...

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31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019

作者： Calsius, Frederik Asteriadis, Stylianos Department of Data Science and Knowledge Engineering Maastricht University Netherlands

In Personality Computing, one of the major goals is to estimate the personality of an individual by making use of computational techniques. Among the existing models that classify personality traits, the Big-5 factor model is probably the most popular, due to the fact that it provides a compact and complete set of traits describing human personalities. This paper presents an adversarial method that allows the generation of body postures exhibiting the characteristics of a chosen personality trait. The Big-5 and a broader model are analyzed and, in particular, we propose and analyze a technique for generating silhouettes with different levels of extroversion, as well as the aspect of a broader model corresponding to how over (or under) constrained a person is. The proposed approach can be applied in domains such as automatic character animation, marketing, and the broader field of Affective Computing. © 2019 for this paper by its authors.

关键词： Generative adversarial networks

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Carbon-doped percentage effect on the mechanical properties of nanoporous silicon sample using molecular dynamics simulation

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Case Studies in Chemical and Environmental engineering 2025年 11卷

作者： Sawaran Singh, Narinderjit Singh Ali, Ali B.M. Ameen, Hawzhen Fateh M. Al-Zahiwat, Murtadha M. Salahshour, Soheil Emami, Nafiseh Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Petroleum Technology Erbil Technology College Erbil Polytechnic University Kurdistan Region Erbil Iraq Department of Petroleum Engineering Knowledge University Erbil Iraq Department of Chemical engineering College of Engineering University of Misan Amarah Iraq Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Türkiye Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Türkiye Research Center of Applied Mathematics Khazar University Baku Türkiye Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Porous materials have attracted considerable attention from researchers due to its many uses in molecular separation, heterogeneous catalysis, absorption technologies, and electronic improvements. These solid materials, often defined by their structural voids, are essential in several sectors. This research investigated the impact of carbon doping on the mechanical characteristics of nanoporous silicon matrices. The use of high-purity silicon doping is very beneficial in the semiconductor industry and is crucial for high-power devices and automotive applications. This study simulates a nanoporous silicon sample by molecular dynamics methods, adding carbon doping at different concentrations. The findings demonstrate that when the carbon doping concentration escalated from 1 % to 30 %, the mechanical resistance of the system decreased correspondingly. The ultimate tensile strength fell from 10.26 to 9.02 GPa. Furthermore, Young's modulus rose from 83.47 to 98.37 GPa. The decline in mechanical stability was associated with a drop in the model's total weight, which had considerable ramifications for industrial applications. Thus, incorporating C-doped nanoporous silicon into real applications not only lowered the weight of target materials but also improved their use. © 2025 The Author(s)

关键词： Carbon doping Mechanical properties Molecular dynamics simulation Nanoporous Porous materials

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A novel framework for efficient dominance-based rough set approximations using K-dimensional (K-D) tree partitioning and adaptive recalculations techniques

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engineering Applications of Artificial Intelligence 2025年 154卷

作者： Nawaz, Uzma Saeed, Zubair Atif, Kamran Knowledge and Data Science Research Centre Department of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Science and Technology Islamabad Pakistan Department of Electrical & Computer Engineering Texas A&M University College StationTX United States Department of Civil Engineering Deakin University Victoria Australia

In dynamic datasets, both entries and their possible values frequently change. This makes it difficult to update the necessary calculations for analysis. The Dominance-based Rough Set Approach (DRSA) effectively analyzes preference-based data. Still, it can be slow because it requires recalculating important values each time the data are updated. This study proposes an optimized K-dimensional (K-D) tree algorithm that integrates dynamic weighting and lazy recalculations to reduce computational cost. The K-D tree effectively organizes data, dynamic weighting prioritizes recalculations for essential nodes, and lazy recalculation postpones unnecessary computation. The proposed approach significantly reduces the execution time and memory consumption while maintaining accuracy. An ablation study was conducted to separately evaluate the impact of each component. This demonstrates the efficiency improvement achieved using the proposed approach. Compared to conventional DRSA, our optimized method reduced computational time and memory usage by 99 % across ten publicly available University of California, Irvine (UCI) datasets, which provided an efficient and scalable solution for real-time data processing in dynamic environments. © 2025 The Authors

关键词： data reduction

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Deep learning for robust end-to-end tone mapping 30

Deep learning for robust end-to-end tone mapping

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30th British Machine Vision Conference, BMVC 2019

作者： Montulet, Rico Briassouli, Alexia Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

Low-light images require localised processing to enhance details, contrast and lighten dark regions without affecting the appearance of the entire image. A range of tone mapping techniques have been developed to achieve this, with the latest state-of-the-art methods leveraging deep learning. In this work, a new end-to-end tone mapping approach based on Deep Convolutional Adversarial Networks (DCGANs) is introduced along with a data augmentation technique, and shown to improve upon the latest state-of-the-art on benchmarking datasets. We carry out comparisons using the MIT-Adobe FiveK (MIT-5K) and the LOL datasets, as they provide benchmark training and testing data, which is further enriched with data augmentation techniques to increase diversity and robustness. A U-net is used in the generator and a patch-GAN in the discriminator, while a perceptually-relevant loss function based on VGG is used in the generator. The results are visually pleasing, and shown to improve upon the state-of-the-art Deep Retinex, Deep Photo Enhancer and GLADNet on the most widely used benchmark dataset MIT-5K and LOL, without additional computational requirements. © 2019. The copyright of this document resides with its authors.

关键词： Image enhancement

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‘Thy Algorithm Shalt Not Bear False Witness’: An Evaluation of Multiclass Debiasing Methods on Word Embeddings 1

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32nd Benelux Conference on Artiﬁcial Intelligence and Belgian-Dutch Conference on Machine Learning, BNAIC/Benelearn 2020

作者： Schlender, Thalea Spanakis, Gerasimos Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

ISBN: (数字)9783030766405

ISBN: (纸本)9783030766399

With the vast development and employment of artificial intelligence applications, research into the fairness of these algorithms has been increased. Specifically, in the natural language processing domain, it has been shown that social biases persist in word embeddings and are thus in danger of amplifying these biases when used. As an example of social bias, religious biases are shown to persist in word embeddings and the need for its removal is highlighted. This paper investigates the state-of-the-art multiclass debiasing techniques: Hard debiasing, SoftWEAT debiasing and Conceptor debiasing. It evaluates their performance when removing religious bias on a common basis by quantifying bias removal via the Word Embedding Association Test (WEAT), Mean Average Cosine Similarity (MAC) and the Relative Negative Sentiment Bias (RNSB). By investigating the religious bias removal on three widely used word embeddings, namely: Word2Vec, GloVe, and ConceptNet, it is shown that the preferred method is ConceptorDebiasing. Specifically, this technique manages to decrease the measured religious bias on average by 82.42%, 96.78% and 54.76% for the three word embedding sets respectively. © 2021, Springer Nature Switzerland AG.

关键词： Embeddings

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