| 1. |
- Janzura, M., et al.
(författare)
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Minimum entropy of error principle in estimation
- 1994
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Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 79:1-2, s. 123-144
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Tidskriftsartikel (refereegranskat)abstract
- The principle of minimum error entropy estimation as found in the work of Weidemann and Stear is reformulated as a problem of finding optimum locations of probability densities in a given mixture such that the resulting (differential) entropy is minimized. New results concerning the entropy lower bound are derived. Continuity of the entropy and attaining the minimum entropy are proved in the case where the mixture is finite. Some other examples and situations, in particular that of symmetric unimodal densities, are studied in more detail
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| 2. |
- Koski, Timo, et al.
(författare)
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Some properties of generalized exponential entropies with applications to data compression
- 1992
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Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 62:1-2, s. 103-132
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Tidskriftsartikel (refereegranskat)abstract
- I. Csiszár discussed generalized entropies in his lecture at the Sixth Prague Conference on Information Theory. The authors emphasize that Csiszár noted the link between certain lower bounds for the quantization error and Rényi's differential entropy of order $\alpha$. Another important reference is the paper by L. L. Campbell where the concept of an exponential entropy was introduced. The authors investigate "several consequences that are of interest in the theory of data (or signal) compression". They also "investigate especially the exponential families of distributions, in particular the Miller-Thomas (or generalized Gaussian) family of distributions". The paper is a detailed discussion of the aforementioned problems coupled with examples and details of the possible applications. Exponential entropy is calculated for the uniform distribution, the univariate Gaussian distribution, the Laplace distribution, the Miller-Thomas distribution, an infinite-dimensional Gaussian exponential family, the Gauss-Laplace mixture and the multivariate Gaussian distribution. The extent of a distribution is given for the shape parameter in the Miller-Thomas distribution. Campbell's representation for E$[\alpha, 1 ; f]$ and the connection between an entropy series and data compression are discussed. A lower bound for the entropy of a partition (as defined in the paper) is given. Examples and proofs are illustrated with outputs from Mathematica.
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| 3. |
- Roy, Sisir, et al.
(författare)
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Uncertainty Relations and Time-Frequency Distributions for Unsharp Observables
- 1996
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Ingår i: Information Sciences. - : Elsevier BV. - 0020-0255 .- 1872-6291. ; 89:3-4, s. 193-209
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Tidskriftsartikel (refereegranskat)abstract
- This paper deals with a new framework in analyzing the formal mathematical correspondence between quantum mechanics and time-frequency representations of a signal. It is also shown that joint time-frequency distributions have a close link with Heisenberg uncertainty relations if the observables are taken as fuzzy entities. This result contradicts the arguments of Cohen [IEEE Proc. 77(7):941 (1989)] regarding the time-frequency distributions and the uncertainty relation. It is postulated that these mechanisms will be of crucial importance in highly fragmented computation structures, such as neural networks, as they may exhibit a strong mutual interaction between data and operator.
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| 4. |
- Adewole, Kayode S., et al.
(författare)
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Energy disaggregation risk resilience through microaggregation and discrete Fourier transform
- 2024
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Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 662
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Tidskriftsartikel (refereegranskat)abstract
- Progress in the field of Non-Intrusive Load Monitoring (NILM) has been attributed to the rise in the application of artificial intelligence. Nevertheless, the ability of energy disaggregation algorithms to disaggregate different appliance signatures from aggregated smart grid data poses some privacy issues. This paper introduces a new notion of disclosure risk termed energy disaggregation risk. The performance of Sequence-to-Sequence (Seq2Seq) NILM deep learning algorithm along with three activation extraction methods are studied using two publicly available datasets. To understand the extent of disclosure, we study three inference attacks on aggregated data. The results show that Variance Sensitive Thresholding (VST) event detection method outperformed the other two methods in revealing households' lifestyles based on the signature of the appliances. To reduce energy disaggregation risk, we investigate the performance of two privacy-preserving mechanisms based on microaggregation and Discrete Fourier Transform (DFT). Empirically, for the first scenario of inference attack on UK-DALE, VST produces disaggregation risks of 99%, 100%, 89% and 99% for fridge, dish washer, microwave, and kettle respectively. For washing machine, Activation Time Extraction (ATE) method produces a disaggregation risk of 87%. We obtain similar results for other inference attack scenarios and the risk reduces using the two privacy-protection mechanisms.
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| 5. |
- Altarabichi, Mohammed Ghaith, 1981-, et al.
(författare)
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Rolling The Dice For Better Deep Learning Performance : A Study Of Randomness Techniques In Deep Neural Networks
- 2024
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Ingår i: Information Sciences. - Philadelphia, PA : Elsevier. - 0020-0255 .- 1872-6291. ; 667, s. 1-17
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a comprehensive empirical investigation into the interactions between various randomness techniques in Deep Neural Networks (DNNs) and how they contribute to network performance. It is well-established that injecting randomness into the training process of DNNs, through various approaches at different stages, is often beneficial for reducing overfitting and improving generalization. However, the interactions between randomness techniques such as weight noise, dropout, and many others remain poorly understood. Consequently, it is challenging to determine which methods can be effectively combined to optimize DNN performance. To address this issue, we categorize the existing randomness techniques into four key types: data, model, optimization, and learning. We use this classification to identify gaps in the current coverage of potential mechanisms for the introduction of noise, leading to proposing two new techniques: adding noise to the loss function and random masking of the gradient updates.In our empirical study, we employ a Particle Swarm Optimizer (PSO) to explore the space of possible configurations to answer where and how much randomness should be injected to maximize DNN performance. We assess the impact of various types and levels of randomness for DNN architectures applied to standard computer vision benchmarks: MNIST, FASHION-MNIST, CIFAR10, and CIFAR100. Across more than 30\,000 evaluated configurations, we perform a detailed examination of the interactions between randomness techniques and their combined impact on DNN performance. Our findings reveal that randomness in data augmentation and in weight initialization are the main contributors to performance improvement. Additionally, correlation analysis demonstrates that different optimizers, such as Adam and Gradient Descent with Momentum, prefer distinct types of randomization during the training process. A GitHub repository with the complete implementation and generated dataset is available. © 2024 The Author(s)
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| 6. |
- Aslam, Muhammad Shamrooz, et al.
(författare)
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Robust stability analysis for class of Takagi-Sugeno (T-S) fuzzy with stochastic process for sustainable hypersonic vehicles
- 2023
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Ingår i: Information Sciences. - Amsterdam : Elsevier. - 0020-0255 .- 1872-6291. ; 641
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Tidskriftsartikel (refereegranskat)abstract
- Recently, the rapid development of Unmanned Aerial Vehicles (UAVs) enables ecological conservation, such as low-carbon and “green” transport, which helps environmental sustainability. In order to address control issues in a given region, UAV charging infrastructure is urgently needed. To better achieve this task, an investigation into the T–S fuzzy modeling for Sustainable Hypersonic Vehicles (SHVs) with Markovian jump parameters and H∞ attitude control in three channels was conducted. Initially, the reentry dynamics were transformed into a control–oriented affine nonlinear model. Then, the original T–S local modeling method for SHV was projected by primarily referring to Taylor's expansion and fuzzy linearization methodologies. After the estimation of precision and controller complexity was assumed, the fuzzy model for jump nonlinear systems mainly consisted of two levels: a crisp level and a fuzzy level. The former illustrates the jumps, and the latter a fuzzy level that represents the nonlinearities of the system. Then, a systematic method built in a new coupled Lyapunov function for a stochastic fuzzy controller was used to guarantee the closed–loop system for H∞ gain in the presence of a predefined performance index. Ultimately, numerical simulations were conducted to show how the suggested controller can be successfully applied and functioned in controlling the original attitude dynamics. © 2023 Elsevier Inc.
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| 7. |
- Aslani, Mohammad, et al.
(författare)
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Efficient and decision boundary aware instance selection for support vector machines
- 2021
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Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 577, s. 579-598
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Tidskriftsartikel (refereegranskat)abstract
- Support vector machines (SVMs) are powerful classifiers that have high computational complexity in the training phase, which can limit their applicability to large datasets. An effective approach to address this limitation is to select a small subset of the most representative training samples such that desirable results can be obtained. In this study, a novel instance selection method called border point extraction based on locality-sensitive hashing (BPLSH) is designed. BPLSH preserves instances that are near the decision boundaries and eliminates nonessential ones. The performance of BPLSH is benchmarked against four approaches on different classification problems. The experimental results indicate that BPLSH outperforms the other methods in terms of classification accuracy, preservation rate, and execution time. The source code of BPLSH can be found in https://github.com/mohaslani/BPLSH.
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| 8. |
- Beikmohammadi, Ali, 1995-, et al.
(författare)
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Accelerating actor-critic-based algorithms via pseudo-labels derived from prior knowledge
- 2024
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Ingår i: Information Sciences. - 0020-0255 .- 1872-6291. ; 661
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Tidskriftsartikel (refereegranskat)abstract
- Despite the huge success of reinforcement learning (RL) in solving many difficult problems, its Achilles heel has always been sample inefficiency. On the other hand, in RL, taking advantage of prior knowledge, intentionally or unintentionally, has usually been avoided, so that, training an agent from scratch is common. This not only causes sample inefficiency but also endangers safety –especially during exploration. In this paper, we help the agent learn from the environment by using the pre-existing (but not necessarily exact or complete) solution for a task. Our proposed method can be integrated with any RL algorithm developed based on policy gradient and actor-critic methods. The results on five tasks with different difficulty levels by using two well-known actor-critic-based methods as the backbone of our proposed method (SAC and TD3) show our success in greatly improving sample efficiency and final performance. We have gained these results alongside robustness to noisy environments at the cost of just a slight computational overhead, which is negligible.
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| 9. |
- Corizzo, Roberto, et al.
(författare)
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Multi-aspect renewable energy forecasting
- 2021
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Ingår i: Information Sciences. - Netherlands : Elsevier. - 0020-0255 .- 1872-6291. ; 546, s. 701-722
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Tidskriftsartikel (refereegranskat)abstract
- The increasing presence of renewable energy plants has created new challenges such as grid integration, load balancing and energy trading, making it fundamental to provide effective prediction models. Recent approaches in the literature have shown that exploiting spatio-temporal autocorrelation in data coming from multiple plants can lead to better predictions. Although tensor models and techniques are suitable to deal with spatio-temporal data, they have received little attention in the energy domain. In this paper, we propose a new method based on the Tucker tensor decomposition, capable of extracting a new feature space for the learning task. For evaluation purposes, we have investigated the performance of predictive clustering trees with the new feature space, compared to the original feature space, in three renewable energy datasets. The results are favorable for the proposed method, also when compared with state-of-the-art algorithms. © 2020 Elsevier Inc.
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| 10. |
- Davidsson, Paul, et al.
(författare)
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Distributed Monitoring and Control of Office Buildings by Embedded Agents
- 2005
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Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 171:4, s. 293-307
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Tidskriftsartikel (refereegranskat)abstract
- We describe a decentralized system consisting of a collection of software agents that monitor and control an office building. It uses the existing power lines for communication between the agents and the electrical devices of the building, such as sensors and actuators for lights and heating. The objectives are both energy saving and increasing customer satisfaction through value added services. Results of qualitative simulations and quantitative analysis based on thermodynamical modeling of an office building and its staff using four different approaches for controlling the building indicate that significant energy savings can result from using the agent-based approach. The evaluation also shows that customer satisfaction can be increased in most situations. The approach here presented makes it possible to control the trade-off between energy saving and customer satisfaction (and actually increase both, in comparison with current approaches).
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