bookmarks (hide)61
- Abstract page for arXiv paper 2304.01300: On Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel Approach
- Abstract page for arXiv paper 2411.10152: Causal Time-Series Synchronization for Multi-Dimensional Forecasting
- Abstract page for arXiv paper 2411.19713: CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures
- Abstract page for arXiv paper 2501.07952: Spiking Neural Network Accelerator Architecture for Differential-Time Representation using Learned Encoding
- Abstract page for arXiv paper 2501.19234: Hourly Short Term Load Forecasting for Residential Buildings and Energy Communities
- Abstract page for arXiv paper 2503.08502: The Space Between: On Folding, Symmetries and Sampling
- Predictive maintenance is a crucial strategy in smart industries and plays an important role in small and medium-sized enterprises (SMEs) to reduce the unexpected breakdown. Machine failures are due to unexpected events or anomalies in the system. Different anomaly...
- The constantly increasing electricity and energy demand in residential buildings, as well as the need for higher absorption rates of renewable sources of energy, demand for an increased flexibility at the end-users. This need is further reinforced by the rising numbers of residential Photovoltaic (PV) and battery-storage systems. In this case, flexibility can be viewed as the excess energy that can be charged to or discharged from a battery, in response to a group objective of several such battery-storage systems (aggregation). One such group objective considered in this paper includes marketing flexibility (charging or discharging) to the Day-ahead (DA) spot market, which can provide both a) financial incentives to the owners of such systems, and b) an increase in the overall absorption rates of renewable energy. The responsible agent for marketing and offering such flexibility, herein aggregator, is directly controlling the participating batteries, in exchange to some financial compensation of the owners of these batteries. In this paper, we present an optimization framework that allows the aggregator to optimally exchange the available flexibility to the DA market. The proposed scheme is based upon a reinforcement-learning approach, according to which the aggregator learns through time an optimal policy for bidding flexibility to the DA market. By design, the proposed scheme is flexible enough to accommodate the possibility of erroneous forecasts (of weather, load or electricity price). Finally, we evaluate our approach on real-world data collected from currently installed battery-storage systems in Upper Austria.
- Reflective algorithms are algorithms that can modify their own behaviour. Recently a behavioural theory of reflective algorithms has been developed, which shows that they are captured by reflective abstract state machines (rASMs). Reflective ASMs ...
- In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic approach is introduced in this article. A unified approach to privacy-preserving interpretable and transferable learning is considered for studying and optimizing the trade-offs between the privacy, interpretability, and transferability aspects of trustworthy AI. A variational membership-mapping Bayesian model is used for the analytical approximation of the defined information theoretic measures for privacy leakage, interpretability, and transferability. The approach consists of approximating the information theoretic measures by maximizing a lower-bound using variational optimization. The approach is demonstrated through numerous experiments on benchmark datasets and a real-world biomedical application concerned with the detection of mental stress in individuals using heart rate variability analysis.
- We introduce a novel copy-protection method for industrial control software. With our method, a program executes correctly only on its target hardware and behaves differently on other machines. The hardware-software binding is based on Physically Unclonable...
publications
(hide)83
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- (2024)
- Communications in Computer and Information Science, Springer International Publishing, (2022)
- Database and Expert Systems Applications - DEXA 2022 Workshops, page 22--31. Cham, Springer International Publishing, (2022)
- Foundations of Information and Knowledge Systems, page 237--255. Cham, Springer Nature Switzerland, (2024)
- (2024)
- Proceedings of the 15th International Working Conference on Variability Modelling of Software-Intensive Systems, ACM, (February 2021)
- Theoretical Computer Science, (2016)
- Software Quality as a Foundation for Security, page 153--169. Cham, Springer Nature Switzerland, (2024)
- Frontiers in Big Data, (March 2022)
- Database and Expert Systems Applications - DEXA 2023 Workshops, page 67--78. Cham, Springer Nature Switzerland, (2023)
- IEEE Transactions on Fuzzy Systems, 29 (12): 3873-3886 (December 2021)
- Algorithms, 16 (9): 450 (September 2023)
- 1An optimal $łess$mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si17.svg"$\greater$$łess$mml:mrow$\greater$$łess$mml:mo stretchy="true"$\greater$($łess$/mml:mo$\greater$$łess$mml:mi$\greater$∊$łess$/mml:mi$\greater$$łess$mml:mo$\greater$,$łess$/mml:mo$\greater$$łess$mml:mi$\greater$$\updelta$$łess$/mml:mi$\greater$$łess$mml:mo stretchy="true"$\greater$)$łess$/mml:mo$\greater$$łess$/mml:mrow$\greater$$łess$/mml:math$\greater$-differentially private learning of distributed deep fuzzy modelsInformation Sciences, (February 2021)
- Information Sciences, (2021)
- Lecture Notes in Computer Science, Springer International Publishing, (2020)
- Journal of Systems and Software, (2023)
- Micromachines, 14 (2): 442 (February 2023)
- Balancing Software Innovation and Regulatory Compliance, page 51--71. Cham, Springer Nature Switzerland, (2025)
- Frontiers in Chemical Engineering, (June 2021)
- (2020)
