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Power Tech Conference

作者： Kyriakos Kyriakou Lenos Hadjidemetriou Christos Panayiotou KIOS Research and Innovation Center of Excellence University of Cyprus Department of Electrical and Computer Engineering University of Cyprus

A massive deployment of renewable energy sources (RESs) is required to enable the decarbonization of the energy infrastructure. The replacement of conventional generators with inverter-based RESs reduces the rotational inertia and can threaten the frequency stability of the system. This work is motivated by analyzing an actual cascading event in a low-inertia system where a voltage sag event that was cleared within few milliseconds triggers a severe frequency disturbance. During a low voltage sag event, the reactive power is prioritized for voltage support purposes according to the existing low voltage fault ride through (LVFRT) grid regulations, which lead to a significant reduction of active power by RESs that triggers the cascading phenomenon. This event indicates that the existing LVFRT regulations require re-evaluation, especially in low-inertia power systems with increased penetration of RESs. Therefore, the existing grid regulations are thoroughly analyzed with a particular emphasis on the coupling phenomena where a voltage support can threat the frequency stability. Moreover, a new LVFRT strategy is proposed where active power prioritization is introduced for enabling an intense voltage support without causing a significant reduction on active power. The existing and proposed LVFRT schemes are benchmarked considering theoretical and simulation-based analysis indicating that the proposed scheme provides improved support and is adequate for low-inertia power systems with intense RESs penetration.

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A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models

arXiv

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arXiv 2024年

作者： Dang, Cuong Le, Dung D. Le, Thai FPT Software AI Center Viet Nam College of Engineering and Computer Science VinUniversity Viet Nam Department of Computer Science Indiana University United States

Existing works have shown that fine-tuned textual transformer models achieve state-of-the-art prediction performances but are also vulnerable to adversarial text perturbations. Traditional adversarial evaluation is often done only after fine-tuning the models and ignoring the training data. In this paper, we want to prove that there is also a strong correlation between training data and model robustness. To this end, we extract 13 different features representing a wide range of input fine-tuning corpora properties and use them to predict the adversarial robustness of the fine-tuned models. Focusing mostly on encoder-only transformer models BERT and RoBERTa with additional results for BART, ELECTRA, and GPT2, we provide diverse evidence to support our argument. First, empirical analyses show that (a) extracted features can be used with a lightweight classifier such as Random Forest to predict the attack success rate effectively, and (b) features with the most influence on the model robustness have a clear correlation with the robustness. Second, our framework can be used as a fast and effective additional tool for robustness evaluation since it (a) saves 30x-193x runtime compared to the traditional technique, (b) is transferable across models, (c) can be used under adversarial training, and (d) robust to statistical randomness. Our code is publicly available at https://***/CaptainCuong/RobustText_ACL2024. © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

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A LiDAR-Based Method to Identify Vegetation Encroachment in Power Networks with UAVs

A LiDAR-Based Method to Identify Vegetation Encroachment in ...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Antonis Savva Manos Papageorgiou Christos Kyrkou Panayiotis Kolios Theocharis Theocharides Christos Panayiotou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

Vegetation encroachment in power transmission and distribution networks constitutes a major hazard for the environment and the networks’ integrity, but also for the society at large, with multifaceted consequences. On many occasions the vegetation near the power lines, in conjunction with the aged infrastructure, caused and spread fires leading to large-scale disasters. To this end, 3D representations are proactively created using LiDAR sensors to identify locations of vegetation encroachment. Of particular interest is the use of UAVs, which propose a cost-effective alternative to employing airplanes. In this study, UAVs were employed to acquire LiDAR data from the power distribution network and a subtractive data-driven methodology is proposed, whereby irrelevant points are discarded, aiming to identify power lines without employing 3D modelling methods. In this context, geometric features are calculated and a rigorous analysis is conducted over the feature set, different classifiers and parameters to investigate the robustness of the proposed approach. Extensive evaluation suggests that the Random Forest classifier is able to identify power lines with high performance (F1-Score=97.74% and Accuracy=99.09%), using both geometric and color-based features, being also robust to the presence of moderate noise and down-sampling levels.

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UAV-based system for real-time wildfire perimeter propagation tracking

UAV-based system for real-time wildfire perimeter propagatio...

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Mediterranean Conference on Control and Automation (MED)

作者： Constantinos Heracleous Panayiotis Kolios Christos G. Panayiotou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

Real-time wildfire perimeter tracking provides situational awareness and enhances decision-making during firefighting. This paper proposes a UAV-based system that integrates real-time data collection (using onboard sensors) into a fire propagation model to provide accurate state information on the wildfire perimeter and improve fire prediction. Firstly, a data fusion scheme is devised to employ available historical data in combination with real-time measurements to provide updated inputs to the fire propagation model. Then the model is used to predict the future fire perimeter and uses these predictions to guide the UAV to track the fire perimeter better. The proposed system is evaluated in extensive simulation experiments, demonstrating its effectiveness for real-time wildfire perimeter propagation tracking.

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Machine Learning for Real-Time Anomaly Detection in Optical Networks

arXiv

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arXiv 2023年

作者： Behera, Sadananda Panayiotou, Tania Ellinas, Georgios KIOS Research Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

This work proposes a real-time anomaly detection scheme that leverages the multi-step ahead prediction capabilities of encoder-decoder (ED) deep learning models with recurrent units. Specifically, an encoder-decoder is used to model soft-failure evolution over a long future horizon (i.e., for several days ahead) by analyzing past quality-of-transmission (QoT) observations. This information is subsequently used for real-time anomaly detection (e.g., of attack incidents), as the knowledge of how the QoT is expected to evolve allows capturing unexpected network behavior. Specifically, for anomaly detection, a statistical hypothesis testing scheme is used, alleviating the limitations of supervised (SL) and unsupervised learning (UL) schemes, usually applied for this purpose. Indicatively, the proposed scheme eliminates the need for labeled anomalies, required when SL is applied, and the need for on-line analyzing entire datasets to identify abnormal instances (i.e., UL). Overall, it is shown that by utilizing QoT evolution information, the proposed approach can effectively detect abnormal deviations in real-time. Importantly, it is shown that the information concerning soft-failure evolution (i.e., QoT predictions) is essential to accurately detect anomalies. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Neural Network Visualization in Virtual Reality: A Use Case Analysis and Implementation

Neural Network Visualization in Virtual Reality: A Use Case...

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Thematic area Human Interface and the Management of Information, HIMI 2022 Part of the 24th HCI International Conference, HCII 2022

作者： Queck, Dirk Wohlan, Annika Schreiber, Andreas Department of Electrical and Computer Engineering Serious Games Engineering TU Kaiserslautern Paul-Ehrlich-Straße Kaiserslautern67663 Germany Institute for Software Technology German Aerospace Center Linder Hoehe Cologne51147 Germany

ISBN: (纸本)9783031064234

software systems and components increasingly rely on machine learning methods such as Artificial Neural Networks (ANNs) to provide intelligent functionality. Therefore, software developers and machine learning users should have a basic understanding of such methods. However, since ANNs are complex and thus challenging to grasp, novel visualization approaches can contribute to a better comprehension. We conducted an online survey to identify use cases and requirements for visualizing ANNs. Based on our results we designed and implemented an ANN visualization in virtual reality (VR) specifically targeted at machine learning users. Our approach is particularly suitable for teaching purposes and machine learning novices or non-experts who want to get an impression of the general functionality of neural networks. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural networks

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Integrating Bidirectional Long Short-Term Memory with Subword Embedding for Authorship Attribution

Integrating Bidirectional Long Short-Term Memory with Subwor...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Modupe, Abiodun Celik, Turgay Marivate, Vukosi Olugbara, Oludayo O. University of Pretoria Department of Computer Science South Africa School of Electrical Information Engineering University of the Witwatersrand Johannesburg South Africa Mict Seta Center of Excellence in 4IR Durban University of Technology Durban South Africa

ISBN: (纸本)9798350337020

The problem of unveiling the author of a given text document from multiple candidate authors is called authorship attribution. Manifold word-based stylistic markers have been successfully used in deep learning methods to deal with the intrinsic problem of authorship attribution. Unfortunately, the performance of word-based authorship attribution systems is limited by the vocabulary of the training corpus. Literature has recommended character-based stylistic markers as an alternative to overcome the hidden word problem. However, character-based methods often fail to capture the sequential relationship of words in texts which is a chasm for further improvement. The question addressed in this paper is whether it is possible to address the ambiguity of hidden words in text documents while preserving the sequential context of words. Consequently, a method based on bidirectional long short-term memory (BLSTM) with a 2-dimensional convolutional neural network (CNN) is proposed to capture sequential writing styles for authorship attribution. The BLSTM was used to obtain the sequential relationship among characteristics using subword information. The 2-dimensional CNN was applied to understand the local syntactical position of the style from unlabeled input text. The proposed method was experimentally evaluated against numerous state-of-the-art methods across the public corporal of CCAT50, IMDb62, Blog50, and Twitter50. Experimental results indicate accuracy improvement of 1.07%, and 0.96%, on CCAT50 and Twitter, respectively, and produce comparable results on the remaining datasets. © 2023 IEEE.

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Efficient Cartesian Genetic Programming-Based Automatic Synthesis Framework for Reversible Quantum-Flux-Parametron Logic Circuits

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Fu, Rongliang Wille, Robert Yoshikawa, Nobuyuki Ho, Tsung-Yi The Chinese University of Hong Kong Department of Computer Science and Engineering 999077 Hong Kong Technical University of Munich Munich80333 Germany Software Competence Center Hagenberg GmbH Hagenberg4232 Austria Yokohama National University Department of Electrical and Computer Engineering Yokohama240-8501 Japan

Reversible computing has garnered significant attention as a promising avenue for achieving energy-efficient computing systems, particularly within the realm of quantum computing. The reversible quantum-flux-parametron (RQFP) is the first practical reversible logic gate utilizing adiabatic superconducting devices, with experimental evidence supporting both its logical and physical reversibility. Each RQFP logic gate operates on alternating current (AC) power and features three input ports and three output ports. Notably, each output port is capable of implementing a majority function while driving only a single fan-out. Additionally, the three inputs to each gate must arrive in the same clock phase. These inherent characteristics present substantial challenges in the design of RQFP logic circuits. To address these challenges, this paper proposes an automatic synthesis framework for RQFP logic circuit design based on efficient Cartesian genetic programming (CGP). The framework aims to minimize both the number of RQFP logic gates and the number of garbage outputs within the generated RQFP logic circuit. It incorporates the specific characteristics of the RQFP logic circuit by encoding them into the genotype of a CGP individual. It also introduces several point mutation operations to facilitate the generation of new individuals. Furthermore, the framework integrates circuit simulation with formal verification to assess the functional equivalence between the parent and its offspring. Experimental results on RevLib and reversible reciprocal circuit benchmarks demonstrate the effectiveness of our framework. © 2025 IEEE. All rights reserved.

关键词： Genetic programming

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An Efficient Steganographic Approach for Securing Text Transmission Using S-Box in Cloud 2

An Efficient Steganographic Approach for Securing Text Trans...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ashraf, Humaira Malik, Javeria Ihsan, Uswa Al-Quayed, Fatima Humayun, Mamoona Jhanjhi, N.Z. International Islamic University Department of Computer Science and Software Engineering Islamabad Pakistan Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Information Systems College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Taylor's University Malaysia Applied Science Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

In era of modern world and technology where everything is digitalized. Cloud computing emerged as one of the fastest growing parts of digital industry. With the Succor of cloud computing models, business accomplishes their objectives with least exertion when contrasted with traditional processing environment. In spite of these compensations, yet security of the information in cloud database is the significant fear in endorsement of cloud. Although several techniques have been proposed such as cryptographic and steganographic approaches, but all of these have some loopholes. Therefore, in this paper new data protection technique based on steganographic encryption is proposed which has less computational time and secure against known plain text and Man-In-Middle Attack. © 2023 IEEE.

关键词： Cryptography

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Fuzzy Logic-based Steganographic Scheme for high Payload Capacity with high Imperceptibility 11

Fuzzy Logic-based Steganographic Scheme for high Payload Cap...

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11th International Symposium on Digital Forensics and Security, ISDFS 2023

作者： Theophile, Ingororano De La Croix, Ntivuguruzwa Jean Ahmad, Tohari Yeungnam University Department of Computer Engineering Gyeongsan Korea Republic of Adventist University of Central Africa Department of Information Management Kigali Rwanda Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia University of Rwanda African Center of Excellence in Internet of Things Kigali Rwanda

ISBN: (纸本)9798350336986

When communicating confidential information between two parties, it is crucial to consider the security of the information. Cryptography, as well as steganography, are essentially used to keep hidden, secret data. Cryptography mainly focuses on changing private messages into an unreadable format. However, cryptography is questionable because it shows the existence of covert communication;hence there is a need to use it combined with other more secure techniques. On the other hand, steganography conceals confidential data in a cover media to avoid suspicion of covert communication. Most practitioners use different steganographic algorithms selected based on their capacity to carry large payloads with a quality cover image. This paper introduces a new algorithm to hide confidential data using fuzzy logic in digital images with increased payload and high imperceptibility. We use fuzzy logic to identify an image's edges, which shows the level with which a pixel is attached to any edge during image processing;then, we apply a factor to the data to hide before embedding it to the pixels with low values. Our method effectively conceals large amounts of secret bits while producing a quality stego image that ensures confidentiality in communication through the insecure public network. Experimental results demonstrate the outperformance of our method over the existing schemes for both the peak signal-to-noise ratio and the payload size. © 2023 IEEE.

关键词： Fuzzy logic

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