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Correction to: A comprehensive performance evaluation of container migration strategies

Computing 2025年第4期107卷 1-2页

作者： Feitosa, Leonel Barbosa, Vandirleya Sabino, Arthur Lima, Luiz Nelson Fé, iure Silva, Luis Guilherme Callou, Gustavo Carvalho, Juliana Leão, Erico Nguyen, Tuan Anh Rego, Paulo Silva, Francisco Airton Laboratory of Applied Research to Distributed Systems (PASID) Federal University of Piauí (UFPI) Picos Brazil Department of Computing Federal Rural University of Pernambuco (UFRPE) Recife Brazil Department of Software Engineering Faculty of Information Technology University of Industry and Trade (HUIT) Ho Chi Minh City Vietnam Department of Computer Science Federal University of Ceará (UFC) Fortaleza Brazil

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Forecasting day-ahead electric power prices with functional data analysis

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Frontiers in Energy Research 2025年 13卷

作者： Jan, Faheem iftikhar, Hasnain Tahir, Mehwish Khan, Mehak Department of Mathematics and Statistics Bacha Khan University Charsadda Pakistan Department of Statistics Quaid-i-Azam University Islamabad Pakistan Department of Mathematics Shaheed Benazir Bhutto Women University Peshawar Pakistan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

Day-ahead electricity prices in today’s competitive electric power markets have complex features such as high frequency, high volatility, non-linearity, non-stationarity, mean reversion, multiple periodicities, and calendar effects. These complicated features make price forecasting difficult. To address this, this research examines the application of functional data analysis to forecasting day-ahead electric power prices. Compared to classical time series forecasting approaches, functional data analysis is more appealing since it anticipates the daily profile, allowing for short-term projections. This technique uses a functional autoregressive ((Formula presented.) AR) and a functional autoregressive with exogenous predictors ((Formula presented.) AR (Formula presented.)) model to predict the next-day electric power prices. in addition, standard time-series forecasting models, including autoregressive (AR) AR (Formula presented.), autoregressive integrated moving average (ARiMA), and ARiMA (Formula presented.) are also utilized for comparison. The model’s prediction performance was evaluated using data on electricity prices from the British electricity market, considering forecast error indicators and the same forecast statistical test. The results show that the proposed functional models ((Formula presented.) AR and (Formula presented.) AR (Formula presented.)) outperform standard time series models. in comparison to the benchmark models (AR, AR (Formula presented.), ARiMA, ARiMA (Formula presented.), and the proposed (Formula presented.) AR model), the (Formula presented.) AR (Formula presented.) model reduces: the day-ahead forecasting average MAPE by ranges of 5.02%–45.77%, 4.07%–40.63%, 3.80%–38.99%, 1.90%–24.22%, and 0.95%–13.78%;MAE by ranges of 9.43%–69.32%, 5.17%–65.48%, 6.04%–59.16%, 3.02%–42.01%, and 1.51%–26.59%;RMSE by ranges of 8.98%–40.97%, 6.68%–34.03%, 4.22%–24.58%, 3.91%–23.20%, and 2.30%–15.11%. Furthermore, compared with the literature-proposed b

关键词： classical time series models day-ahead electricity price forecasting electric power market functional data analysis functional time series models

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Parsoffensive: Persian Offensive Comments Dataset

Parsoffensive: Persian Offensive Comments Dataset

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international Conference on Web Research (iCWR)

作者： Ali Nazarizadeh Minoo Sayyadpour Omid Ebrahimkhani Amirmasoud iravani Golnaz Ghannadan Shirazi Mojgan Behravan Department of Computer Engineering Central Tehran Branch Islamic Azad University Tehran Iran Dept. Math and Computer Science Kharazmi University Tehran Iran Department of English Translation Studies Faculty of Persian Literature and Foreign Languages Allameh Tabataba'i University Tehran Iran Department of Linguistics Faculty of Letters and Humanities Ferdowsi University of Mashhad Mashhad Iran Department of Engineering Faculty of Computer Engineering Alzahra University Tehran Iran Department of IT& Technology IU International University of Applied science Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Offensive language detection is essential for maintaining respectful and inclusive online environments. By implementing detection systems, platforms aim to protect users from harmful content, curb the spread of hate speech, and promote positive interactions within digital communities. Leveraging natural language processing (NLP) techniques, this area has become a key focus in computational linguistics research and development. To address this challenge, we created the ParsOffensive dataset, consisting of approximately 8,433 Persian comments labeled as Offensive or Neutral. These comments were sourced from instagram and labeled by two linguists. Machine learning models were trained on this dataset, with the ParsBert model achieving 90% accuracy in detecting offensive speech in Persian.

关键词： Accuracy Social networking (online) Computational modeling Prevention and mitigation Hate speech Text categorization Natural language processing Web sites Multimedia communication Research and development

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Shunt Capacitor Bank Fault Detection and Localization Using Sub-Cycle Algorithm

Shunt Capacitor Bank Fault Detection and Localization Using ...

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iEEE Canada Electric Power Conference, EPEC

作者： Mohsen Tajdinian Behzad Behdani Ali Goodarzi Harold R. Chamorro Vijay K. Sood Department of Power and Control Engineering School of Electrical and Computer Engineering Shiraz University Shiraz Iran Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft the Netherlands Fars Electricity Distribution Company Fars Iran School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University Ontario Canada

ISBN: (纸本)9781665463195

Fast internal detection and location in Shunt Ca-pacitor Banks (SCBs) can lead to the prevention of damages to other SCBs' elements and consequently avoid undesirable performance and effects in power system operation. This paper targets the performance of phasor-based algorithms of failure detection and fault location of SCBs. Being dependent on the fundamental phasor components which usually are calculated based on the Discrete Fourier Transform (DFT), the failure detection and fault location algorithms suffer from almost one-cycle delay. This paper provides sub-cycle phasor estimation based on the least-square technique. The proposed algorithm is evaluated for different configurations of SCBs considering different fuse protection designs. The proposed method provides a criterion for relay decision-making in the case of multiple faulty phases condition. The proposed method is designed to monitor and detect consecutive failures based on the existing data of commercial relays. Performance evaluations are conducted under different circumstances namely voltage unbalance conditions and multiple internal fault locations.

关键词： Performance evaluation Fault detection Capacitors Discrete Fourier transforms Estimation Voltage Fault location

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CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images

arXiv

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arXiv 2023年

作者： Ali, Momina Liaqat Rauf, Zunaira Khan, Asifullah Sohail, Anabia Ullah, Rafi Gwak, Jeonghwan Pattern Recognition Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Center for Mathematical Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Department of Computer Science Faculty of Computing and Artificial Intelligence Air University IslamabadE-9 Pakistan Perak31750 Malaysia Department of Software Korea National University of Transportation Chungju27469 Korea Republic of

Transformers, due to their ability to learn long-range dependencies, have overcome the shortcomings of convolutional neural networks (CNNs) for global perspective learning. However, their multi-head attention module only captures global-level feature representations, which is insufficient for medical images. To address this issue, we propose a Channel Boosted Hybrid Vision Transformer (CB-HVT) that uses transfer learning to generate boosted channels and employs both transformers and CNNs to analyse lymphocytes in histopathological images. The proposed CB-HVT comprises five modules to effectively identify lymphocytes. its (i) channel generation module, uses the idea of channel boosting through transfer learning to extract diverse channels from different auxiliary learners. These boosted channels are first concatenated and ranked using an attention mechanism in the (ii) channel exploitation module. A fusion block is then utilized in the (iii) channel merging module for a gradual and systematic merging of the diverse boosted channels to improve the network’s learning representations. The CB-HVT also employs a proposal network in its (iv) region aware module followed by its (v) detection and segmentation head to accurately identify and distinguish objects, even in the regions with crowded presence and artifacts. We evaluated the proposed CB-HVT on two publicly available datasets for lymphocyte assessment in histopathological images. The results demonstrate that CB-HVT has a good generalization on unseen data therefore, it can serve as a valuable tool for pathologists. Copyright © 2023, The Authors. All rights reserved.

关键词： Lymphocytes

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Time-Series Anomaly Detection of Mozi Malware in ioT Devices Using Arima and Local Outlier Factor

Time-Series Anomaly Detection of Mozi Malware in IoT Devices...

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international Symposium on applied Computational intelligence and informatics ( SACi)

作者： Tünde Kaufman Jozsef Katona National Media and Infocommunications Authority Budapest Hungary Kandó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary

ISBN: (数字)9798331515478

ISBN: (纸本)9798331515485

The increasing demand for ioT devices in industries has generated a high level of cybersecurity threats, with botnets including Mozi exploiting poor passwords and unpatched vulnerabilities to target networked infrastructure. Peer-to-peer (P2P) topology of Mozi, employing Distributed Hash Tables (DHTs) for Command-and-Control (C2) communications, makes it resistant to takedown operations and capable of high-scale Distributed Denial-of-Service (DDoS) attack, data exfiltration, and remote command execution. Traditional one-class, machine learning techniques such as OneClass Support Vector Machine (OCSVM) and Local Outlier Factor (LOF) have been widely used for discovering malwarerelated anomalies. However, such techniques require high labelled training datasets, whose availability in dynamically changing environments of malwares proves challenging. in this work, an AutoRegressive integrated Moving Average (ARiMA)-based mechanism for discovering time-series anomalies in ioT-infected devices with Mozi infection is proposed. in an experimental setup using a Raspberry Pi running Pi-hole, baseline profiles of CPU consumption, RAM usage, and network activity are first established under normal conditions. Next, a system is subjected to a simulation of infection with Mozi malware and monitored for deviation with ARiMA forecasting and Local Outlier Factor (LOF) for discovering anomalies. By comparing predicted values with actual values, effective intrusion detection for malware-related anomalies is attained, providing a real-time, adaptive mechanism for early intrusion detection. in conclusion, with its effectiveness in discovering stealthy behaviour of malwares, employing ARiMA for discovering time-series anomalies in ioTinfected devices proves effective, and its employment in ioT cybersecurity aids in providing real-time mechanism for discovering threats for system admins and security professionals. As future work, a combination of ARiMA with deep neural networks such as Long

关键词： Training Botnet intrusion detection Malware Real-time systems Peer-to-peer computing Topology internet of Things Long short term memory Anomaly detection

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Exploiting Partial Observability and Optimized Simple State Representations in Deep Q-Learning

Exploiting Partial Observability and Optimized Simple State ...

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Frontiers of information Technology (FiT)

作者： Danyaal Mahmood Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

The advent of deep Q-learning has opened up new possibilities in training autonomous agents to perform intelligently in intricate settings. This research work examines the potential of deep Q-learning in the paradigmatic Snake game, which requires an agent to navigate a grid, ingest food items, and avoid collisions. We incorporated a partially observable game state, introducing a novel level of difficulty to the task. Our proposed approach utilizes a straightforward feed-forward neural network to estimate Q-values of potential actions, given the current state. The agent progressively learns to balance between exploration and exploitation, allowing it to navigate optimally in the game scenario. Most existing studies in deep Q-learning for the Snake game overlook the complexities posed by partial observability, focusing mainly on fully observable state representations. Furthermore, the majority of the investigations delve into the efficacy of complex neural network architectures, leaving a gap in the understanding of the performance of more simplistic feed-forward networks. Our study fills this gap by offering an in-depth exploration and analysis of deep Q-learning strategies, especially when applied to partially observable game states and simple neural networks. Simulations and results reveal that adopting simpler state representations can significantly boost the agent’s performance in the Snake game. A more detailed and intricate reward function is also observed to enhance the agent’s decision-making skills and overall proficiency in the game. These observations underscore the significance of optimizing state representations and reward functions for maximizing the performance of deep Q-learning agents in the Snake game.

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PCA: Semi-Supervised Segmentation with Patch Confidence Adversarial Training

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iEEE Transactions on Network science and engineering 2025年第4期12卷 2473-2486页

作者： Xu, Zhenghua Yang, Runhe Xu, Zihang Zhang, Shuo Yang, Yuchen Liu, Weipeng Xu, Weichao Chen, Junyang Lukasiewicz, Thomas Leung, Victor C. M. Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Tianjin China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The Johns Hopkins University Department of Applied Mathematics and Statistic Baltimore United States Hebei University of Technology School of Artificial Intelligence Tianjin China Hebei Provincial Hospital of Traditional Chinese Medicine Digestive Disease Center Shijiazhuang China Shenzhen University College of Computer Science and Software Engineering Shenzhen China University of Oxford Department of Computer Science University of Oxford United Kingdom Department of Computer Science United Kingdom Vienna University of Technology institute of Logic and Computation nstitute of Logic and Computation Vienna Austria

Deep-learning-based semi-supervised learning (SSL) methods have achieved a strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions. We argue that the current performance restrictions for such approaches are the problems of feature extraction and learning preferences. in this paper, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. The PCA method's discriminator penalizes patch-level structures, guiding the generator to optimize different patch areas, by leveraging pixel context, the generator is driven to focus on high-frequency features, making it harder to deceive the discriminator and easy to converge to an ideal state, which more effectively guides the segmenter to generate high-quality pseudo-labels. Furthermore, at the discriminator's input, we supplement image information constraints, making it simpler to fit the expected data distribution. Extensive experiments on the Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019 challenge dataset show that our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation. © 2025 iEEE. All rights reserved.

关键词： Adversarial machine learning

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Benchmarking Cryptographic Protocols for (ioT) Malware Defence

Benchmarking Cryptographic Protocols for (IoT) Malware Defen...

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international Symposium on applied Machine intelligence and informatics (SAMi)

作者： Khalil Ben Khaled Dönìz Borsos Jozsef Katona Kandó Kalmán Faculty of Electrical Engineering Institute of Microelectronics and Technology Obuda University Budapest Hungary Kándó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This study presents a comprehensive benchmarking analysis of cryptographic protocols for internet of Things (ioT) malware defense. The framework was specifically tailored to evaluate cryptographic protocols such as AES-128, AES-256, ChaCha20, RSA (1024-bit, 2048-bit, and 4096-bit), SHA256, SHA512, and HMAC-SHA256 were tested in both standalone and emulated environments. The primary objective was to evaluate the performance and resource consumption of these protocols, focusing on their encryption and hashing efficiency. Key innovations include the comparative analysis of resource consumption and performance efficiency across diverse cryptographic operations, under both real-world and emulated conditions. By identifying protocols like ChaCha20 for high efficiency and minimal resource usage, and RSA 4096-bit for enhanced security at higher computational costs, this study provides actionable insights into the trade-offs between security and performance. These findings offer a foundational reference for selecting optimized cryptographic protocols, advancing ioT malware defense strategies through informed decision-making.

关键词： Performance evaluation Technological innovation Benchmark testing Malware Encryption internet of Things Reliability informatics Machine intelligence Cryptographic protocols

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Magnetically tuned continuous transition from weak to strong coupling in terahertz magnon polaritons

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Physical Review Research 2023年第1期5卷 L012039-L012039页

作者： Andrey Baydin Kenji Hayashida Takuma Makihara Fuyang Tay Xiaoxuan Ma Wei Ren Guohong Ma G. Timothy Noe, ii ikufumi Katayama Jun Takeda Hiroyuki Nojiri Shixun Cao Motoaki Bamba Junichiro Kono Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Smalley-Curl Institute Rice University Houston Texas 77005 USA Division of Applied Physics Graduate School of Engineering Hokkaido University Sapporo Hokkaido 060-8628 Japan Department of Physics and Astronomy Rice University Houston Texas 77005 USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas 77005 USA Department of Physics International Center of Quantum and Molecular Structures and Materials Genome Institute Shanghai University 200444 Shanghai China Department of Physics Graduate School of Engineering Science Yokohama National University Yokohama 240-8501 Japan Institute for Materials Research Tohoku University Sendai 980-8577 Japan The Hakubi Center for Advanced Research Kyoto University Kyoto 606-8502 Japan Department of Physics I Kyoto University Kyoto 606-8502 Japan PRESTO Japan Science and Technology Agency Kawaguchi 332-0012 Japan Department of Materials Science and NanoEngineering Rice University Houston Texas 77005 USA

Depending on the relative rates of coupling and dissipation, a light-matter coupled system is either in the weak- or strong-coupling regime. Here, we present a unique system where the coupling rate continuously increases with an externally applied magnetic field while the dissipation rate remains constant, allowing us to monitor a weak-to-strong coupling transition as a function of magnetic field. We observed a Rabi splitting of a terahertz magnon mode in yttrium orthoferrite above a threshold magnetic field of ∼14 T. Based on a microscopic theoretical model, we show that with increasing magnetic field the magnons transition into magnon polaritons through an exceptional point, which will open up new opportunities for in situ control of non-Hermitian systems.

关键词： Antiferromagnetism High magnetic fields Magneto-optics Magnons Polaritons Non-Hermitean systems Terahertz time-domain spectroscopy Transfer matrix method

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