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arXiv 2025年

作者： Chen, Chen Guan, Peiyuan Chen, Ziru Taherkordi, Amir Hou, Fen Cai, Lin X. Department of Computer Science and Technology University of Cambridge United Kingdom Department of Informatics University of Oslo Norway Department of Electrical and Computer Engineering Illinois Institute of Technology United States Department of Electrical and Computer Engineering University of Macau China

Serverless computing adopts a pay-as-you-go billing model where applications are executed in stateless and short-lived containers triggered by events, resulting in a reduction of monetary costs and resource utilization. However, existing platforms do not provide an upper bound for the billing model which makes the overall cost unpredictable, precluding many organizations from managing their budgets. Due to the diverse ranges of serverless functions and the heterogeneous capacity of edge devices, it is challenging to receive near-optimal solutions for deployment cost in a polynomial time. In this paper, we investigated the function scheduling problem with a budget constraint for serverless computing in wireless networks. Users and IoT devices are sending requests to edge nodes, improving the latency perceived by users. We propose two online scheduling algorithms based on reinforcement learning, incorporating several important characteristics of serverless functions. Via extensive simulations, we justify the superiority of the proposed algorithm by comparing with an ILP solver (Midaco). Our results indicate that the proposed algorithms efficiently approximate the results of Midaco within a factor of 1.03 while our decision-making time is 5 orders of magnitude less than that of Midaco. © 2025, CC BY.

关键词： Budget control

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Modelling and control of a multi-product pipeline transportation process

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International Journal of Information Technology (Singapore) 2025年 1-11页

作者： Jiménez-Cabas, Javier Piñeres-Espitia, Gabriel Rodelo-Serrano, Esteban Pamucar, Dragan Ramirez-Leon, Hermes Butt, Shariq Aziz Naz, Sumera Simic, Vladimir Computer Science and Electronic Department Universidad de La Costa Barranquilla Colombia Departamento de Ingeniería Mecánica Universidad Simón Bolívar Barranquilla Venezuela Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Industrial Engineering & Management Yuan Ze University Taoyuan Taiwan Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade 11010 Serbia Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea School of Systems and Technology Department of Computer Science University of Management and Technology Lahore Pakistan Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan

One of the most critical processes in the petroleum industry is transporting crude oil and its derivatives. Usually, it is done, due to easiness and economic aspects, through pipelines;several products, such as gasoline, kerosene, naphtha, and liquefied petroleum gas, among others, are moved in batch processes to both distribution and storage centers or transport ships. Since these transport systems transport different products, many works have focused on developing optimization strategies for scheduling these pipelines. That is, strategies that seek to optimize the use of the pipeline by establishing at what time points certain products must be transported. These strategies would be located at the planning level in the automation pyramid. In this work, on the other hand, different control strategies are proposed for a pipeline, but at the operation level. These strategies would be located at the control level, below the planning strategies in the automation pyramid. A pipeline of three pipe sections is modeled to develop the proposed control strategies, and the mathematical model is validated with actual data. The results show that, first, the proposed model approximates the actual behavior of the pipeline quite well and, second, that the combination of PID and feedforward control is an excellent alternative to implement control strategies for the operation of liquid transportation systems through pipelines. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： control strategies liquid transportation modeling of system

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A DNN Framework on Waveform Design for Over-the-Air Computation

A DNN Framework on Waveform Design for Over-the-Air Computat...

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IEEE Conference on Wireless Communications and Networking

作者： Nikos G. Evgenidis Nikos A. Mitsiou Sotiris A. Tegos Panagiotis D. Diamantoulakis Panagiotis Sarigiannidis George K. Karagiannidis Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Informatics and Telecommunication Engineering University of Western Macedonia Kozani Greece

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

One of the main applications expected to be enabled by next-generation networks is computing. The goal-oriented nature of computing allows the use of different implementation techniques, with over-the-air (OTA) computation being one of the main proposed schemes due to its effective resource management and computational efficiency. In this work, we aim at optimizing the waveform of the system in the presence of intersymbol interference (ISI) and sampling error. To this end, we propose a deep neural network (DNN) framework that generates an optimal waveform that minimizes the mean square error (MSE) of the OTA computation system. To ensure that the generated waveform exhibits the same behavior as other common waveforms, weighted energy and spectrum constraints are included in the loss function of the training phase. To better mitigate ISI, the spectrum constraint integrates the roll-off factor of the waveform, allowing for the generation of different waveforms. Simulation results verify that the desired constraints are met and show a significant performance gain over state-of-the-art waveforms.

关键词： Training Wireless networks Simulation Intersymbol interference Artificial neural networks Mean square error methods Performance gain Computational efficiency Resource management Next generation networking

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Performance and Implementation Comparison of Knuth-Morris-Pratt and Boyer-Moore String Search Algorithms

Performance and Implementation Comparison of Knuth-Morris-Pr...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Taj Saleh Fatma Corut Ergin Malek Malkawi Reda Alhajj Department of Computer Engineering Marmara University Istanbul Turkey Department of Computer Engineering Istanbul Medipol University Istanbul Turkey Department of Computer Science University of Calgary Alberta Canada Department of Heath Informatics University of Southern Denmark Odense Denmark

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

String search algorithms play an important role in many research areas such as data mining and bioinformatics. While there exist a number of algorithms that handles the topic, we are exploring the the Knuth-Morris-Pratt (KMP) and Boyer-Moore algorithms due to their efficiency and versatility. In this work, we compared the algorithms in terms of characteristics, performance and implementation details. We also tested both the algorithms with various patterns and texts that differs in size. We also analyzed the performance of the algorithms on 4 different processors to understand the technological advancements effects on their performance. Our findings suggest that the BM algorithm perform better with large texts and patterns, while the KMP algorithm is better suited for smaller ones. Also, while newer processor generally exhibit improved performance, the significance of these enhancements may vary. Thus, we should rather be looking specific architectural advancements within generations rather than focusing solely on the generational gap.

关键词： Technological innovation Program processors Smart cities Focusing Data mining Bioinformatics Pattern matching

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ChainSafari: A General and Efficient Blockchain Verifiable Query Scheme with Real-Time Synchronization 24th

ChainSafari: A General and Efficient Blockchain Verifiable ...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xu, Yuwei Dai, Shengjiang Zeng, Junyu Cao, Jie He, Ran Xiang, Qiao School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China Department of Electrical and Computer Engineering Queen’s University Kingston Canada School of Informatics Xiamen University Xiamen China

ISBN: (纸本)9789819615414

With the rapid advancement of blockchain technology, its utilization has become widespread across various domains, resulting in the accumulation of significant volumes of valuable data. This trend has created an increasing demand for efficient querying of blockchain data. However, the inherent structure of blockchain leads to low querying efficiency and limited support for complex queries. Therefore, there is a trend toward migrating data to off-chain storage for querying purposes. The untrustworthy nature of the off-chain environment necessitates data validation. However, existing validation schemes incur high costs and fail to ensure the timely updating of the off-chain data. To address the challenges above, we propose ChainSafari, a general, efficient, and real-time synchronized scheme for verifiable querying on the blockchain. In our scheme, we design a general data model for storing transactions of various blockchains and develop an efficient authenticated data structure (ADS) based on this data model, enabling efficient verifiable queries for multiple data types in parallel. Furthermore, we propose a multi-party synchronization detection and recovery protocol to ensure the consistency of on-chain and off-chain data. Finally, we implement a prototype system of ChainSafari and conduct extensive experiments. The experimental results demonstrate the superior query throughput and enhanced data validation efficiency of ChainSafari compared to existing schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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Cyber Risk Identification and Classification-Based Load Forecasting Tool for Pandemic Situations

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IET Cyber-Physical Systems: Theory and Applications 2025年第1期10卷

作者： Shivran, Kuldeep Singh Swire-Thompson, Kyle Saxena, Neetesh Das, Sarasij Department of Electrical Engineering Indian Institute of Science Bangalore India School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

Smart grid operators use load forecasting algorithms to predict energy load for the reliable and economical operation of the electricity grid. COVID-19 pandemic-like situations (PLS) can significantly impact energy load demand due to uncertainties in factors such as regulatory orders, pandemic severity and human behavioural patterns. Additionally, in a smart grid, cyberattacks can manipulate forecasted load data, leading to suboptimal decisions, economic losses and potential blackouts. Forecasting load during these situations is challenging for traditional load forecasting tools, as they struggle to identify cyberattacks amidst uncertain load demand, where cyberattacks may mimic pandemic-like load patterns. Traditional forecasting methods do not incorporate factors related to pandemics and cyberattacks. Recent studies have focused on forecasting by considering factors such as COVID-19 cases, social distancing, weather, and temperature but fail to account for the impact of regulatory orders and pandemic severity. They also lack the ability to differentiate between normal and anomalous forecasts and classify the type of attack in anomalous data. This paper presents a tool for short-term load forecasting, anomaly detection and cyberattack classification for pandemic-like situations (PLS). The proposed short-term load forecasting algorithm uses a weighted moving average and an adjustment factor incorporating regulatory orders and pandemic severity, making it computationally efficient and deterministic. Additionally, the proposed anomaly detection and cyberattack classification algorithm provides robust options for detecting anomalies and classifying various types of cyberattacks. The proposed tool has been evaluated using K-Fold cross-validation to improve generalisability and reduce overfitting. The mean squared error (MSE) was used to measure prediction accuracy and detect discrepancies. It has been analysed and tested on real-load data from the State Load Dispatch Ce

关键词： Mean square error

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DataJudge: Cross-Chain Data Consistency Verification Based on Extended Merkle Hash Tree 24th

DataJudge: Cross-Chain Data Consistency Verification Based o...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xu, Yuwei Zeng, Junyu Cao, Jie Dai, Shengjiang Xiang, Qiao Cheng, Guang School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China Department of Electrical and Computer Engineering Queen’s University Kingston Canada School of Informatics Xiamen University Xiamen China

ISBN: (纸本)9789819615414

Consortium blockchains, with their decentralized characteristics, have found widespread applications across various industries. However, this also forms isolated data islands. These data islands have become obstacles to data sharing between industries, and the issue of cross-chain data consistency verification remains unresolved. Current mainstream cross-chain technologies solve asset transfer issues through complex transaction steps, thus failing to meet the need for efficient data transmission. Existing research has proposed solutions for cross-chain data consistency verification, but two major issues remain. Firstly, these schemes do not consider data changes or support data dynamic operations. Secondly, these schemes involve a significant amount of complex computations, leading to slow efficiency. To address the above issues, we propose DataJudge, a cross-chain data consistency verification scheme based on an extended Merkle hash tree. This scheme not only verifies cross-chain data consistency but also effectively fills the gap in existing schemes regarding dynamic operations. Additionally, we modify the paillier homomorphic encryption. To improve efficiency, we do not introduce many complex computations. Furthermore, the extended Merkle hash tree structure enhances efficiency. Finally, we implement a prototype system and evaluate it. The results demonstrate that DataJudge can effectively verify cross-chain data consistency and outperform existing schemes such as DCIV and BeDCV. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data consistency

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Big social data analysis for quality of MOOC courses: the moderating role of practical examples

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Quality and Quantity 2025年 1-33页

作者： Nilashi, Mehrbakhsh Abumalloh, Rabab Ali Zibarzani, Masoumeh Centre for Business Informatics and Industrial Management (CBIIM) UCSI University No. 1 Jalan Menara Gading UCSI Heights Cheras Kuala Lumpur 56000 Malaysia Department of Computer Science and Engineering Qatar University Doha 2713 Qatar Department of Management Faculty of Social Sciences and Economics Alzahra University Tehran Iran

Big social data analysis has played an important role in understanding learners’ preferences in Massive Open Online Courses (MOOCs). This study investigates learners’ satisfaction with MOOCs using big social data analysis and a large-scale survey. The learners’ online reviews are analyzed using a hybrid method based on machine learning techniques and Partial Least Squares Structural Equation Modeling. We adopted a text mining approach, Latent Dirichlet Allocation (LDA), which is widely used in topic modeling. We utilized a large dataset of 6004 online reviews obtained from the learners of Udemy’s online courses. The results from the analysis identified nine topics as aspects of the quality of online courses, namely Course Accessibility, Course Cost, Course Material Quality, Course Organization, Course Resource Availability, Course Usefulness, Instructor Explanation, Instructor Knowledge, and Practical Examples in the Course. The results of the analysis were used to construct a research model, which was evaluated using a large-scale survey (N = 464). The results of the PLS-SEM confirmed the significant impacts of the proposed research factors, except for course organization, on learners’ satisfaction. The results also revealed that the practical examples in the course moderate the relationships between course usefulness, instructor explanation, instructor knowledge, and learners’ satisfaction. The findings of this research contribute to both the theoretical and methodological aspects of discovering and analyzing the influential factors for learners' satisfaction with MOOCs, thereby offering valuable insights for enhancing the overall quality of education in digital learning environments. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Big social data Education quality Learners' satisfaction Learners’ online reviews Online courses Soft computing

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Respiratory Anomaly and Disease Detection using Multi-Level Temporal Convolutional Networks

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IEEE Journal of Biomedical and Health informatics 2025年 PP卷 PP页

作者： Le, Kim-Ngoc T. Byun, Gyurin Raza, Syed M. Le, Duc-Tai Choo, Hyunseung The Department of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of The James Watt School of Engineering University of Glasgow Glasgow United Kingdom The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of The Department of AI Systems Engineering Department of Electrical and Computer Engineering College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of

An automated analysis of respiratory sounds using Deep Learning (DL) plays a pivotal role in the early detection of lung diseases. However, current DL methods often examine the spatial and temporal characteristics of respiratory sounds in isolation, which inherently limit their potential. This study proposes a novel DL framework that captures spatial features through convolution operations and exploits the spatiotemporal correlations of these features using temporal convolution networks. The proposed framework incorporates MultiLevel Temporal Convolutional Networks (ML-TCN) to considerably enhance the model accuracy in detecting anomaly breathing cycles and respiratory recordings from lung sound audio. Moreover, a transfer learning technique is also employed to extract semantic features efficiently from limited and imbalanced data in this domain. Thorough experiments on the well-known ICBHI 2017 challenge dataset show that the proposed framework outperforms state-of-the-art methods in both binary and multi-class classification tasks for respiratory anomaly and disease detection. In particular, improvements of up to 2.29% and 2.27% in terms of the Score metric, average sensitivity and specificity, are demonstrated in binary and multi-class anomaly breathing cycle detection tasks, respectively. In respiratory recording classification tasks, the classification accuracy is improved by 2.69% for healthy-unhealthy binary classification and 1.47% for healthy, chronic, and non-chronic diagnosis These results highlight the marked advantage of the ML-TCN over existing techniques, showcasing its potential to drive future innovations in respiratory healthcare technology. © 2025 IEEE.

关键词： Lung cancer

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Simultaneous Communications and Sensing with Hybrid Reconfigurable Intelligent Surfaces

Simultaneous Communications and Sensing with Hybrid Reconfig...

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European Conference on Antennas and Propagation, EuCAP

作者： Ioannis Gavras George C. Alexandropoulos Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Electrical and Computer Engineering University of Illinois Chicago USA

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

Hybrid Reconfigurable Intelligent Surfaces (HRISs) constitute a new paradigm of truly smart metasurfaces with the additional features of signal reception and processing, which have been primarily considered for channel estimation and self-reconfiguration. In this paper, leveraging the simultaneous tunable reflection and signal absorption functionality of HRIS elements, we present a novel framework for the joint design of transmit beamforming and the HRIS parameters with the goal to maximize downlink communications, while simultaneously illuminating an area of interest for guaranteed localization coverage performance. Our simulation results verify the effectiveness of the proposed scheme and showcase the interplay of the various system parameters on the achievable Integrated Sensing and Communications (ISAC) performance.

关键词： Location awareness Simulation Human-robot interaction Europe Reconfigurable intelligent surfaces Integrated sensing and communication Metasurfaces Reflection Reliability Terahertz communications

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