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arXiv 2025年

作者： Zhang, Qiquan Wickramasinghe, Buddhi Ambikairajah, Eliathamby Sethu, Vidhyasaharan Li, Haizhou School of Electrical Engineering and Telecommunications The University of New South Wales Sydney2052 Australia School of Electrical and Computer Engineering Purdue University West LafayetteIN United States Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen51872 China

Hand-crafted features, such as Mel-filterbanks, have traditionally been the choice for many audio processing applications. Recently, there has been a growing interest in learnable front-ends that extract representations directly from the raw audio waveform. However, both hand-crafted filterbanks and current learnable front-ends lead to fixed computation graphs at inference time, failing to dynamically adapt to varying acoustic environments, a key feature of human auditory systems. To this end, we explore the question of whether audio front-ends should be adaptive by comparing the Ada-FE front-end (a recently developed adaptive front-end that employs a neural adaptive feedback controller to dynamically adjust the Q-factors of its spectral decomposition filters) to established learnable front-ends. Specifically, we systematically investigate learnable front-ends and Ada-FE across two commonly used back-end backbones and a wide range of audio benchmarks including speech, sound event, and music. The comprehensive results show that our Ada-FE outperforms advanced learnable front-ends, and more importantly, it exhibits impressive stability or robustness on test samples over various training epochs. Copyright © 2025, The Authors. All rights reserved.

关键词： Gabor filters

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An Adaptive Service Function Chains Mapping With Multi-Task Deep Reinforcement Learning

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IEEE Transactions on Network Science and engineering 2025年

作者： Wei, Wenting Wang, Qingyi Gu, Huaxi Zheng, Danyang Zhang, Ning Wu, Celimuge Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Southwest Jiaotong University School of Computing and Artificial Intelligence China University of Windsor Department of Electrical and Computer Engineering Canada University of Electro-Communications Meta-Networking Research Center Japan

Network function virtualization (NFV) facilitates different virtual network functions (VNF) to be dynamically chained in sequence to offer new services in a flexible, scalable, and cost-effective manner. Recent years have witnessed the increasing diverse service demands from the ever-increasing new applications, which has posed significant challenges to the efficient and sequential execution of VNFs to achieve specific objectives, especially under conditions of shared resources. To address these challenges, substantial efforts have been dedicated to enhancing resource utilization and minimizing the costs associated with service function chains (SFCs), while maintaining high quality of service. However, an overemphasis on cost reduction can sometimes result in network congestion, which ultimately degrades both network performance and service quality. Given the time-varying and unpredictable characteristics of SFCs, it is essential to leverage their temporal features, along with those of network states, for adaptive SFC mapping. In this paper, we introduce an adaptive online SFC mapping algorithm to reduce operational costs and alleviate network congestion. This is achieved through the adaptive allocation of VNFs and the control of traffic routing between them. Our approach incorporates multi-task deep reinforcement learning to manage the coexistence of multiple SFC requests with varying resource requirements. Specifically, we integrate a long short-term memory (LSTM) layer into our model to capture the temporal dynamics of network states and resource demands, thereby enabling more effective long-term planning. To address the issue of reward sparsity, we implement a hierarchical reward mechanism and reward shaping techniques. Experimental results demonstrate that our algorithm achieves near-optimal performance in optimizing service delay, bandwidth consumption, and network congestion, while also ensuring a high acceptance rate for user requests. © 2013 IEEE.

关键词： Network function virtualization

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Analysis and Treatment of Missing Data in Happiness Score Using Machine Learning

Analysis and Treatment of Missing Data in Happiness Score Us...

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International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET

作者： Wassim Touati Yamen El Touati Nejib Ben Hadj-Alouane Abderrazek Jemai SERCOM Laboratory Polytechnic School of Tunisia Carthage University Tunis Tunisia Department of Computer Science Faculty of Computing and Information Technology Northern Border University Kingdom of Saudi Arabia OASIS Laboratory ENIT University of Tunis EI Manar Tunisia Electrical Engineering and Computer Engineering Department American University in Dubai Dubai UAE

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Machine learning, a vital part of artificial intelli-gence, improves our ability to make predictions from complex data. The success of these predictions relies heavily on the model's fit with its data and the data's consistency. Accurate predictions depend on closely matching the model to the data patterns. However, missing data can seriously disrupt the accuracy of predictions, causing errors. To address this, we recommend adopting a proposed framework that enhances predictions by effectively filling in missing data. Our objective is to refine the accuracy of our predictions, especially in scenarios where the data might be incomplete and features exhibit patterns of dependency. In the context of missing data, our approach concentrates on enhancing regression performance to minimize the effects of missing data as effectively as possible.

关键词： Accuracy Correlation Statistical analysis Smart cities Predictive models Data models Imputation Reliability Random forests Robots

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Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies

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IEEE/CAA Journal of Automatica Sinica 2021年第4期8卷 718-752页

作者： Othmane Friha Mohamed Amine Ferrag Lei Shu Leandros Maglaras Xiaochan Wang Networks and Systems Laboratory University of Badji Mokhtar-AnnabaAnnaba 23000Algeria Department of Computer Science Guelma UniversityGulema 24000Algeria College of Engineering Nanjing Agricultural UniversityNanjing 210095China School of Engineering University of LincolnLincoln LN67TSUK School of Computer Science and Informatics De Montfort UniversityLeicester LE19BHUK Department of Electrical Engineering Nanjing Agricultural UniversityNanjing 210095China

This paper presents a comprehensive review of emerging technologies for the internet of things(IoT)-based smart *** begin by summarizing the existing surveys and describing emergent technologies for the agricultural IoT,such as unmanned aerial vehicles,wireless technologies,open-source IoT platforms,software defined networking(SDN),network function virtualization(NFV)technologies,cloud/fog computing,and middleware *** also provide a classification of IoT applications for smart agriculture into seven categories:including smart monitoring,smart water management,agrochemicals applications,disease management,smart harvesting,supply chain management,and smart agricultural ***,we provide a taxonomy and a side-by-side comparison of the state-ofthe-art methods toward supply chain management based on the blockchain technology for agricultural ***,we present real projects that use most of the aforementioned technologies,which demonstrate their great performance in the field of smart ***,we highlight open research challenges and discuss possible future research directions for agricultural IoTs.

关键词： Agricultural internet of things(IoT) internet of things(IoT) smart agriculture smart farming sustainable agriculture

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Family of Mutually Uncorrelated Codes for DNA Storage Address Design

IEEE Transactions on Nanobioscience

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IEEE Transactions on Nanobioscience 2025年 PP卷 PP页

作者： Liu, Zhenlu Cao, Ben Shao, Qi Zheng, Yanfen Wang, Bin Zhou, Shihua Zheng, Pan Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China University of Canterbury Upper Riccarton Department of Accounting and Information Systems Christchurch8140 New Zealand

Deoxyribonucleic acid (DNA) has become an ideal medium for long-term storage and retrieval due to its extremely high storage density and long-term stability. But access efficiency is an existing bottleneck in DNA storage, especially the lack of high-quality random access address sequences. Therefore, in this paper, we report a series of approaches based on k-weakly mutually uncorrelated (k-WMU) codes to design the address sequence to improve the access efficiency of DNA storage. To address the problem of DNA sequences that are poorly scalable at the base level, we propose a 0-m-ruling coding scheme combined with k-WMU codes that can make address sequences avoid generating secondary structure with stem lengths ranging from 3 to 9. Based on the decoupled structure, We further extend the k-WMU codes with error correction function while satisfying combinatorial biological constraints. In order to investigate the performance of the designed address sequences for real-world applications, we perform simulation experiments based on thermodynamic properties and error correction capability as well as compared the minimum free energy (MFE), melting temperature (TM), and average decoding success rate (ADSR) with previous work. The results show that designed address sequences have a high MFE value and ADSR and a substantial reduction in TM-variance while satisfying the combinatorial biological constraints. As the quality of address sequences improves, this will help to achieve accurate random access as well as enhance the robustness of the DNA storage system. © 2002-2011 IEEE.

关键词： DNA sequences

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arXiv

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arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

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Spiking neural networks: Towards bio-inspired multimodal perception in robotics

arXiv

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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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Adaptive Dimming of Highway Lights Using Recurrent Neural Networks 6th

Adaptive Dimming of Highway Lights Using Recurrent Neural N...

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6th EAI International Conference on Intelligent Transport systems, INTSYS 2022

作者： Kolaitis, Angelos Alexandridis, Georgios Adam, Panagiota Alexandridis, Petros Chasanis, Grigoris Breton, Fabrice Artificial Intelligence and Learning Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Zografou157 80 Greece Extrabit L.P. 97 Nikolaou Plastira str Egaleo122 42 Greece Olympia Odos S.A. 4 Rizareiou str Chalandri152 33 Greece

ISBN: (纸本)9783031308543

Highway operators are in a constant search of techniques and methodologies that can reduce their energy footprint. In this respect, the installation of dimmable light-emitting diode lights on the open road section of highways appears to be a promising solution, due to the reduced energy consumption (compared to high pressure sodium lamps) and the ability to adjust their brightness at various levels, based on the road’s traffic load. However, setting the desired level of light intensity cannot be performed instantly, due to safety and contractual reasons that a highway operator must follow. For this reason, an adaptive and intelligent system is proposed in this work, that models traffic load and is able to predict its future trend, based on current load and light intensity measurements. In this way, the unnecessary use of the lighting equipment is avoided, as brightness is dropped to a minimum level when traffic load is predicted to be low. The proposed model is based on recurrent neural networks and more specifically on long short-term memory cells that are able to model complex dependencies in data with temporal correlations, like traffic load measurements. The overall approach is evaluated on relevant data provided by Olympia Odos S.A. that operates the Elefsina-Korinthos-Patra highway in Greece, with promising results. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Luminance

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Dual-beam photothermal material trace detection method based on self-mixing interference

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Applied Optics 2025年第16期64卷 4549-4558页

作者： Yan Zhao Yunlong Zhi Hui Zhao Daming Dong Jigang Tong Chunling Tu School of Electrical Engineering and Automation Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin 300384 China National Engineering Research Center for Intelligent Agricultural Equipment Beijing Academy of Agriculture and Forestry Sciences Beijing 100097 China Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria 0188 South Africa

In this paper, a dual-beam photothermal self-mixing substance trace detection system is proposed. The crystal violet (CV) solution of the sample undergoes a thermal lens effect under the action of pump photoperiod excitation, and the refractive index changes cause the deviation of the probe beam, and the self-mixing interference (SMI) effect occurs when the modulated light returns to the cavity. By combining theoretical and experimental results, the power fluctuation of the SMI signal caused by the thermo-optic effect is linearly related to CV concentration and other parameters. At an excitation power of 24 mW, the detection limit can reach $8.0 \times {10^{- 8}}\;{\rm mol/L}$ . This method does not require labeling or complex CV pretreatment and has high sensitivity and flexibility, providing a guide for CV characterization in biological, environmental, and pharmaceutical research.

关键词： Gaussian beams Laser beams Optical fields Phase shift Refractive index Spatial resolution

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Backdoor-Empowered Regulable Privilege Authorization for Edge-Level Graph Learning in 6G Vehicular Networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Yang, Xiao Li, Gaolei Wu, Jun Zhou, Kai Li, Jianhua Yang, Wu Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China The Hong Kong Polytechnic University Department of Computing Hong Kong Harbin Engineering University College of Computer Science and Technology Harbin China

Edge-Level Graph Learning System (EGLS) exhibits diverse applicability in flow prediction, route planning, and accident forecasting. Existing EGLS studies extremely stress absolute fairness and impartiality for all users, resultantly depriving its flexibility and humanity in addressing special circumstances and rendering the privilege authorization for exceptional responses challenging to achieve. To address this limitation, we present BackPriv, the first privilege authorization methodology enabling regulable privilege assignment or revocation in EGLS. Specifically, it is achieved through a creative edge backdoor mechanism, wherein the edge training samples are poisoned via user-specific privilege tokens to induce privilege outputs from the well-trained EGLS for authorized users. Moreover, related EGLS regulating strategies were proposed to dynamically realize the addition and revocation of user privileges by system retraining to guarantee privilege control in EGLS. Additionally, a Graph Mutual Information-based adaptive privilege token generation method is introduced to reduce the impact of privilege authorization on normal inputs and augment authorization functionality. Based on experiments with multiple real-world datasets, BackPriv demonstrates high success rates of privilege authorization under various scenarios ( 100%) and exerts minimal impact on normal task performance for regular inputs (mean accuracy drop 0.32%). © 1975-2011 IEEE.

关键词： Adversarial machine learning

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