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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Xu, Jun Yu, Songnan Lu, Xiaoyu Sean Wu, Di Kang, Qi School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing210000 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ United States

ISBN: (纸本)9798350365221

This study aims to investigate cyberbullying incidents on Weibo by constructing a comprehensive dataset of labeled conversations. We collect 89K social media sessions from 10K user profiles and manually annotated the data to identify instances of cyberbullying. We analyze cyberbullying based on three fundamental characteristics, examining its distribution across different topics and the relationship between posting times and cyberbullying incidents. Additionally, we explore the influence of user popularity on the similarity of receiving bullying comments. Our study provides valuable insights into the characteristics of cyberbullying on Chinese social media and highlights the need for culturally sensitive detection models. © 2024 IEEE.

关键词： Phishing

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Enhanced Efficiency in Fog Computing: A Fuzzy Data-Driven Machine Selection Strategy

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International Journal of Fuzzy Systems 2024年第1期26卷 368-389页

作者： Zavieh, Hadi Javadpour, Amir Ja’fari, Forough Sangaiah, Arun Kumar Slowik, Adam School of Economics and Statistics Guangzhou University Guangzhou510006 China Harbin Institute of Technology Shenzhen China Department of Computer Engineering Sharif University of Technology Tehran Iran International Graduate School of AI National Yunlin University of Science and Technology Douliu Taiwan Department of Electronics and Computer Science Koszalin University of Technology Koszalin Poland ADiT-Lab Electrotechnics and Telecommunications Department Instituto Politécnico de Viana do Castelo Porto4900–347 Portugal Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

With the rapid proliferation of IoT and Cloud networks and the corresponding number of devices, handling incoming requests has become a significant challenge. Task scheduling problems have emerged as a common concern, necessitating the exploration of new methods for request management. This paper proposes a novel approach called the Fuzzy Inverse Markov Data Envelopment Analysis Process (FIMDEAP). Our method combines the strengths of the Fuzzy Inverse Data Envelopment Analysis (FIDEA) and Fuzzy Markov Decision Process (FMDP) techniques to enable the efficient selection of physical and virtual machines while operating in a fuzzy mode. We represent data as triangular fuzzy numbers and employ the alpha-cut method to solve the proposed models. The paper provides a mathematical optimization model for the proposed method and presents a numerical example for illustration. Furthermore, we evaluate the performance of our method in a cloud environment through simulations. The results demonstrate that our approach outperforms existing methods, namely PSO + ACO and FBPSO + FBACO, in terms of key metrics, including energy consumption, execution cost, response time, gain of cost, and makespan. © The Author(s) 2023.

关键词： Markov processes

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Dual-head alternating attention-based adaptive convolutional framework for improving low-resource and imbalanced text classification

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Neural Computing and Applications 2025年 1-21页

作者： Agbesi, Victor Kwaku Chen, Wenyu Hossin, Md Altab Ukwuoma, Chiagoziem C. School of Computer Science and Engineering University of Electronic Science and Technology of China No. 2006 Xiyuan Ave West Hi-Tech Zone Sichuan Chengdu China School of Innovation and Entrepreneurship Chengdu University No. 2025 Chengluo Avenue 610106 Sichuan Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan Chengdu610059 China

Low-resource imbalance texts pose significant challenges due to class imbalance, adversely affecting the performance of deep learning approaches. To address this issue, we propose a dual-head alternating attention-based adaptive convolutional framework with label embedding (AltXLM-AdaCLE), a novel framework designed for imbalanced text classification in low-resourced datasets. Our framework features a new alternating attention model, which combines a dual-head attention mechanism with a bidirectional long short-term memory network, facilitating dynamic filter generation that adapts to the input data’s characteristics and enhances the capture of relevant features and long-range dependencies. A key innovation in this study is the dual-head attention mechanism, which utilizes an alternating reading sequence to extract information from multiple document segments, thereby minimizing information loss and improving the understanding of complex relationships. Furthermore, we introduce a method for embedding class labels in a shared vector space with the text, allowing the model to leverage label information throughout the classification process, which is particularly beneficial for imbalanced datasets. Our framework also incorporates an adaptive convolutional layer that processes text features and label embeddings, generating more representative features of the underlying data distribution and improving classification performance. Experimental results on six low-resourced imbalance text datasets validate the effectiveness of AltXLM-AdaCLE with an average AUC gain of 26.42%, demonstrating its superiority over existing methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Vector spaces

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TE-waves in waveguide structures filled with graded dielectric media 25

TE-waves in waveguide structures filled with graded dielectr...

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25th International Conference on Electromagnetics in Advanced Applications, ICEAA 2024

作者： Dalarsson, Mariana School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE 100-44 Sweden

ISBN: (纸本)9798350360974

Graded dielectric media composites, with negative-index materials (MM), are of interest for various applications such as microwave graded-index (GRIN) lenses [ 1,2], wideband THz filtering [3], and enhanced energy harvesting [4]. Graded dielectric media with regular positive-index materials are also of importance in e.g. the field of biosensing. One example is mimicking of dielectric phantoms for the characterization of biological tissue's properties and response to electromagnetic radiation [5,6]. Our research is therefore of interest for applications within both general electromagnetics and biosensing due to its generality. In line with our previous work [7], in the present abstract we review TE-wave propagation in waveguide structures filled with dielectric composites graded along the direction of the waveguide (conveniently chosen to be z-direction). Using Maxwell's equations in a source-free (p = 0 and 7 = 0) inhomogeneous medium{Formula Method} for TE-waves (Ez = 0) in the z-direction along the waveguide structure filled with a non-magnetic (ii(r, a») = ii0) dielectric composite with graded permittivity along the z-direction (e(r, a») = e(z, a»)), the wave equation for the electric field reduces to a simple form V2E(r, a>) + fc2£r(z, a>)E(r, a>) = 0 where k2 = o»2£0lu0 and £r(z,a>) is the relative permittivity function. The longintudinal component of the magnetic field Hz(r,a>) satisfies the same differential equation, i.e. V2Hz(r,a>) + k2Er(z,a>)Hz(r,a>) = 0 . These wave equations have the form of the Klein-Gordon (or Schrôdinger) equation of quantum physics, and they offer readily available exact analytical solutions for a number of permittivity profiles of interest, such as the hyperbolic tangent profile or periodic (sinusoidal) profile and similar. As an example, for a circular waveguide, of radius r0 , filled with graded dielectric composite with the periodic permittivity function{Formula Method} where C {...} and S {...} are the even and odd Mathieu func

关键词： Maxwell equations

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Flow-Guided Deformable Alignment Network with Self-Supervision for Video Inpainting 48

Flow-Guided Deformable Alignment Network with Self-Supervisi...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wu, Zhiliang Zhang, Kang Sun, Changchang Xuan, Hanyu Yan, Yan Nanjing University of Science and Technology School of Computer Science and Engineering China Illinois Institute of Technology Department of Computer Science United States Anhui University School of Big Data and Statistics China

ISBN: (纸本)9781728163277

Video inpainting aims to utilize plausible contents to fill missing regions in the video. State-of-the-art video inpainting methods typically generate the missing contents of the target frame (current frame) by aggregating the temporal information of reference frames (neighboring frames) aligned using deformable convolution. However, these deformable convolution alignment networks often suffer from offset overflow during training, resulting in unsatisfactory alignment, thereby obtaining compromised inpainting performance. In this paper, we propose a self-supervised Flow-Guided Deformable Alignment (FGDA) network for aligning reference frames at the feature level. FGDA computes the residual of the optical flow as the offsets. This design can effectively reduce the burden of offsets learning, thereby avoiding offset overflow. Furthermore, a gradient-weighted reconstruction loss for supervised completed frame reconstruction is designed, which can use the gradients in all directions of the video frame to emphasize the difficultly reconstructed texture regions, so that detail textures get more attention during training. Experiments show that FGDA-based video inpainting model trained with gradient-weighted reconstruction loss outperforms the state-of-the-art by a significant margin in terms of PSNR and SSIM with relative improvements of 6.2% and 2.1%, respectively. © 2023 IEEE.

关键词： Alignment

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Wide and fault diameter in Kneser graphs for enhanced fault tolerance in parallel computing

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International Journal of Networking and Virtual Organisations 2024年第3期31卷 169-190页

作者： Sundara Rajan, R. Kirithiga Nandini, G. Lin, Yuqing Reji, Remi Mariam Department of Mathematics Hindustan Institute of Technology and Science Chennai603 103 India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai603 103 India School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia Department of Mathematics Christian College Kerala Chengannur689 122 India

A system’s fault tolerance is its capacity to function even if one or more of its components fail. Implementing a fault-tolerant network becomes an important criterion for reliable computing. Reliability measures play a significant part in recognising the role of faulty and non-faulty processors in a parallel computing system. Parallel computing is used primarily for saving time, solving big problems, and doing multiple tasks at once at the same time. Various reliability measures have been introduced to evaluate a network’s fault-tolerance capability. We have measured the wide diameter and fault diameter of the Kneser graphs in this study. Also, we have verified the fault diameter obtained using an experimental study. Further, we have described some applications of wide diameter and fault diameter in parallel and distributed computing. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Fault tolerant computer systems

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A Load-Balancing Strategy Based on Multi-Task Learning in a Distributed Training Environment 4

A Load-Balancing Strategy Based on Multi-Task Learning in a ...

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4th International Conference on Advances in Electrical engineering and computer Applications, AEECA 2023

作者： Huang, Yinqi Zhou, Wenjiang Li, Liang Lyu, Ting Xu, Haitao Univerisity of Science and Technology Beijing School of Computer and Communication Engineering Beijing China China Academy of Information and Communications Technology Beijing China Shunde Innovation School Univerisity of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798350308082

With the development of machine learning and big data technologies, distributed training has become an important way to improve computational efficiency. However, in the distributed training environment, the performance difference between workers and the interference of unrelated tasks may lead to the uneven load of the system and cause the 'straggler phenomenon'. Therefore, how to solve the straggler phenomenon and achieve load balancing of the system is a pressing problem of distributed training. To address the shortcomings of the existing parallel computing model DSSP, this paper proposes a load-balancing strategy for DDP, which effectively reduces the load difference by adjusting the batch size and data size of workers in the training process. Then, to explore the correlation between data size in DDP and synchronization threshold in DSSP in dynamic adjustment, we perform multi-task learning for the two dynamic adjustment strategies and integrate the proposed Joint Multi-Task Prediction scheme on DSSP to implement a new parallel computing model ESP. extensive experiments The results show that ESP can not only guarantee the model accuracy but also effectively improve the training speed in distributed training. © 2023 IEEE.

关键词： Computational efficiency

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Textual Data De-Privatization Scheme Based on Generative Adversarial Networks 24th

Textual Data De-Privatization Scheme Based on Generative Adv...

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

作者： Du, Yanning Xu, Jinnan Zhang, Yaling Wang, Yichuan Wang, Zhoukai School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (纸本)9789819615506

In many fields, such as healthcare, finance, and scientific research, data sharing and collaboration are critical to achieving better outcomes. However, the sharing of personal data often involves privacy risks, so privacy-preserving techniques are needed to ensure data security and privacy. The superior performance and flexibility of generative models in data representation have led to significant progress in the development of data privacy. This paper proposes a textual data de-privatization scheme based on generative adversarial networks that well combines generative adversarial networks with privacy protection. The generated de-privacy data retains the statistical properties of the original data and effectively removes sensitive information. Moreover, feature sorting and reshaping modules are introduced to enable the generator to better capture the relationships between features, thus improving the quality of synthetic data. In this paper, the utility of synthetic data is evaluated from three aspects, including similarity assessment, privacy assessment, and model utility assessment. Experimental results show that this method achieves a good trade-off in terms of textual data privacy protection and data quality maintenance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Self-improved COOT Algorithm for Resource Allocation in Cloud Data Centers 4th

Self-improved COOT Algorithm for Resource Allocation in Clou...

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4th International Conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Singh, Shubham Singh, Pawan Tanwar, Sudeep Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India

ISBN: (纸本)9789819914784

Cloud computing has been developed as a front-line platform for consumer and commercial potential. Additionally, it enables users to access data and apps from any location. Businesses will use cloud services to borrow storage as well as other operations, which significantly lowers the cost of technology. In this aspect, resource allocation is more important to assure the efficiency of the cloud service. The primary goal of this study is to suggest a new resource allocation model that uses optimization to help maximize sales while minimizing cost. An improved k-means clustering with an optimal tuned centroid is used to cluster all workload (the jobs that have been allotted). Here, tasks are clustered as per the consideration of QoS (trust) and task execution time. Subsequent to this, the resource allocation is done optimally under the consideration of power utilization effectiveness (PUE), CPU usage, and execution time, respectively. For solving the optimization problem specified, this work proposes a new self-upgraded coot optimization (SUCO) algorithm. Finally, the proposed SUCO algorithm was evaluated over the other models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： K-means clustering

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Omni Contextual Aggregation Networks for High-Fidelity Image Inpainting

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 6129-6144页

作者： Peng, Jinjia Li, Mengkai Wang, Bingyan Wang, Huibing Hebei University School of Cyber Security and Computer Hebei China Hebei Machine Vision Engineering Research Center China Dalian Maritime University College of Information Science and Technology Liaoning China

Image inpainting aims to restore a realistic image from a damaged or incomplete version. Although Transformer-based methods have achieved impressive results by modeling long-range dependencies, the inherent quadratic complexity of canonical self-attention has typically led to these approaches adopting uni-dimensional modeling, which limits the model's ability to capture complex relationships from both spatial and channel dimensions. To this end, this paper exploits a novel attention paradigm termed Dynamic Omni-Attention Mechanism (DOAM) for simultaneously modeling pixel-interaction from both spatial and channel dimensions, and implements the information interaction across the omni-axis (i.e., spatial and channel) with linear computational complexity. In addition, to handle large-scale degradation, this paper proposes a Multi-band Feature Enhancement (MFE) module to enhance feature representation in downsampling, thus unlocking the potential of subsequent attentional interactions. Moreover, motivated by recent advances in image restoration, this paper incorporates a domain-related prior representation from CNN-based Network to modulate the features during proposed attention mechanism and feed-forward networks. Integrating the above designs into an encoder-decoder architecture, the proposed Omni Contextual Aggregation Networks (OCANet) achieve superior performance at lower parameters and time costs than the competitive baselines. Extensive experiments on CelebA-HQ, Paris Street View, FFHQ and Dunhuang datasets validate the efficacy of the proposed method. © 2025 IEEE.

关键词： Image reconstruction

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