Maximum utilisation of minimal amount of resources is pivotal for achieving a sustainable operation in large-scale Cloud Data Centres. Prediction driven resource provisioning in Cloud Data Centres is a potential approach to execute Cloud workloads in a sustianable way. Traditional prediction models often struggle to deliver accurate predictions under dynamic and heterogeneous cloud workloads, as capturing long-range dependencies and sudden workload spikes is often challenging in Cloud environments. In addition, recent time series models such as the Adversarial Error Correction Generative Adversarial Network (AEC-GAN) characterise shortcomings when applied to cloud workload datasets, particularly whilst managing volatility and learning irregular patterns. To address such challenges, this paper proposes a novel prediction model using Data Augment Aware Transformer Rectification-based Generative Adversarial Networks (TR-GAN), which incorporates a continuous and conditional learning Transformer block in the GAN's generator module to serve both as a data distribution moderator and as a data augmentation generator, ultimately to deliver accurate predictions. TR-GAN is the first GAN-based model tailored for long-term cloud workload forecasting that explicitly couples data augmentation with sequence rectification. Unlike discriminative forecasters, Its generative formulation allows it to model the intrinsic variability and uncertainty of cloud workloads,generate context-aware synthetic data to improve generalization under sparse or irregular patterns, and iteratively refine predictions through an adversarial learning process, thereby reducing error accumulation in long-horizon forecasts. The prediction performance of the proposed model is evaluated with two widely-used cloud workload datasets, namely the Google clusters and Alibaba traces. Experimental results demonstrate that the proposed TR-GAN model can deliver a prediction improvement of around 15% than notable state-of-the-art models, including Informer, Autoformer and AEC-GAN, for long forecasting horizons.

Identifying influential spreaders in complex networks is a crucial problem. This topic has garnered significant interest in network science research, and it is of considerable significance in traffic accident prediction, infectious disease prevention, and targeted advertising. The goal of it is to select a set of seed nodes that maximize the spread of influence. Traditional approaches, such as k-core decomposition, identify seed nodes based on their connectivity. However, these methods often select highly overlapping nodes, thereby limiting their overall influence. Alternative methods leverage Neighbourhood Coreness centrality to mitigate this issue by considering the$k$-shell indices of a node's neighbours, but they still fail to account for highly connected peripheral nodes or the influence of selected nodes on their neighbours. To address these limitations, we propose an Improved Neighborhood Coreness-based Heuristic Algorithm for Influence Maximization in Complex Networks (INC-HAIM). It iteratively selects uncovered nodes with the highest NC centrality and updates the coverage status and the centrality of neighbouring nodes and edge nodes, effectively reducing the impact of edge contributions. Experimental evaluations under the Independent Cascade (IC) model across multiple datasets demonstrate that as network size and propagation probability increase, INCHAIM consistently achieves superior influence spread compared to existing heuristic and centrality-based approaches.

In recent years, Low Earth Orbit (LEO) constellation based networks have attracted wide attention from both the academia and the industry. Broadband access and backhaul becomes a typical application for LEO satellite networks. However, the high-speed motion of satellites brings periodic fluctuation of delays, which is a great violation to the Quality of Service (QoS) provisioning. In this work, inspired by the success of Software Defined Networking (SDN), and considering the dynamics of LEO satellite networks, we propose a low-jitter multiple slots routing in LEO satellite networks to provide low-jitter end-to-end delay path computing and control. The proposed multiple slots routing optimization mechanism takes account of the topology shift as well as the dynamic propagation delay across multiple topology snapshots, and thus achieves low-jitter performance. The performance improvement of the proposed mechanism is validated by the simulation, which promises the value of jitter under 30 ms.

In the context of escalating advancements in AI generative technologies, Deepfakes, the sophisticated face forgeries created using deep learning methods, have emerged as a significant security threat. The predominant countermeasures are Deepfake detectors based on deep learning (DL). However, due to the opaque nature of DL-model, they struggle to offer understandable explanations for their predictive decisions, which undermines their reliability and effectiveness in real-world applications. Existing mainstream DL-oriented interpretation approaches, the feature attribution methods, struggle to work on Deepfake detectors due to issues of low interpretation fidelity, poor intelligibility, and limited applicability across different types of detectors. This paper addresses these critical challenges by proposing the Deepfake Detector Lens (DDL), a novel framework designed to enhance the interpretability of diverse architectural Deepfake detectors, encompassing those based on image, frequency domain, and video.DDLemploys a heuristic algorithm to enhance interpretation efficacy and incorporates image segmentation and face parsing techniques to bridge the gap between the machine-generated interpretation saliency map and human understanding. Comprehensive evaluations ofDDLdemonstrate its superiority over existing feature attribution methods in terms of fidelity, intelligibility, and applicability. The proposedDDLsignificantly advances the interpretability of Deepfake detection technology, offering a more reliable and understandable tool for combating AI-generated face forgeries.

Deepfakes refers to various deep-learning-based techniques that manipulate the face in videos. Maliciously manufactured face forgeries could result in serious problems such as portrait infringement, information confusion, or even public panic. Previous countermeasures focused mainly on promoting detection accuracy while relatively overlooking robustness and computational overhead. In this work, we propose an efficient and robust framework named GANK , which discriminates Deepfake videos through temporal modeling on decoupled geometric and appearance features. A temporal denoising technique featuring landmark tracking and Kalman filtering is introduced to optimize the feature sequences, and multi-stream Recurrent Neural Networks (RNN) are constructed for sufficient exploitation of dynamic features. Besides, we introduce two optimizations to alleviate overfitting and enhance the utilization of temporal information, including channel-wise dropout and temporal random cropping. Our framework achieves outstanding robustness using very lightweight network backbones, reaching state-of-the-art performance on multiple benchmarks.

The performance of deep learning models highly depends on the amount of training data. It is common practice for today's data holders to merge their datasets and train models collaboratively, which yet poses a threat to data privacy. Different from existing methods such as secure multi-party computation (MPC) and federated learning (FL), we find representation learning has unique advantages in collaborative learning due to its low privacy budget, wide applicability to tasks and lower communication overhead. However, data representations face the threat of model inversion attacks. In this article, we formally define the collaborative learning scenario, and present ARS (for adversarial representation sharing), a collaborative learning framework wherein users share representations of data to train models, and add imperceptible adversarial noise to data representations against reconstruction or attribute extraction attacks. By theoretical analysis and evaluating ARS in different contexts, we demonstrate that our mechanism is effective against model inversion attacks, and can achieve great utility and low communication complexity while preserving data privacy. Moreover, the ARS framework has wide applicability, which can be easily extended to the vertical data partitioning scenario and utilized in different tasks.

Deepfakes is a branch of malicious techniques that transplant a target face to the original one in videos, resulting in serious problems such as infringement of copyright, confusion of information, or even public panic. Previous efforts for Deepfakes videos detection mainly focused on appearance features, which have a risk of being bypassed by sophisticated manipulation, also resulting high model complexity and sensitiveness to noise. Besides, how to mine the temporal features of manipulated videos and exploit them is still an open question. We propose an efficient and robust framework named LRNet for detecting Deepfakes videos through temporal modeling on precise geometric features. A novel calibration module is devised to enhance the precision of geometric features, making it more discriminative, and a two-stream Recurrent Neural Network (RNN) is constructed for sufficient exploitation of temporal features. Compared to previous methods, our proposed method is lighter-weighted and easier to train. Moreover, our method has shown robustness in detecting highly compressed or noise corrupted videos. Our model achieved 0.999 AUC on FaceForensics+ + dataset. Meanwhile, it has a graceful decline in performance (-0.042 AUC) when faced with highly compressed videos.1