Existing Kubernetes autoscaling solutions predominantly rely on single metrics with single-direction scaling (either horizontal or vertical), making it challenging to balance end-to-end latency and CPU utilization effectively. This paper introduces HyMetricScaler, a hybrid autoscaling framework. The framework employs a horizontal scaling component that makes decisions based on distributed tracing data and incorporates an exponential moving average mechanism to mitigate scaling oscillations, thereby optimizing end-to-end latency. Additionally, it utilizes a vertical scaling component based on PID control theory to adjust CPU quotas, improving CPU utilization. Experimental results demonstrate that HyMetricScaler significantly reduces oscillations in scaling decisions under high-load scenarios. In low-load scenarios, it explores the performance trade-off space by adjusting CPU target utilization. Through configuration adjustments of its components, the framework can achieve optimal end-to-end latency or CPU utilization, meeting various business requirements for different performance metrics. Comparative experiments against baseline algorithms validate the effectiveness of the proposed solution.

Understanding the interplay between diseases and genes is crucial for gaining deeper insights into disease mechanisms and optimizing therapeutic strategies. In recent years, various computational methods have been developed to uncover potential disease-gene associations. However, existing computational approaches for disease-gene association prediction still face two major limitations. First, most current studies focus on constructing complex heterogeneous graphs using multi-dimensional biological entity relationships, while overlooking critical latent interaction patterns, namely, disease neighbor interactions and gene neighbor interactions-which are more valuable for association prediction. Second, in self-supervised learning (SSL), the presence of noise in auxiliary tasks commonly affects the accurate modeling of diseases and genes. In this study, we propose a novel denoising method for disease-gene association prediction, termed DGSL. To address the first issue, we utilize bipartite graphs corresponding to diseases and genes to derive disease-disease and gene-gene similarities, and further construct disease and gene interaction graphs to capture the latent interaction patterns. To tackle the second challenge, we implement cross-view denoising through adaptive semantic alignment in the embedding space, while preserving useful neighbor interactions. Extensive experiments on benchmark datasets demonstrate the effectiveness of our method.

Abstract In cyberspace, intrusion and detection constitute dynamic and continuous game processes, where data streams are generated incrementally during intrusion. To mitigate intrusions and safeguard assets effectively, it is imperative to take prompt actions based on real-time detection and analysis of the currently available data streams. However, existing approaches that rely on complete and clean data struggle to keep pace with the continuous real-time flow of new network data. To address this issue, we introduce IL-IDS (Incremental Learning for Intrusion Detection Systems), a novel intrusion detection approach that utilizes incremental learning to enable accurate and timely detection of intrusions in real-world scenarios, where the need for real-time processing and learning from newly generated traffic data is paramount. IL-IDS performs in scenarios with limited data availability, where it initially transforms textual data streams into vectorized representations and leverages a variation autoencoder (VAE) to compress these vectors, efficiently extracting their latent features. Then a classifier is trained to distinguish attack and normal behaviors, and a three-way decision method is employed to establish a boundary for ambiguous data that pose challenges in direct classification. Concurrently, threat intelligence is integrated into this process to enhance the accuracy of decision-making. We validate the effectiveness and efficiency of IL-IDS with experiments on real-world deployments during an international activity, highlighting its robustness and reliability in intrusion detection applications, especially under conditions of confined data streams. Notably, IL-IDS has exhibited comparable accuracy and recall results, and attains exceptional 99.93% precision and 96.83% F1-score, which demonstrates a notable improvement of 5.27% and 2.59% respectively in comparison to intrusion detection models trained on complete and readily available data.

Nested named entity recognition (Nested NER) in knowledge graph (KG) aims at obtaining all meaningful entities, including nested entities for sentences in longer text region. Those obtained entities are to facilitate downstream applications, such as relation extraction, entity resolution, and coreference resolution. This task, however, is challenging not only because of the demand to detect the boundary of the entity but also due to the complexity of those hierarchically nested entities. Since a substantial amount of work has been made to Flat NER (or Nested NER), a few of them can explicitly acquire the position of the entity and utilize the grammatical construction of text. In this work, we propose PANNER, a POS-aware Nested NER model, to solve all the above issues. Specifically, we first construct a heterogeneous graph by introducing the part-of-speech (POS) information of the word. Second, we design a dilated random walk (DRW) algorithm based on a grammatical path to sample a fixed size of neighbors for each node. Third, we aggregate the message from different types of neighbors through an attention mechanism. Finally, we use a bidirectional decoding module to recognize and categorize all the flat and nested entities based on the node embedding in a layer-wise manner. Our extensive experiments show the effectiveness of PANNER in both flat and nested NER.

Despite the impressive results achieved by many existing Structure from Motion (SfM) approaches, there is still a need to improve the robustness, accuracy, and efficiency on large-scale scenes with many outlier matches and sparse view graphs. In this paper, we propose AdaSfM: a coarse-to-fine adaptive SfM approach that is scalable to large-scale and challenging datasets. Our approach first does a coarse global SfM which improves the reliability of the view graph by leveraging measurements from low-cost sensors such as Inertial Measurement Units (IMUs) and wheel encoders. Subsequently, the view graph is divided into sub-scenes that are refined in parallel by a fine local incremental SfM regularised by the result from the coarse global SfM to improve the camera registration accuracy and alleviate scene drifts. Finally, our approach uses a threshold-adaptive strategy to align all local reconstructions to the coordinate frame of global SfM. Extensive experiments on large-scale benchmark datasets show that our approach achieves state-of-the-art accuracy and efficiency. [Project Page]

ABSTRACT This paper addresses the issues of rate control and routing for cloud data center networks. Based on the theory of the supply–demand equilibrium, we propose a fixed point model for formulating cloud network equilibrium problems in which the equilibrium conditions are given by nonlinear equations. We show that the network equilibrium point is the optimal solution of a nonlinear programming problem by utilizing the tools of the variational inequality and convex optimization. The augmented Lagrangian multiplier algorithm is used to solve the nonlinear programming problem for computing the network equilibrium point. Further consideration is given to the equilibrium problems of a cloud network with multirate multicast sessions. We evaluate our approach on some random networks with unicast and multicast sessions, and the results show the effectiveness of our approach in finding the optimal equilibrium rates. We further evaluate the performance of our approach through cloud data center network simulation under various parameter settings, and the results show that the performance of our algorithm can be tuned and improved by choosing appropriate parameter values. Copyright © 2013 John Wiley & Sons, Ltd.