795 Result(s)
within
Article
**hu Li
Information Systems Applications (incl. Internet)
Software Engineering
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Packet header-based reweight-long short term memory (Rew-LSTM) method for encrypted network traffic classification
With the development of Internet technology, cyberspace security has become a research hotspot. Network traffic classification is closely related to cyberspace security. In this paper, the problem of classific...
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Multi-example query over ontology-label knowledge graphs
Multi-example query over knowledge graphs allows users to provide multiple example fragments to express their intention, overcoming the shortage of single example query asks users to give a specific complete q...
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Incorporating self-attentions into robust spatial-temporal graph representation learning against dynamic graph perturbations
This paper proposes a Robust Spatial-Temporal Graph Neural Network (RSTGNN), which overcomes the limitations faced by graph-based models against dynamic graph perturbations using robust spatial-temporal self-a...
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Differentially private federated learning with non-IID data
In Differentially Private Federated Learning (DPFL), gradient clip** and random noise addition disproportionately affect statistically heterogeneous data. As a consequence, DPFL has a disparate impact: the a...
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SG-NeRF: Sparse-Input Generalized Neural Radiance Fields for Novel View Synthesis
Traditional neural radiance fields for rendering novel views require intensive input images and pre-scene optimization, which limits their practical applications. We propose a generalization method to infer sc...
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Community anomaly detection in attribute networks based on refining context
With the widespread use of attribute networks, anomalous node detection on attribute networks has received increasing attention. By utilizing communities as reference contexts for local anomaly node detection,...
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LGAT: a light graph attention network focusing on message passing for semi-supervised node classification
Deep learning has shown superior performance in various applications. The emergence of graph convolution neural networks (GCNs) enables deep learning to learn the latent representation from graph-structured da...
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Influence maximization in mobile social networks based on RWP-CELF
Influence maximization (IM) problem for messages propagation is an important topic in mobile social networks. The success of the spreading process depends on the mechanism for selection of the influential user...
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Optimization for energy-aware design of task scheduling in heterogeneous distributed systems: a meta-heuristic based approach
The motivation of task scheduling in heterogeneous computing systems is the optimal management of heterogeneous distributed resources as well as the exploitation of system capabilities. Energy consumption is o...
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An association rule mining-oriented approach for prioritizing functional requirements
Software requirements play a vital role in ensuring a software product’s success. However, it remains a challenging task to implement all of the user requirements, especially in a resource-constrained developm...
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Enhancing sine cosine algorithm based on social learning and elite opposition-based learning
In recent years, Sine Cosine Algorithm (SCA) is a kind of meta-heuristic optimization algorithm with simple structure, simple parameters and trigonometric function principle. It has been proved that it has goo...
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Person re-identification method based on fine-grained feature fusion and self-attention mechanism
Aiming at the problem of low accuracy of person re-identification (Re-ID) algorithm caused by occlusion, low distinctiveness of person features and unclear detail features in complex environment, we propose a ...
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Adaptive vertical federated learning via feature map transferring in mobile edge computing
To bring more intelligence to edge systems, Federated Learning (FL) is proposed to provide a privacy-preserving mechanism to train a globally shared model by utilizing a massive amount of user-generated data o...
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AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks
Exploring the expected quantizing scheme with suitable mixed-precision policy is the key to compress deep neural networks (DNNs) in high efficiency and accuracy. This exploration implies heavy workloads for do...
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Understanding and Detecting Inefficient Image Displaying Issues in Android Apps
Mobile applications (apps for short) often need to display images. However, inefficient image displaying (IID) issues are pervasive in mobile apps, and can severely impact app performance and user experience. ...
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Mconvkgc: a novel multi-channel convolutional model for knowledge graph completion
The incompleteness of the knowledge graph limits its applications to various downstream tasks. To this end, numerous influential knowledge graph embedding models have been presented and have made great achieve...
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SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems
Novel artificial intelligence (AI) technology has expedited various scientific research, e.g., cosmology, physics, and bioinformatics, inevitably becoming a significant category of workload on high-performance...
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Enhancing Storage Efficiency and Performance: A Survey of Data Partitioning Techniques
Data partitioning techniques are pivotal for optimal data placement across storage devices, thereby enhancing resource utilization and overall system throughput. However, the design of effective partition sche...
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WavEnhancer: Unifying Wavelet and Transformer for Image Enhancement
Image enhancement is a widely used technique in digital image processing that aims to improve image aesthetics and visual quality. However, traditional methods of enhancement based on pixel-level or global-lev...
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UAV-assisted wireless charging and data processing of power IoT devices
To ensure the reliability and operational efficiency of the grid system, this paper proposes an unmanned aerial vehicle (UAV)-assisted Power Internet of Things (PIoT), which obtains real-time grid data through...
