2,954 Result(s)
within
Computer Science
Algorithm Analysis and Problem Complexity
Computer Communication Networks
**u-**u Zhang
Chapter
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Chapter and Conference Paper
Multi-sourced Integrated Ranking with Exposure Fairness
Integrated ranking system is one of the critical components of industrial recommendation platforms. An integrated ranking system is expected to generate a mix of heterogeneous items from multiple upstream sour...
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Chapter and Conference Paper
Mask Adaptive Spatial-Temporal Recurrent Neural Network for Traffic Forecasting
How to model the spatial-temporal graph is a crucial problem for the accuracy of traffic forecasting. Existing GNN-based work mostly captures spatial dependencies by using a pre-defined graph for close nodes a...
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Chapter and Conference Paper
MSTAN: A Multi-view Spatio-Temporal Aggregation Network Learning Irregular Interval User Activities for Fraud Detection
Discovering fraud patterns from numerous user activities is crucial for fraud detection. However, three factors make this task quite challenging: Firstly, previous research usually utilize just one of the two ...
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Chapter and Conference Paper
FMSYS: Fine-Grained Passenger Flow Monitoring in a Large-Scale Metro System Based on AFC Smart Card Data
In this paper, we investigate the real-time fine-grained passenger flows in a complex metro system. Our primary focus is on addressing crucial questions, such as determining the number of passengers on a movin...
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Chapter and Conference Paper
TFAugment: A Key Frequency-Driven Data Augmentation Method for Human Activity Recognition
Data augmentation enhances Human Activity Recognition (HAR) models by diversifying training data through transformations, improving their robustness. However, traditional techniques with random masking pose ch...
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Chapter and Conference Paper
MvRNA: A New Multi-view Deep Neural Network for Predicting Parkinson’s Disease
Magnetic Resonance Imaging (MRI) is a critical medical diagnostic tool that assists experts in precisely identifying lesions. However, due to its high-dimensional nature, it requires substantial storage resour...
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Chapter and Conference Paper
MixCL: Mixed Contrastive Learning for Relation Extraction
Entity representation plays a fundamental role in modern relation extraction models. Previous efforts usually explicitly distinguish entities from contextual words, e.g., by introducing position embedding w.r....
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Chapter and Conference Paper
A Novel SegNet Model for Crack Image Semantic Segmentation in Bridge Inspection
Cracks on bridge surfaces represent a significant defect that demands accurate and efficient inspection methods. However, current approaches for segmenting cracks suffer from low accuracy and slow detection sp...
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Chapter and Conference Paper
Optimizing the Parallelism of Communication and Computation in Distributed Training Platform
With the development of deep learning, DNN models have become more complex. Large-scale model parameters enhance the level of AI by improving the accuracy of DNN models. However, they also present more severe ...
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Chapter and Conference Paper
Online Matching with Stochastic Rewards: Advanced Analyses Using Configuration Linear Programs
Mehta and Panigrahi (2012) proposed Online Matching with Stochastic Rewards, which generalizes the Online Bipartite Matching problem of Karp, Vazirani, and Vazirani (1990) by associating the edges with success...
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Chapter and Conference Paper
Multi-stage Optimization of Incentive Mechanisms for Mobile Crowd Sensing Based on Top-Trading Cycles
For collaborative tasks requiring multiple users, in Mobile Crowd Sensing (MCS), low user interest in certain tasks usually results in insufficient user re-cruitment. However, the interest of the user directly...
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Chapter and Conference Paper
Local Subsequence-Based Distribution for Time Series Clustering
Analyzing the properties of subsequences within time series can reveal hidden patterns and improve the quality of time series clustering. However, most existing methods for subsequence analysis require point-t...
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Chapter and Conference Paper
ImMC-CSFL: Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning
Clustering as one of the main research methods in data mining, with the generation of multi-view data, multi-view clustering has become the research hotspot at present. Many excellent multi-view clustering alg...
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Chapter and Conference Paper
Spatial Gene Expression Prediction Using Multi-Neighborhood Network with Reconstructing Attention
Spatial transcriptomics (ST) has made it possible to link local spatial gene expression with the properties of tissue, which is very helpful to the research of histopathology and pathology. To obtain more ST d...
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Chapter and Conference Paper
Enhancing Continuous Domain Adaptation with Multi-path Transfer Curriculum
Addressing the large distribution gap between training and testing data has long been a challenge in machine learning, giving rise to fields such as transfer learning and domain adaptation. Recently, Continuou...
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Chapter and Conference Paper
Intelligent Collaborative Control of Multi-source Heterogeneous Data Streams for Low-Power IoT: A Flow Machine Learning Approach
LPWAN has partially replaced traditional wired networks in fields such as smart industry, smart healthcare, smart home, etc., due to its low power consumption, high reliability and low cost. LPWAN can achieve ...
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Chapter and Conference Paper
CAST: An Intricate-Scene Aware Adaptive Bitrate Approach for Video Streaming via Parallel Training
Adaptive Bitrate (ABR) algorithms have become increasingly important for delivering high-quality video content over fluctuating networks. Considering the complexity of video scenes, video chunks can be separat...
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Chapter and Conference Paper
Residual Spatio-Temporal Collaborative Networks for Next POI Recommendation
As location-based services become increasingly integrated into users’ lives, the next point-of-interest (POI) recommendation has become a prominent area of research. Currently, many studies are based on Recurr...
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Chapter and Conference Paper
Meta-Reinforcement Learning Algorithm Based on Reward and Dynamic Inference
Meta-Reinforcement Learning aims to rapidly address unseen tasks that share similar structures. However, the agent heavily relies on a large amount of experience during the meta-training phase, presenting a fo...
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Chapter and Conference Paper
Graph-based Dynamic Preference Modeling for Personalized Recommendation
Sequential Recommendation (SR) can predict possible future behaviors by considering the user’s behavioral sequence. However, users’ preferences constantly change in practice and are difficult to track. The exi...
