2,965 Result(s)
within
Algorithm Analysis and Problem Complexity
Computer Communication Networks
**u-**u Zhang
Chapter
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Chapter and Conference Paper
Enhancing Policy Gradient for Traveling Salesman Problem with Data Augmented Behavior Cloning
The use of deep reinforcement learning (DRL) techniques to solve classical combinatorial optimization problems like the Traveling Salesman Problem (TSP) has garnered considerable attention due to its advantage...
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Chapter and Conference Paper
SolGPT: A GPT-Based Static Vulnerability Detection Model for Enhancing Smart Contract Security
In this study, we present SolGPT, a novel approach to addressing the pivotal issue of detecting and mitigating vulnerabilities inherent in smart contracts, particularly those written in Solidity, the predomina...
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Chapter and Conference Paper
Spatial-Temporal Transformer with Error-Restricted Variance Estimation for Time Series Anomaly Detection
Due to the intricate dynamics of multivariate time series in cyber-physical system, unsupervised anomaly detection has always been a research hotspot. Common methods are mainly based on reducing reconstruction...
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Chapter and Conference Paper
Soft Contrastive Learning for Implicit Feedback Recommendations
Collaborative filtering (CF) plays a crucial role in the development of recommendations. Most CF research focuses on implicit feedback due to its accessibility, but deriving user preferences from such feedback...
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Chapter and Conference Paper
Projection-Free Bandit Convex Optimization over Strongly Convex Sets
Projection-free algorithms for bandit convex optimization have received increasing attention, due to the ability to deal with the bandit feedback and complicated constraints simultaneously. The state-of-the-ar...
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Chapter and Conference Paper
GSPM: An Early Detection Approach to Sudden Abnormal Large Outflow in a Metro System
Early detection of Sudden Abnormal Large Outflow (SALO) aims to determine abnormal large outflows and locate the station where real-time outflow significantly exceeds expectations. SALO serves as a crucial indica...
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Chapter and Conference Paper
Enhanced HMM Map Matching Model Based on Multiple Type Trajectories
Map matching (MM) aims to align GPS trajectory with the actual roads on a map that vehicles pass through, essential for applications like trajectory search and route planning. The Hidden Markov Model (HMM) is ...
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Chapter and Conference Paper
MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading
Algorithmic trading refers to executing buy and sell orders for specific assets based on automatically identified trading opportunities. Strategies based on reinforcement learning (RL) have demonstrated remark...
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Chapter and Conference Paper
A Joint Optimization Scheme in Heterogeneous UAV-Assisted MEC
Mobile Edge Computing (MEC) is considered as a promising technology to meet the high-quality service requirements of emerging applications in mobile intelligent terminals. It can effectively handle computation...
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Chapter and Conference Paper
Towards Multi-subsession Conversational Recommendation
Conversational recommendation systems (CRS) could acquire dynamic user preferences towards desired items through multi-round interactive dialogue. Previous CRS works mainly focus on the single conversation (subse...
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Chapter and Conference Paper
Dual-Graph Convolutional Network and Dual-View Fusion for Group Recommendation
Group recommendation constitutes a burgeoning research focus in recommendation systems. Despite a multitude of approaches achieving satisfactory outcomes, they still fail to address two major challenges: 1) th...
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Chapter and Conference Paper
MPRG: A Method for Parallel Road Generation Based on Trajectories of Multiple Types of Vehicles
Accurate and up-to-date digital road maps are the foundation of many applications, such as navigation and autonomous driving. Recently, the ubiquity of GPS devices in vehicular systems has led to an unpreceden...
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Chapter and Conference Paper
Towards Cost-Efficient Federated Multi-agent RL with Learnable Aggregation
Multi-agent reinforcement learning (MARL) often adopts centralized training with a decentralized execution (CTDE) framework to facilitate cooperation among agents. When it comes to deploying MARL algorithms in...
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Chapter and Conference Paper
We Will Find You: An Edge-Based Multi-UAV Multi-Recipient Identification Method in Smart Delivery Services
Unmanned aerial vehicle (UAV) is increasingly becoming a promising solution for last-mile delivery in smart logistics, and multi-UAV scenarios have become increasingly common. In multi-UAV delivery services, t...
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Chapter and Conference Paper
SD-Attack: Targeted Spectral Attacks on Graphs
Graph learning (GL) models have been applied in various predictive tasks on graph data. But, similarly to other machine learning models, GL models are also vulnerable to adversarial attacks. As a powerful atta...
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Chapter and Conference Paper
Learning Disentangled Task-Related Representation for Time Series
Multivariate time series representation learning employs unsupervised tasks to extract meaningful representations from time series data, enabling their application in diverse downstream tasks. However, despite...
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Chapter and Conference Paper
Efficient and Accurate Similarity-Aware Graph Neural Network for Semi-supervised Time Series Classification
Semi-supervised time series classification has become an increasingly popular task due to the limited availability of labeled data in practice. Recently, Similarity-aware Time Series Classification (SimTSC) ha...
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Chapter and Conference Paper
Reallocation Mechanisms Under Distributional Constraints in the Full Preference Domain
We study the problem of reallocating indivisible goods among a set of agents in one-sided matching market, where the feasible set for each good is subject to an associated distributional matroid or M-convex const...
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Chapter and Conference Paper
Modeling Treatment Effect with Cross-Domain Data
Treatment effect estimation has received increasing attention recently. However, the issue of data sparsity often poses a significant challenge, limiting the feasibility of modeling. This paper aims to leverage c...
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Chapter and Conference Paper
Neuron Pruning-Based Federated Learning for Communication-Efficient Distributed Training
Efficient and flexible cloud computing is widely used in distributed systems. However, in the Internet of Things (IoT) environment with heterogeneous capabilities, the performance of cloud computing may declin...
