Rui Li

Signal sorting by combining Bayesian method and Gibbs sampling strategy

2015

Radar signal sorting technology is critical in electronic warfare, and the level of sorting is an important indicator to measure the radar reconnaissance system. In this work, signals manipulated by the same source from multiple …

The Strategy of Small and Medium-sized Enterprises to Build Health Information Ecosystem

2016 · Advances in computer science research

On the basis of the information ecology theory research, Combined with the problems existing in the enterprise information ecosystem, In-depth analysis of the problem such as optimal allocation of enterprise information ecosystem Enterprise information index …

System Design of English Test Examination Room Arrangement

2015

With the rapid development of the computer and universal application, its high quality and efficient work efficiency provides people with modern tools and instruments, examination room arrangement problem is a problem that has been plagued …

A Novel Multi-objective Optimization Scheme for Rebalancing Virtual Machine Placement

2016

In cloud computing, load balancing is a very important performance factor. Frequent addition and removal of Virtual Machines (VMs) can cause load imbalance across Host Machines (HMs). Therefore, redistribution of VMs to different HMs needs …

Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes

2019 · Neural Information Processing Systems

This paper studies statistical characteristics of multivariate observations with irregular changes in their covariance structures across input space. We propose a unified nonstationary modeling framework to jointly encode the observation correlations to generate a piece-wise …

PHYAlert: identity spoofing attack detection and prevention for a wireless edge network

2020 · Journal of Cloud Computing Advances Systems and Applications

Abstract Delivering service intelligence to billions of connected devices is the next step in edge computing. Wi-Fi, as the de facto standard for high-throughput wireless connectivity, is highly vulnerable to packet-injection-based identity spoofing attacks (PI-ISAs). …

Approximate Quantum Adders With Genetic Algorithms: An IBM Quantum Experience

2017 · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)

It has been proven that quantum adders are\n\t\t\t\t forbidden by the laws of quantum mechanics. We analyze\n\t\t\t\t theoretical proposals for the implementation of approximate\n\t\t\t\t quantum adders and optimize them by means\n\t\t\t\t of genetic algorithms, improving …

COMPARATIVE STUDY OF MUTATION OPERATORS IN THE GENETIC ALGORITHMS FOR THE K-MEANS PROBLEM

2021 · Facta Universitatis Series Mathematics and Informatics

The k-means problem and the algorithm of the same name are the most commonly used clustering model and algorithm. Being a local search optimization method, the k-means algorithm falls to a local minimum of the …

Optimal Model for Fewer-Qubit CNOT Gates With Rydberg Atoms

2022 · Physical Review Applied

Fewer-qubit quantum logic gate, serving as a basic unit for constructing universal multiqubit gates, has been widely applied in quantum computing and quantum information. However, traditional constructions for fewer-qubit gates often utilize a multipulse protocol, …

Improved collaborative filtering algorithm using intuitionistic fuzzy C -means clustering and incorporating time factors

2022 · Research Square

Abstract Collaborative filtering (CF) has been widely used to help users discover items of interest. Most of the existing CF methods collect preference information from other users to predict user’s ratings. However, the user’s interest …

Towards Improved Learning in Gaussian Processes: The Best of Two Worlds

2022 · arXiv (Cornell University)

Gaussian process training decomposes into inference of the (approximate) posterior and learning of the hyperparameters. For non-Gaussian (non-conjugate) likelihoods, two common choices for approximate inference are Expectation Propagation (EP) and Variational Inference (VI), which have …

Feature Alignment and Uniformity for Test Time Adaptation

2023 · arXiv (Cornell University)

Test time adaptation (TTA) aims to adapt deep neural networks when receiving out of distribution test domain samples. In this setting, the model can only access online unlabeled test samples and pre-trained models on the …

Are Human-generated Demonstrations Necessary for In-context Learning?

2023 · arXiv (Cornell University)

Despite the promising few-shot ability of large language models (LLMs), the standard paradigm of In-context Learning (ICL) suffers the disadvantages of susceptibility to selected demonstrations and the intricacy to generate these demonstrations. In this paper, …

Distributed Data Sharing and Access Control in Industrial IoT Using Blockchain Technology

2024

With the rapid development of the Industrial Internet of Things (IIoT), secure and efficient data sharing has become crucial for enabling industrial automation and smart transformation. However, existing centralized solutions face limitations in scalability, latency, …

Wavelet-Guided Hybrid Attention Neural Network for Identification of Microseismic Event

2025 · IEEE Access

Microseismic monitoring technology enables accurate and real-time identification of the spatial location and type of geological hazards, serving as a crucial component of early warning systems for geological disaster prevention and mitigation. Due to the …

Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment

2025 · arXiv (Cornell University)

With rapid advancement and increasing accessibility of LLMs, fine-tuning aligned models has become a critical step for adapting them to real-world applications, which makes the safety of this fine-tuning process more important than ever. However, …

Content Diversity-guided Ambiguity Mitigation for Open-Set Noisy Label Learning

2026 · Proceedings of the AAAI Conference on Artificial Intelligence

Open-set noisy label learning faces a critical challenge in maintaining robust DNN performance when training data contain both in-distribution noisy (IDN) and out-of-distribution (OOD) samples. These noisy samples induce overconfident but erroneous predictions due to …

Overcriminalization in Cybercrime Governance: Judicial Restraint in Regulating Assistance to Online Crimes in China

2026 · Journal of Legal Studies

Abstract Overcriminalization has become a central concern in contemporary cybercrime governance. In China, the Crime of Assisting Information Network Criminal Activities (CAINCA), introduced in 2015, criminalizes assistance that creates abstract risks within cybercrime networks. In …