Yan Wang

Research on User Segmentation based on RFL Model and K-means Clustering Algorithm

2015 · Advances in intelligent systems research/Advances in Intelligent Systems Research

With the rapidly shifting dynamics of the current market, the companies are seeking a more thorough method to research the preferences of their target market. As such, data mining models of user segmentation are often …

Parallel Coevolution of Quantum-Behaved Particle Swarm Optimization for High-Dimensional Problems

2016 · Communications in computer and information science

Quantum-behaved particle swarm optimization (QPSO) has successfully been applied to unimodal and multimodal optimization problems. However, with the emergence and popularity of big data and deep machine learning, QPSO encounters limitations with high dimensional problems. …

Research and Discussion on Practical Teaching Reform of Control Surveying

2017

In order to meet the needs of the applicationoriented transformation of surveying and mapping, a series of reforms were made to the experiment and practice of "control survey" from three aspects: teaching content, teaching methods …

Research on the Community Intelligent Wireless Distributed Information Publishing System

2018 · Proceedings of the 1st International Conference on Contemporary Education and Economic Development (CEED 2018)

Based on the research of the intelligent wireless distributed information publishing system in the new community, using wireless ad-hoc network technology, intelligent control technology, intelligent display technology, and regional distributed interactive information technology to the …

Memory access integrity: detecting fine-grained memory access errors in binary code

2019 · Cybersecurity

As one of the most notorious programming errors, memory access errors still hurt modern software security. Particularly, they are hidden deeply in important software systems written in memory unsafe languages like C/C++. Plenty of work …

Discrete Intelligible Recognition Network

2020

We present a new approach to recognize object and we test it in MNIST data set. The main purpose of this method is to solve some problems encountered by most current artificial intelligence. Firstly, most …

A Social Recommendation Method Based on Double-Layer Weak Relation Network

2022

In recent years, social recommendation become a popular research direction. Most social recommendation algorithms use solid relations for the recommendation, which causes a severe problem of accumulation of homogenized information in the recommendation list. Therefore, …

Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design

2020 · arXiv (Cornell University)

Deep Neural Networks (DNNs) have achieved remarkable progress in various real-world applications, especially when abundant training data are provided. However, data isolation has become a serious problem currently. Existing works build privacy preserving DNN models …

Automatic Test Scoring System Based on Deep Learning Technology

2022

The traditional examination papers generally use manual marking, which is a very time-consuming and inefficient marking method. If teachers manually correct students' examination papers every time, not only the workload is heavy, but also due …

Vibration Dataset

2023 · Zenodo (CERN European Organization for Nuclear Research)

Description: In the on wrist scenario, we collect device credentials with different settings on the smartwatches to evaluate the efficacy and robustness of the system. In particular, we conduct experiments with 5 different vibration patterns, …

Uni-Dual: A Generic Unified Dual-Task Medical Self-Supervised Learning Framework

2023

RGB images and medical hyperspectral images (MHSIs) are two widely-used modalities in computational pathology. The former is cheap, easy and fast to obtain while lacking pathological information such as physiochemical state. The latter is an …

CTP: Defending Against Data Poisoning in Attack Traffic Detection Based Deep Neural Networks

2023

Deep learning is extensively employed in attack traffic detection, exhibiting outstanding performance. To enhance model effectiveness, security personnel acquires additional traffic data from public sources for training. However, public source data may not always be …

BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving

2025 · Proceedings of the AAAI Conference on Artificial Intelligence

The rapid development of the autonomous driving industry has led to a significant accumulation of autonomous driving data. Consequently, there comes a growing demand for retrieving data to provide specialized optimization. However, directly applying previous …

Causal Deconfounding via Confounder Disentanglement for Dual-Target Cross-Domain Recommendation

2025 · ACM Transactions on Information Systems

In recent years, dual-target Cross-Domain Recommendation (CDR) has been proposed to capture comprehensive user preferences in order to ultimately enhance the recommendation accuracy in both data-richer and data-sparser domains simultaneously. However, in addition to users’ …

New Challenges for Information Technology Teachers in Basic Education under the "Double Reduction" Policy

2025 · Journal of Education and Educational Research

Taking the “China Education Modernisation 2035” as the policy background, the study systematically explores the three core issues of basic education reform. Firstly, the study analyses the value of the ‘Double Reduction’ policy as a …

Deep Neural Solver for Math Word Problems

2017

This paper presents a deep neural solver to automatically solve math word problems. In contrast to previous statistical learning approaches, we directly translate math word problems to equation templates using a recurrent neural network (RNN) …

A Unified Framework for Cross-Domain and Cross-System Recommendations

2021 · IEEE Transactions on Knowledge and Data Engineering

Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) have been proposed to improve the recommendation accuracy in a target dataset (domain/system) with the help of a source one with relatively richer information. However, most existing CDR …

Sequential Recommender Systems: Challenges, Progress and Prospects

2019 · UTS ePRESS (University of Technology Sydney)

The emerging topic of sequential recommender systems (SRSs) has attracted increasing attention in recent years. Different from the conventional recommender systems (RSs) including collaborative filtering and content-based filtering, SRSs try to understand and model the …

DTCDR

2019

In order to address the data sparsity problem in recommender systems, in recent years, Cross-Domain Recommendation (CDR) leverages the relatively richer information from a source domain to improve the recommendation performance on a target domain …

Graph-to-Tree Learning for Solving Math Word Problems

2020

While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities …

A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation

2020

The conventional single-target Cross-Domain Recommendation (CDR) only improves the recommendation accuracy on a target domain with the help of a source domain (with relatively richer information). In contrast, the novel dual-target CDR has been proposed …

A Survey on Session-based Recommender Systems

2021 · ACM Computing Surveys

Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy. In recent years, session-based recommender systems (SBRSs) have emerged as a …

Cross-Domain Recommendation: Challenges, Progress, and Prospects

2021

To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser …

A Deep Framework for Cross-Domain and Cross-System Recommendations

2018

Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender systems. They leverage the relatively richer information, e.g., ratings, from the source domain …

Large language models for generative information extraction: a survey

2024 · Frontiers of Computer Science

Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural language texts. Recently, generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. As a result, numerous works have …