Wei Li

An Empirical Investigation of Predicting Fault Count, Fix Cost and Effort Using Software Metrics

2016 · International Journal of Advanced Computer Science and Applications

Software fault prediction is important in software engineering field. Fault prediction helps engineers manage their efforts by identifying the most complex parts of the software where errors concentrate. Researchers usually study the fault-proneness in modules …

Analysis of the Survey and Solve Path to "Telecom and Network Fraud" in Colleges and Universities

2017 · DEStech Transactions on Social Science Education and Human Science

Through the questionnaire survey on the "students' understanding of "telecom network fraud" to the students in colleges and universities, we More accurately grasp the college students in case of "telecommunications network fraud", college students' attitude …

Generative Model for Heterogeneous Inference

2018 · arXiv (Cornell University)

Generative models (GMs) such as Generative Adversary Network (GAN) and Variational Auto-Encoder (VAE) have thrived these years and achieved high quality results in generating new samples. Especially in Computer Vision, GMs have been used in …

Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network

2018 · arXiv (Cornell University)

As more and more academic papers are being submitted to conferences and journals, evaluating all these papers by professionals is time-consuming and can cause inequality due to the personal factors of the reviewers. In this …

Efficient and Privacy-preserving Voice-based Search over mHealth Data

2018 · arXiv (Cornell University)

In-home IoT devices play a major role in healthcare systems as smart personal assistants. They usually come with a voice-enabled feature to add an extra level of usability and convenience to elderly, disabled people, and …

Improved DNN-HMM English Acoustic Model Specially For Phonotactic Language Recognition

2019

The now-acknowledged sensitive of Phonotactic Language Recognition (PLR) to the performance of the phone recognizer front-end have spawned interests to develop many methods to improve it. In this paper, improved Deep Neural Networks Hidden Markov …

A cloud-based framework for verifiable privacy-preserving spectrum auction

2021 · High-Confidence Computing

Spectrum auction is one of the most effective ways to achieve dynamic spectrum allocation in cognitive radio networks , and it provides one effective way to manage the spectrum demands of IoT devices with limited …

The optimal positive operator-valued measure for state discrimination

2021 · arXiv (Cornell University)

Evaluating the amount of information obtained from non-orthogonal quantum states is an important topic in the field of quantum information. The commonly used evaluation method is Holevo bound, which only provides a loose upper bound …

Enhanced Velocity-Driven Particle Swarm Optimization for Evolving Artificial Neural Network

2021

research-article Share on Enhanced Velocity-Driven Particle Swarm Optimization for Evolving Artificial Neural Network Authors: Wei Li Xi'an University of Technology, China Xi'an University of Technology, ChinaView Profile , Haonan Luo Xi'an University of Technology, China …

SgSum: Transforming Multi-document Summarization into Sub-graph Selection

2021 · arXiv (Cornell University)

Most of existing extractive multi-document summarization (MDS) methods score each sentence individually and extract salient sentences one by one to compose a summary, which have two main drawbacks: (1) neglecting both the intra and cross-document …

Phase-matching quantum key distribution with light source monitoring

2021 · Chinese Physics B

The transmission loss of photons during quantum key distribution (QKD) process leads to the linear key rate bound for practical QKD systems without quantum repeaters. Phase matching quantum key distribution (PM-QKD) protocol, an novel QKD …

Evolutionary Action Selection for Gradient-based Policy Learning

2022 · arXiv (Cornell University)

Evolutionary Algorithms (EAs) and Deep Reinforcement Learning (DRL) have recently been integrated to take the advantage of the both methods for better exploration and exploitation.The evolutionary part in these hybrid methods maintains a population of …

Improving Non-native Word-level Pronunciation Scoring with Phone-level Mixup Data Augmentation and Multi-source Information

2022 · arXiv (Cornell University)

Deep learning-based pronunciation scoring models highly rely on the availability of the annotated non-native data, which is costly and has scalability issues. To deal with the data scarcity problem, data augmentation is commonly used for …

Large-scale coherent Ising machine based on optoelectronic parametric oscillator

2022 · Light Science & Applications

Ising machines based on analog systems have the potential to accelerate the solution of ubiquitous combinatorial optimization problems. Although some artificial spins to support large-scale Ising machines have been reported, e.g., superconducting qubits in quantum …

Iaso: Enhancing Syntax Error Correction using Deep Learning Techniques

2023 · Research Square

Abstract Syntax errors can significantly impede students' progress and comprehension of programming concepts. AI-generated programs are also prone to syntax bugs, highlighting the importance of effectively addressing and resolving such errors. This paper presents Iaso, …

PasCore: A Chinese Overlapping Relation Extraction Model Based on Global Pointer Annotation Strategy

2023

Recent work for extracting relations from texts has achieved excellent performance. However, existing studies mainly focus on simple relation extraction, these methods perform not well on overlapping triple problem because the tags of shared entities …

ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area

2024 · arXiv (Cornell University)

Large Language Models (LLMs) have achieved remarkable success and have been applied across various scientific fields, including chemistry. However, many chemical tasks require the processing of visual information, which cannot be successfully handled by existing …

FinerDedup: Sifting Fingerprints for Efficient Data Deduplication on Mobile Devices

2024

Data deduplication is promised to extend the lifetime and capacity of storage on mobile devices. However, existing data deduplication works show high memory consumption and indexing costs for maintaining a fingerprint for each data block, …

PGDiffSeg: Prior-Guided Denoising Diffusion Model with Parameter-Shared Attention for Breast Cancer Segmentation

2024 · arXiv (Cornell University)

Early detection through imaging and accurate diagnosis is crucial in mitigating the high mortality rate associated with breast cancer. However, locating tumors from low-resolution and high-noise medical images is extremely challenging. Therefore, this paper proposes …

Machine Learning-Driven Container Scheduling for Edge-Empowered Microservices

2025

This research investigates the integration of machine learning technology into microservices architectures for edge-enabled container scheduling strategies. Deploying microservices applications can be intricate, while container virtualisation technology solves environmental complexities. However, container scheduling strategies remain …

Design and Implementation of Satellite Status Processing and Monitoring System Based on Multi-channel High-rate Frame Processing

2025 · Chinese Journal of Space Science

星地稳定的信道建立使得地面控制中心能够接收来自卫星的遥测数据、跟踪和控制信号, 以及科学和观测数据. 传统在轨卫星下行数据通道包括卫星遥测信道数据以及数传信道数据, 其中遥测信道数据指的是S波段遥测信道下行数据, 而数传信道数据则指的是数传数据工程参数. 基于数传信道数据的高速率、双通道以及多应用模式等特点, 设计了一种卫星状态量处理监视系统. 该系统不仅能够处理多通道数据, 还具备对高速率数传数据工程参数进行拼帧处理、解析、显示、查询、存储以及异常报警等功能. 该软件已成功应用于在轨卫星的任务运控系统, 满足对卫星型号平台和载荷的实时监视需求.

Improving FAIR Compliance for High-Dimensional Data via Automated Metadata Extraction

2025

High-dimensional datasets are becoming an increasingly vital asset in the machine learning and AI domains due to their ability to capture large volumes of structured, self-descriptive information. Research data repositories play a key role in …

Exploring Federated Pruning for Large Language Models

2025 · arXiv (Cornell University)

LLM pruning has emerged as a promising technology for compressing LLMs, enabling their deployment on resource-limited devices. However, current methodologies typically require access to public calibration samples, which can be challenging to obtain in privacy-sensitive …

Improving non-native mispronunciation detection and enriching diagnostic feedback with DNN-based speech attribute modeling

2016

We propose the use of speech attributes, such as voicing and aspiration, to address two key research issues in computer assisted pronunciation training (CAPT) for L2 learners, namely detecting mispronunciation and providing diagnostic feedback. To …

Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling

2018

Information selection is the most important component in document summarization task. In this paper, we propose to extend the basic neural encoding-decoding framework with an information selection layer to explicitly model and optimize the information …

Recommending what video to watch next

2019

In this paper, we introduce a large scale multi-objective ranking system for recommending what video to watch next on an industrial video sharing platform. The system faces many real-world challenges, including the presence of multiple …

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

2019 · arXiv (Cornell University)

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning …

Multi-Interest Network with Dynamic Routing for Recommendation at Tmall

2019

Industrial recommender systems have embraced deep learning algorithms for building intelligent systems to make accurate recommendations. At its core, deep learning offers powerful ability for learning representations from data, especially for user and item representations. …

Behavior sequence transformer for e-commerce recommendation in Alibaba

2019

Deep learning based methods have been widely used in industrial recommendation systems (RSs). Previous works adopt an Embedding&MLP paradigm: raw features are embedded into low-dimensional vectors, which are then fed on to MLP for final …