Xin Zhou

A Deep Convolutional Network Demodulator for Mixed Signals with Different Modulation Types

2017

In recent years, deep learning is becoming more and more popular. It has been widely applied to fields including image recognition, automatic speech recognition and natural language processing(NLP). In the field of communication, signals are …

Ethnographic research in software engineering: a critical review and checklist

2019

Software Engineering (SE) community has recently been investing significant amount of effort in qualitative research to study the human and social aspects of SE processes, practices, and technologies. Ethnography is one of the major qualitative …

Toward Efficient Compute-Intensive Job Allocation for Green Data Centers: A Deep Reinforcement Learning Approach

2019

Reducing the energy consumption of the servers in a data center via proper job allocation is desirable. Existing advanced job allocation algorithms, based on constrained optimization formulations capturing servers' complex power consumption and thermal dynamics, …

Toward Automated Calibration of Data Center Digital Twins: A Neural Surrogate Approach

2020 · arXiv (Cornell University)

Evolving the computational fluid dynamics (CFD) model to high fidelity digital twin is desirable for industrial data center management. However, existing CFD model calibration approaches to improve the model accuracy require either excessive manual tuning …

AVAE: Adversarial Neural Network for Self-representation in One Class Classification

2019

To address the bottleneck of machine learning algorithms in one class classification(OCC), we present an end-to-end deep learning model for OCC. The basic idea is that our model is divided into two parts, reconstructor and …

Optimizing Data Center Energy Efficiency via Event-Driven Deep Reinforcement Learning

2022 · IEEE Transactions on Services Computing

To reduce the skyrocketing energy consumption of data centers, the prevailing approaches adopt the time-driven manner to control IT and cooling subsystems. These methods suffer from highly dynamic system states, complex action spaces and the …

A Collaborative Filtering Algorithm based on Entropy and Trust Optimized Neighbor Selection

2022 · 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

The Collaborative filtering (CF) is an emerging technology that deals with overload problems by providing recommendations for products that users may be interested in. Neighbor selection is a key part of the recommendation process. The …

Rethinking Negative Pairs in Code Search

2023

Recently, contrastive learning has become a key component in fine-tuning code search models for software development efficiency and effectiveness. It pulls together positive code snippets while pushing negative samples away given search queries. Among contrastive …

RiQ-KGC: Relation Instantiation Enhanced Quaternionic Attention for Complex-Relation Knowledge Graph Completion

2024 · Applied Sciences

A knowledge graph is a structured semantic network designed to describe physical entities and relations in the world. A comprehensive and accurate knowledge graph is essential for tasks such as knowledge inference and recommendation systems, …

Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness

2024 · arXiv (Cornell University)

Randomized smoothing has become a leading method for achieving certified robustness in deep classifiers against l_{p}-norm adversarial perturbations. Current approaches for achieving certified robustness, such as data augmentation with Gaussian noise and adversarial training, require …

Multi-modal Food Recommendation using Clustering and Self-supervised Learning

2024 · arXiv (Cornell University)

Food recommendation systems serve as pivotal components in the realm of digital lifestyle services, designed to assist users in discovering recipes and food items that resonate with their unique dietary predilections. Typically, multi-modal descriptions offer …

Enhancing the Accuracy and Comprehensibility in Architectural Tactics Detection via Small Model-Augmented Prompt Engineering

2025 · arXiv (Cornell University)

Architectural tactics (ATs), as the concrete implementation of architectural decisions in code, address non-functional requirements of software systems. Due to the implicit nature of architectural knowledge in code implementation, developers may risk inadvertently altering or …

Multimodal Data Integration Improves Disease Risk Prediction in the UK Biobank.

2024 · PubMed

Family health history is an important component to assess risk for common chronic diseases. The integration of electronic health records and genetic data offers great potential to improve disease risk prediction by capturing both clinical …