Jun Zhang

Security and reliability in big data

2016 · Concurrency and Computation Practice and Experience

The purpose of this special issue is to collate a selection of representative research articles that were primarily presented at the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom …

Diversity-Based Multi-Population Differential Evolution for Large-Scale Optimization

2016

There are increasing large-scale optimization problems in science and engineering nowadays. This paper proposes a diversity-based multi-population differential evolution (DB-MPDE) to maintain the population diversity, which is crucial for the large-scale optimizations. The performance of …

A novel genetic algorithm for constructing uniform test forms of cognitive diagnostic models

2016

Cognitive diagnostic models (CDMs) are a new class of test models developed for educational assessment. They have gained growing attention in recent years for their distinctive ability to provide detailed feedback about examinees' ability. Automatic …

Load balance aware distributed differential evolution for computationally expensive optimization problems

2017 · Proceedings of the Genetic and Evolutionary Computation Conference Companion

Computationally expensive problem challenges the application of evolutionary algorithms (EAs) due to the long runtime. Distributed EAs on distributed resources for calculating the individual fitness value in paralllel is a promising method to reduce runtime. …

Noise-Resistant Statistical Traffic Classification

2017 · IEEE Transactions on Big Data

Network traffic classification plays a significant role in cyber security applications and management scenarios. Conventional statistical classification techniques rely on the assumption that clean labelled samples are available for building classification models. However, in the …

Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ

2017 · arXiv (Cornell University)

Purpose: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease following the di- agnosis of ductal carcinoma in situ (DCIS) by core needle biopsy. …

Convolutional encoder-decoder for breast mass segmentation in digital breast tomosynthesis

2018 · Medical Imaging 2018: Computer-Aided Diagnosis

Digital breast tomosynthesis (DBT) is a relatively new modality for breast imaging that can provide detailed assessment of dense tissue within the breast. In the domains of cancer diagnosis, radiogenomics, and resident education, it is …

Stronger uncertainty relations with improvable upper and lower bounds

2016 · arXiv (Cornell University)

We utilize quantum superposition principle to establish the improvable upper and lower bounds on the stronger uncertainty relation, i.e., the "weighted-like" sum of the variances of observables. Our bounds include some free parameters which not …

A Note on Self-Dual Generalized Reed-Solomon Codes

2020 · arXiv (Cornell University)

A linear code is called an MDS self-dual code if it is both an MDS code and a self-dual code with respect to the Euclidean inner product. The parameters of such codes are completely determined …

How Powerful is Graph Convolution for Recommendation?

2021

Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empirical successes remain elusive. In this paper, we endeavor to obtain a better …

Optimizing Niche Center for Multimodal Optimization Problems

2021 · IEEE Transactions on Cybernetics

Many real-world optimization problems require searching for multiple optimal solutions simultaneously, which are called multimodal optimization problems (MMOPs). For MMOPs, the algorithm is required both to enlarge population diversity for locating more global optima and …

Communication-Efficient Federated Edge Learning for NR-U-Based IIoT Networks

2021 · IEEE Internet of Things Journal

As a key infrastructural technology, Industrial Internet of Things (IIoT) and its related techniques have emerged in the age of Industrial Internet. Among them, an increasing popular and attractive federated edge learning (FEL) mechanism, which …

On Deep Holes of Elliptic Curve Codes

2022 · arXiv (Cornell University)

We give a method to construct deep holes for elliptic curve codes. For long elliptic curve codes, we conjecture that our construction is complete in the sense that it gives all deep holes. Some evidence …

A Novel Outdoor Edge Server Design with Hybrid Air Cooling and Refrigeration

2022 · 2022 21st IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (iTherm)

Edge servers are trending up to 24% of total global server deployment in 2024. Several emerging applications are required to deploy with edge servers in harsh outdoor edge environments where special system design is required …

On Deep Holes of Elliptic Curve Codes

2023 · IEEE Transactions on Information Theory

We give a method to construct deep holes for elliptic curve codes. For long elliptic curve codes, we conjecture that our construction is complete in the sense that it gives all deep holes. Some evidence …

A random elite ensemble learning swarm optimizer for high-dimensional optimization

2023 · Complex & Intelligent Systems

Abstract High-dimensional optimization problems are increasingly pervasive in real-world applications nowadays and become harder and harder to optimize due to increasingly interacting variables. To tackle such problems effectively, this paper designs a random elite ensemble …

Probability Distribution-Guided Adversarial Sample Attacks

2023 · Preprints.org

In recent years, with the rapid development of technology, artificial intelligence(AI) security issues represented by adversarial sample attack have aroused widespread concern in society. Adversarial samples are often generated by surrogate models and then transfer …

CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training

2023 · arXiv (Cornell University)

Speech or text representation generated by pre-trained models contains modal-specific information that could be combined for benefiting spoken language understanding (SLU) tasks. In this work, we propose a novel pre-training paradigm termed Continuous Integrate-and-Fire Pre-Training …

Evolutionary Dynamic Database Partitioning Optimization for Privacy and Utility

2023 · IEEE Transactions on Dependable and Secure Computing

Distributed database system (DDBS) technology has shown its advantages with respect to query processing efficiency, scalability, and reliability. Moreover, by partitioning attributes of sensitive associations into different fragments, DDBSs can be used to protect data …

EvoS&R: Evolving Multiple Seeds and Radii for Varying Density Data Clustering

2023 · IEEE Transactions on Knowledge and Data Engineering

Density clustering has shown advantages over other types of clustering methods for processing arbitrarily shaped datasets. In recent years, extensive research efforts has been made on the improvements of DBSCAN or the algorithms incorporating the …

Attentive Multi-Layer Perceptron for Non-autoregressive Generation

2023 · arXiv (Cornell University)

Autoregressive~(AR) generation almost dominates sequence generation for its efficacy. Recently, non-autoregressive~(NAR) generation gains increasing popularity for its efficiency and growing efficacy. However, its efficiency is still bottlenecked by quadratic complexity in sequence lengths, which is …

Quantum state and detector tomography with known rank

2023 · IFAC-PapersOnLine

Quantum state tomography and quantum detector tomography are two main problems in quantum system identification. In this paper, we study state tomography and detector tomography with prior knowledge about the true rank of the unknown …

Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients

2024 · arXiv (Cornell University)

Federated learning (FL) is an emerging distributed training paradigm that aims to learn a common global model without exchanging or transferring the data that are stored locally at different clients. The Federated Averaging (FedAvg)-based algorithms …

Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution

2024 · arXiv (Cornell University)

Evolutionary algorithms, such as Differential Evolution, excel in solving real-parameter optimization challenges. However, the effectiveness of a single algorithm varies across different problem instances, necessitating considerable efforts in algorithm selection or configuration. This paper aims …

Contrastive Learning with High-Quality and Low-Quality Augmented Data for Query-Focused Summarization

2024

Unlike general text summarization, Query-focused summarization (QFS) is severely limited by insufficient datasets, forcing previous research to transform datasets from other tasks into QFS format for data augmentation. However, this approach has resulted in two …

Multiagent Swarm Optimization With Adaptive Internal and External Learning for Complex Consensus-Based Distributed Optimization

2024 · IEEE Transactions on Evolutionary Computation

Distributed optimization has attracted lots of attention in recent years. Thanks to the intrinsic parallelism and great search capacity, evolutionary computation (EC) has the potential for black-box and non-convex distributed optimization. However, due to the …

Computing dimension for a reconfigurable photonic tensor processing core based on silicon photonics

2024 · Optics Express

In the rapidly evolving field of artificial intelligence, integrated photonic computing has emerged as a promising solution to address the growing demand for high-performance computing with ultrafast speed and reduced power consumption. This study presents …

The Effect of Quantization in Federated Learning:A Rényi Differential Privacy Perspective

2024

Federated Learning (FL) is an emerging paradigm that holds great promise for privacy-preserving machine learning using distributed data. To enhance privacy, FL can be combined with Differential Privacy (DP), which involves adding Gaussian noise to …

Multisource Knowledge Fusion Based on Graph Attention Networks for Many-Task Optimization

2024 · IEEE Transactions on Evolutionary Computation

Although knowledge transfer methods are developed for many-task optimization problems, they tend to utilize solutions from a single task for knowledge transfer. Indeed, there are usually multiple relevant source tasks with commonality. Multisource data fusion …

Substation Abnormal Scene Recognition Based on Two-Stage Contrastive Learning

2024 · Energies

Substations are an important part of the power system, and the classification of abnormal substation scenes needs to be comprehensive and reliable. The abnormal scenes include multiple workpieces such as the main transformer body, insulators, …

Individual-Level Dominant Exemplar Selection for Particle Swarm Optimization

2024

Leading exemplars play significant roles in updating particles to seek optimal solutions for Particle Swarm Optimization (PSO). Along this road, this paper devises an Individual-level Dominant Exemplar Selection (IDES) framework for PSO, giving rise to …

Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning

2025 · arXiv (Cornell University)

Chain-of-Thought (CoT) prompting has emerged as a powerful technique for enhancing language model's reasoning capabilities. However, generating long and correct CoT trajectories is challenging. Recent studies have demonstrated that Looped Transformers possess remarkable length generalization …

CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning

2025 · IEEE Transactions on Knowledge and Data Engineering

Active learning (AL) reduces human annotation costs for machine learning systems by strategically selecting the most informative unlabeled data for annotation, but performing it individually may still be insufficient due to restricted data diversity and …

PHP-CABE: Partially Hidden Policy Comparable Attribute-Based Encryption with Computation Outsourcing

2025

With the widespread application of Internet of Things (IoT) devices, an increasing number of edge users are utilizing these devices to collect various types of data, such as health data and e-government data. To achieve …

Real-time vacuum-state quantum random number generator on a chip

2025 · arXiv (Cornell University)

Quantum random number generators (QRNGs) produce true random numbers, which are guaranteed by the fundamental principles of quantum physics. Miniaturization of QRNGs is crucial for a wide range of communication and cryptography applications. Here, we …

Explainable Machine Learning for Student Performance Prediction

2026 · AI in Education

Early identification of at-risk students is crucial for timely pedagogical intervention. Determining which assessments instructors should prioritize is complicated by the fact that different eXplainable-AI (XAI) methods can produce conflicting rankings for the same predictive …

Review on Ansatz Architectures of Variational Quantum Algorithms for Continuous Optimization: From Fixed Structures to Adaptive Evolution

2026 · Processes

Variational quantum algorithms (VQAs) are a leading framework for realizing quantum advantages in the Noisy Intermediate-Scale Quantum (NISQ) era, with applications spanning discrete combinatorial problems and continuous optimization. While the topologies of parameterized quantum circuits …

A Neural Collaborative Filtering Model with Interaction-based Neighborhood

2017

Recently, deep neural networks have been widely applied to recommender systems. A representative work is to utilize deep learning for modeling complex user-item interactions. However, similar to traditional latent factor models by factorizing user-item interactions, …