Jason V. Paul - IEEE Xplore Author Profile

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Author details

Jason V. Paul

Publications

3

Citations

5

Publications by Year

20072013

Co-Authors:

Robert W. BenningtonPeter J. CollinsRonald A. CoutuYong C. KimRoy Porter

Show All Co-Authors (6)

Jason V. Paul

Affiliation

Electrical and Computer Engineering

Air Force Institute of Technology

OH, USA

Publication Topics

Bitstream,
Clock Cycles,
Dynamic Reconfiguration,
Attenuation Constant,
Azimuthal Wave,
Bessel Function,
Cadence,
Circuit Performance,
Clock Period,
Complex Argument,
Current Architecture,
Dielectric Constant

Biography

Jason Paul received the B.Eng. degree in electrical engineering from Youngstown State University, Youngstown, OH, USA, in 2002 and the M.S. degree from the Air Force Institute of Technology, Dayton, OH, USA, in 2007.,He is a member of the Tau Beta Pi honor society and is currently pursuing a doctorate in electrical engineering. His research interests include electromagnetic materials design, low observables and antitamper.(Based on document published on 24 January 2013).

Publications

3

Citations

5

Publications by Year

20072013

Co-Authors:

Robert W. Bennington
Peter J. Collins
Ronald A. Coutu
Yong C. Kim
Roy Porter

Show All Co-Authors (6)

Author's Published Works

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Showing 1-21 of 21 results

Conferences (17)

Journals (4)

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Zhehuan Zhao(21)
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Bo Xu(13)
Jian Wang(10)
School of Software, Dalian University of Technology, Dalian, China(12)
College of Computer Science and Technology, Dalian University of Technology, Dalian, China(12)
Academy of Military Medical Sciences, Beijing, China(3)
Institute of Health Service and Transfusion Medicine, Academy of Military Medical Sciences, Beijing, China(2)
Beijing Institute of Health Administration and Medical Information, Beijing, China(2)
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)(5)
2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)(2)
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2)
IEEE Transactions on NanoBioscience(2)
2012 IEEE International Conference on Bioinformatics and Biomedicine(1)
IEEE(21)
Lisbon, Portugal(5)
Madrid, Spain(2)
Seoul, Korea, Republic of(2)
Abu Dhabi, United Arab Emirates(1)
Budapest, Hungary(1)
Medical Literature(8)
Graph Convolutional Network(6)
Word Embedding(6)
Attention Mechanism(5)
Convolutional Neural Network(5)

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Efficient Knowledge Graph Embedding Framework to Alleviate Data Sparsity for Polypharmacy Side Effects Prediction

Senbo Tu;Zhihao Yang;Lei Wang;Wei Liu;Yin Zhang;Ling Luo;Bo Xu;Jian Wang;Yumeng Yang;Zhehuan Zhao;Hongfei Lin

2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2024 | Conference Paper |

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Polypharmacy is the combined use of multiple drugs for the treatment of diseases, which also often comes with a higher risk of side effects. In the medical industry, acquiring rich and comprehensive information about the side effects of multiple drug therapy becomes a crucial task. However, data collection for many side effects is often sparse, so the features of these data cannot be adequately le...Show More

Efficient Knowledge Graph Embedding Framework to Alleviate Data Sparsity for Polypharmacy Side Effects Prediction

Senbo Tu;Zhihao Yang;Lei Wang;Wei Liu;Yin Zhang;Ling Luo;Bo Xu;Jian Wang;Yumeng Yang;Zhehuan Zhao;Hongfei Lin

2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2024 | Conference Paper |

Biomedical Event Extraction as Semantic Segmentation

Liangyu Gao;Zhihao Yang;Jinzhong Ning;Kai Ma;Lei Wang;Wei Liu;Yin Zhang;Ling Luo;Bo Xu;Jian Wang;Yumeng Yang;Zhehuan Zhao;Yuanyuan Sun;Hongfei Lin

2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2024 | Conference Paper |

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In the biomedical field, information is widely distributed across numerous pieces of literature. Extracting events between entities from biomedical texts has garnered significant attention in recent years. However, previous research primarily focus on extracting flat biomedical events, with less attention given to nested biomedical events. Moreover, existing methods for extracting nested events of...Show More

Biomedical Event Extraction as Semantic Segmentation

Liangyu Gao;Zhihao Yang;Jinzhong Ning;Kai Ma;Lei Wang;Wei Liu;Yin Zhang;Ling Luo;Bo Xu;Jian Wang;Yumeng Yang;Zhehuan Zhao;Yuanyuan Sun;Hongfei Lin

2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2024 | Conference Paper |

Document Embeddings Enhance Biomedical Retrieval-Augmented Generation

Yongle Kong;Zhihao Yang;Ling Luo;Zeyuan Ding;Lei Wang;Wei Liu;Yin Zhang;Bo Xu;Jian Wang;Yuanyuan Sun;Zhehuan Zhao;Hongfei Lin

2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2024 | Conference Paper |

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Large language models (LLMs) perform well in many NLP tasks but frequently generate inaccurate information in the biomedical domain, due to hallucination issues. Retrieval-Augmented Generation (RAG) has been introduced to address this issue by integrating external knowledge, enhancing the factual accuracy of outputs. However, naive RAG encounters challenges in effectively utilizing retrieved conte...Show More

Document Embeddings Enhance Biomedical Retrieval-Augmented Generation

Yongle Kong;Zhihao Yang;Ling Luo;Zeyuan Ding;Lei Wang;Wei Liu;Yin Zhang;Bo Xu;Jian Wang;Yuanyuan Sun;Zhehuan Zhao;Hongfei Lin

2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2024 | Conference Paper |

Document-level Biomedical Relation Extraction Based on Relation-guided Entity-level Graphs

Liangyu Gao;Zhihao Yang;Haixin Tan;Lei Wang;Wei Liu;Yin Zhang;Ling Luo;Bo Xu;Jian Wang;Yumeng Yang;Zhehuan Zhao;Yuanyuan Sun;Hongfei Lin

2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2024 | Conference Paper |

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The task of document-level biomedical relation extraction involves identifying relational facts between entities across sentences, given specific entities. However, most current methods overlook the associations between entity pairs and generate fixed entity representations merely through mentions, leading to irrelevant mentions interfering with the determination of relational facts. Additionally,...Show More

Document-level Biomedical Relation Extraction Based on Relation-guided Entity-level Graphs

Liangyu Gao;Zhihao Yang;Haixin Tan;Lei Wang;Wei Liu;Yin Zhang;Ling Luo;Bo Xu;Jian Wang;Yumeng Yang;Zhehuan Zhao;Yuanyuan Sun;Hongfei Lin

2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2024 | Conference Paper |

Biomedical Document-level Relation Extraction with Coreference and Anaphor Graphs

Jiru Li;Yuanyuan Sun;Zhihao Yang;Ling Luo;Lei Wang;Wei Liu;Yin Zhang;Bo Xu;Jian Wang;Yumeng Yang;Zhehuan Zhao;Hongfei Lin

2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2024 | Conference Paper |

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Biomedical document-level relation extraction is a crucial technology for mining the biomedical relationships necessary for clinical diagnosis, treatment, and medical discovery. Although existing intrasentential relation extraction methods have achieved significant results, the complexity and scattered nature of information in biomedical literature require relation extraction techniques to effecti...Show More

Biomedical Document-level Relation Extraction with Coreference and Anaphor Graphs

Jiru Li;Yuanyuan Sun;Zhihao Yang;Ling Luo;Lei Wang;Wei Liu;Yin Zhang;Bo Xu;Jian Wang;Yumeng Yang;Zhehuan Zhao;Hongfei Lin

2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2024 | Conference Paper |

HIM: Discovering Implicit Relationships in Heterogeneous Social Networks

Bo Xu;Jinpeng Wang;Zhehuan Zhao;Hongfei Lin;Feng Xia

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

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To date, research on relation mining has typically focused on analyzing explicit relationships between entities, while ignoring the underlying connections between entities, known as implicit relationships. Exploring implicit relationships can reveal more about social dynamics and potential relationships in heterogeneous social networks to better explain complex social behaviors. The research prese...Show More

HIM: Discovering Implicit Relationships in Heterogeneous Social Networks

Bo Xu;Jinpeng Wang;Zhehuan Zhao;Hongfei Lin;Feng Xia

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Semantics Driven Multi-View Knowledge Graph Embedding for Cross-Lingual Entity Alignment

Xin Zhang;Yu Liu;Zhehuan Zhao

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Cited by: Papers (2)

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Cross-lingual entity alignment (EA) is a critical step in the integration of multilingual knowledge, which aims to match entities with the same meaning in different knowledge graphs (KGs). Recently, based on GCN models and pre-trained language models (PLMs), EA has achieved breakthrough performance by utilizing graph structures and auxiliary semantic information. However, existing EA methods rely ...Show More

Semantics Driven Multi-View Knowledge Graph Embedding for Cross-Lingual Entity Alignment

Xin Zhang;Yu Liu;Zhehuan Zhao

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2024 | Conference Paper |

Unsupervised Anomaly Detection on Attributed Networks With Graph Contrastive Learning for Consumer Electronics Security

Bo Xu;Jinpeng Wang;Zhehuan Zhao;Hongfei Lin;Feng Xia

IEEE Transactions on Consumer Electronics

Year: 2024 | Volume: 70, Issue: 1 | Journal Article |

Cited by: Papers (3)

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The proliferation of consumer electronic products has engendered a substantial surge in data generation and information exchange, concurrently escalating the potential for security threats. Detecting anomalies effectively on attributed networks has undeniable positive significance for consumer electronic security, such as fraudulent user detection, malicious consumption actions analysis, and netwo...Show More

Unsupervised Anomaly Detection on Attributed Networks With Graph Contrastive Learning for Consumer Electronics Security

Bo Xu;Jinpeng Wang;Zhehuan Zhao;Hongfei Lin;Feng Xia

IEEE Transactions on Consumer Electronics

Year: 2024 | Volume: 70, Issue: 1 | Journal Article |

Biomedical Named Entity Recognition Through Deep Reinforcement Learning

Zhehuan Zhao;Yuying Zou;Menghan Wang;Bo Xu;Zhihao Yang;Hongfei Lin

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2023 | Conference Paper |

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Biomedical Named Entity Recognition (BioNER) is a crucial task in extracting entities from biomedical literature. It plays a key role as the initial step in various biomedical information extraction tasks. Pre-training models have gained popularity in BioNER. However, these models require a large number of parameters, which poses a barrier for many researchers due to the hardware resource requirem...Show More

Biomedical Named Entity Recognition Through Deep Reinforcement Learning

Zhehuan Zhao;Yuying Zou;Menghan Wang;Bo Xu;Zhihao Yang;Hongfei Lin

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2023 | Conference Paper |

Dual Relation-Aware Entity Alignment for Knowledge Graph

Xin Zhang;Yu Liu;Zhehuan Zhao

2023 International Joint Conference on Neural Networks (IJCNN)

Year: 2023 | Conference Paper |

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Entity alignment (EA) is a vital step for knowledge fusion, which aims to discover entities with the same meaning from the different knowledge graphs. Several researchers attempted to obtain enhanced entity embeddings by using the strategies that relation-aware entity embeddings for EA. However, they ignored the effect of aggregating different range neighborhoods of entities on relation embeddings...Show More

Dual Relation-Aware Entity Alignment for Knowledge Graph

Xin Zhang;Yu Liu;Zhehuan Zhao

2023 International Joint Conference on Neural Networks (IJCNN)

Year: 2023 | Conference Paper |

TL-BERT: A Novel Biomedical Relation Extraction Approach

Zhehuan Zhao;Yuying Zou;Yang Tian;Bo Xu;Zhihao Yang;Jian Wang;Hongfei Lin;Shimin Shan;Yu Liu

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2021 | Conference Paper |

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Automatically extracting entity-pair interactions from biomedical literature plays an important role in promoting the development of the biomedical field. For instance, the interactions between drugs can guide patients to take drugs correctly and avoid clinical adverse drug reactions; The interactions between proteins can help researchers design therapeutic drugs and discover disease mechanisms. H...Show More

TL-BERT: A Novel Biomedical Relation Extraction Approach

Zhehuan Zhao;Yuying Zou;Yang Tian;Bo Xu;Zhihao Yang;Jian Wang;Hongfei Lin;Shimin Shan;Yu Liu

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2021 | Conference Paper |

A Context-Based Network For Referring Image Segmentation

Xinyu Li;Yu Liu;Kaiping Xu;Zhehuan Zhao;Sipei Liu

2020 IEEE International Conference on Image Processing (ICIP)

Year: 2020 | Conference Paper |

Cited by: Papers (2)

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Referring image segmentation is an important task aiming at segmenting out the object referred by a natural language expression. Current works usually employ the methods of concatenating the visual and linguistic features. They underestimate the importance of language-to-vision and object-to-object relationships when the natural language expression has multiple entities. Therefore, we propose a ne...Show More

A Context-Based Network For Referring Image Segmentation

Xinyu Li;Yu Liu;Kaiping Xu;Zhehuan Zhao;Sipei Liu

2020 IEEE International Conference on Image Processing (ICIP)

Year: 2020 | Conference Paper |

Disease Gene Prediction Based on Heterogeneous Probabilistic Hypergraph Ranking

Feng Ding;Anfu Liu;Chenxu Bai;Bo Xu;Xiaoxia Liu;Shengtian Sang;Hongfei Lin;Zhihao Yang;Jian Wang;Xiangjie Kong;Zhehuan Zhao;Feng Xia

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2019 | Conference Paper |

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In order to save time and cost, many disease gene prediction methods have been proposed in recent years. However, the traditional network model uses a binary relationship to represent the relationship between different proteins or gene molecules and phenotypes, which leads to the loss of information. Recently, hypergraph shows that it can overcome this loss of information to some extent and preser...Show More

Disease Gene Prediction Based on Heterogeneous Probabilistic Hypergraph Ranking

Feng Ding;Anfu Liu;Chenxu Bai;Bo Xu;Xiaoxia Liu;Shengtian Sang;Hongfei Lin;Zhihao Yang;Jian Wang;Xiangjie Kong;Zhehuan Zhao;Feng Xia

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2019 | Conference Paper |

Distantly Supervised Relation Extraction through a Trade-off Mechanism

Jun Ni;Yu Liu;Kai Wang;Zhehuan Zhao;Quan Z. Sheng

2019 International Joint Conference on Neural Networks (IJCNN)

Year: 2019 | Conference Paper |

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Distantly supervised relation extraction can label large amounts of unstructured text without human annotations for training. However, distant supervision inevitably accompanies with the wrong labeling problem, which can deteriorate the performance of relation extraction. What's more, the entity-pair information, which can enrich instance information, is still underutilized. In the light of these ...Show More

Distantly Supervised Relation Extraction through a Trade-off Mechanism

Jun Ni;Yu Liu;Kai Wang;Zhehuan Zhao;Quan Z. Sheng

2019 International Joint Conference on Neural Networks (IJCNN)

Year: 2019 | Conference Paper |

Incorporating User Generated Content for Drug Drug Interaction Extraction Based on Full Attention Mechanism

Bo Xu;Xiufeng Shi;Yueqin Yin;Zhehuan Zhao;Wei Zheng;Hongfei Lin;Zhihao Yang;Jian Wang;Feng Xia

IEEE Transactions on NanoBioscience

Year: 2019 | Volume: 18, Issue: 3 | Journal Article |

Cited by: Papers (7)

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It is crucial for doctors to fully understand the interaction between drugs in prescriptions, especially when a patient takes multiple medications at the same time during treatment. The purpose of drug drug interaction (DDI) extraction is to automatically obtain the interaction between drugs from biomedical literature. Current state-of-the-art approaches for DDI extraction task are based on artifi...Show More

Incorporating User Generated Content for Drug Drug Interaction Extraction Based on Full Attention Mechanism

Bo Xu;Xiufeng Shi;Yueqin Yin;Zhehuan Zhao;Wei Zheng;Hongfei Lin;Zhihao Yang;Jian Wang;Feng Xia

IEEE Transactions on NanoBioscience

Year: 2019 | Volume: 18, Issue: 3 | Journal Article |

HMNPPID: A Database of Protein-protein Interactions Associated with Human Malignant Neoplasms

Qingqing Li;Zhihao Yang;Zhehuan Zhao;Ling Luo;Zhiheng Li;Lei Wang;Yin Zhang;Hongfei Lin;Jian Wang;Yijia Zhang

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2018 | Conference Paper |

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Protein-protein interactions (PPIs) play a key role in varies aspects of the structural and functional organization of the cell. Knowledge about them unveils the molecular mechanisms of biological processes. At the same time, the PPIs related to human malignant neoplasms can shed light on the biology behind these neoplasms. However, there is no such PPI database available so far. We constructed a ...Show More

HMNPPID: A Database of Protein-protein Interactions Associated with Human Malignant Neoplasms

Qingqing Li;Zhihao Yang;Zhehuan Zhao;Ling Luo;Zhiheng Li;Lei Wang;Yin Zhang;Hongfei Lin;Jian Wang;Yijia Zhang

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2018 | Conference Paper |

Full-attention Based Drug Drug Interaction Extraction Exploiting User-generated Content

Bo Xu;Xiufeng Shi;Zhehuan Zhao;Wei Zheng;Hongfei Lin;Zhihao Yang;Jian Wang;Feng Xia

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2018 | Conference Paper |

Cited by: Papers (9)

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When a patient takes multiple medications simultaneously under treatment, it is vital for the doctor to comprehend all interactions between drugs in the prescription entirely. Drug drug interaction (DDI) extraction aims to obtain interactions between drugs from biomedical literature automatically. Nowadays, researchers apply artificial intelligence and natural language processing techniques to per...Show More

Full-attention Based Drug Drug Interaction Extraction Exploiting User-generated Content

Bo Xu;Xiufeng Shi;Zhehuan Zhao;Wei Zheng;Hongfei Lin;Zhihao Yang;Jian Wang;Feng Xia

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2018 | Conference Paper |

Leveraging Biomedical Resources in Bi-LSTM for Drug-Drug Interaction Extraction

Bo Xu;Xiufeng Shi;Zhehuan Zhao;Wei Zheng

Year: 2018 | Volume: 6 | Journal Article |

Cited by: Papers (40)

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The discovery of drug-drug interaction (DDI) is not only critical in understanding the mechanism of medicine, but also aids in preventing medical error and controlling healthcare costs. When physicians or pharmacists prescribe multiple drugs simultaneously to a single patient, DDI can be a crucial piece of information to keep the patient from experiencing adverse reactions or any other potential p...Show More

Leveraging Biomedical Resources in Bi-LSTM for Drug-Drug Interaction Extraction

Bo Xu;Xiufeng Shi;Zhehuan Zhao;Wei Zheng

Year: 2018 | Volume: 6 | Journal Article |

A hybrid protein-protein interaction triple extraction method for biomedical literature

Zhehuan Zhao;Zhihao Yang;Cong Sun;Lei Wang;Hongfei Lin

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2017 | Conference Paper |

Cited by: Papers (4)

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Protein-protein interaction extraction research can be widely applied to the field of life science research. However, most of the machine learning based methods focus on binary PPI relation extraction, which loses rich relationship type information that is critical to the PPIs study. The rule based open information extraction methods can extract the PPI triple (i.e. “protein1, interaction word, pr...Show More

A hybrid protein-protein interaction triple extraction method for biomedical literature

Zhehuan Zhao;Zhihao Yang;Cong Sun;Lei Wang;Hongfei Lin

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2017 | Conference Paper |

PPIExtractor: A Protein Interaction Extraction and Visualization System for Biomedical Literature

Zhihao Yang;Zhehuan Zhao;Yanpeng Li;Yuncui Hu;Hongfei Lin

IEEE Transactions on NanoBioscience

Year: 2013 | Volume: 12, Issue: 3 | Journal Article |

Cited by: Papers (14)

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Protein-protein interactions (PPIs) play a key role in various aspects of the structural and functional organization of the cell. Knowledge about them unveils the molecular mechanisms of biological processes. However, the amount of biomedical literature regarding protein interactions is increasing rapidly and it is difficult for interaction database curators to detect and curate protein interactio...Show More

PPIExtractor: A Protein Interaction Extraction and Visualization System for Biomedical Literature

Zhihao Yang;Zhehuan Zhao;Yanpeng Li;Yuncui Hu;Hongfei Lin

IEEE Transactions on NanoBioscience

Year: 2013 | Volume: 12, Issue: 3 | Journal Article |

PPIExtractor: A protein-protein interaction Extractor for biomédical literature

Zhihao Yang;Zhehuan Zhao;Yanpeng Li;Yuncui Hu;Hongfei Lin

2012 IEEE International Conference on Bioinformatics and Biomedicine

Year: 2012 | Conference Paper |

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Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. In this paper, we present a PPI extraction system, termed PPIExtractor, which automatically extracts PPIs from biomedical text and visualizes them. Given a Medline record dataset, PPIExtractor first applies Feature Coupling Generalization (FCG) to tag protein names, next uses the extended ...Show More

PPIExtractor: A protein-protein interaction Extractor for biomédical literature

Zhihao Yang;Zhehuan Zhao;Yanpeng Li;Yuncui Hu;Hongfei Lin

2012 IEEE International Conference on Bioinformatics and Biomedicine

Year: 2012 | Conference Paper |

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