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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Haifeng Long, Qiuyu Zhao, Nan Nanjing Normal University School of Computer and Electronic Information China Nanjing University of Chinese Medicine Department of Big Data Management and Application China Nanjing University of Chinese Medicine School of Artificial Intelligence and Information Technology China

ISBN: (纸本)9798350386226

Drug repositioning offers a promising strategy to identify novel therapeutic applications for existing drugs. Despite the frequent use of graph neural network in this domain, their efficacy is often hampered by the sparsity of known drug interaction networks. Furthermore, diseases within the same subclass frequently share structural and clinical traits, leading to similar complications and responses to specific treatments among related *** tackle the challenges of sparse drug interaction networks and leverage the shared treatment potential among similar diseases, we propose Dron, a dual-branched contrast-enhanced method for drug repositioning. Dron enhances drug and disease representations using a branched contrastive loss strategy, and improves identification accuracy by aggregating features from related categories. Validation across multiple public datasets shows that Dron significantly enhances drug repositioning performance. Molecular docking experiments on Alzheimer's disease further confirm Dron's effectiveness in addressing complex diseases. © 2024 IEEE.

关键词： Contrast Learning Drug repositioning Graph Neural Network

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RAUIE: A Relation-Augmented Document-Level Event Extraction Model Based on UIE 5

RAUIE: A Relation-Augmented Document-Level Event Extraction ...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Liang, Yiyuan Jia, Hong Wang, Licai Huang, Yangchen Yang, Zhe Big Data Research and Development Center North China Institute of Computing Technology Beijing China Tsinghua University Department of Computer Science and Technology Beijing China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350385557

In document-level event extraction tasks, multiple event types often coexist within a document (the multi-event problem), and arguments may play different roles across different events (the argument intertwining problem). Existing methods often suffer from significantly reduced event extraction accuracy, mainly when focusing on scenarios with scarce annotated data and imbalanced event-type distributions. This paper emphasizes enhancing the correlation between event triggers and arguments to address these challenges. Therefore, we propose the Relation-Augmented UIE Model (RADIE), which introduces entity relationships into prompts using prompt-based learning and designs a relation-augmentation algorithm to improve the accuracy and completeness of event extraction. Experimental results demonstrate that this approach significantly outperforms baseline methods, mainly showcasing excellent performance on datasets with long-tail distributions. © 2024 IEEE.

关键词： document-level event extraction relation augment Universal Information Extraction

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Hotspots and frontiers of the relationship between gastric cancer and depression:A bibliometric study

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World Journal of Gastroenterology 2023年第46期29卷 6076-6088页

作者： Jia-Yu Liu Ji-Qi Zheng Cheng-Liang Yin Wen-Pei Tang Jian-Ning Zhang Department of Neurosurgery The First Medical CentreChinese PLA General HospitalBeijing 100853China School of Health Humanities Peking UniversityBeijing 100191China Medical Big Data Research Center Medical Innovation Research Division of Chinese PLA General HospitalBeijing 100853China National Engineering Research Center for Medical Big Data Application Technology Chinese PLA General HospitalBeijing 100853China

BACKGROUND A significant relationship between gastric cancer(GC)and depression has been found in the last 20 ***,there is no comprehensive information that helps researchers find popular and potential research directions on GC and *** To determine the research status and hotspots by bibliometric analysis of relevant publications on the relationship between GC and *** We used the Web of Science Core Collection to search and collate the literature on GC and depression from 2000 to 2022 on 31 May,***,visualization analysis was performed using VOSviewer software(version 1.6.19)and the Bibliometrix package in R *** We retrieved 153 pertinent publications from 2000 to *** annual publication count showed an overall upward *** had the most prominent publications and significant contributions to this field(n=64,41.83%).Before 2020,most studies focused on“the effect of GC on the development and progression of depression in patients.”The latest research trends indicate that“the effect of depression on the occurrence and development of GC and its mechanism”will receive more attention in the *** The study of“the effect of depression on the occurrence and development of GC and its mechanism”has emerged as a novel research theme over the past two years,which may become a research hotspot in this *** study provides new insights into the hotpots and frontiers of the relationship between GC and depression,potentially guiding researchers toward hot research topics in the future.

关键词： Gastric cancer Depression Bibliometric analysis Visualization Web of Science

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An Overhead Crane Anti-Swing Control Method Integrating Clustering Salp Swarm Algorithm and Fuzzy PID

An Overhead Crane Anti-Swing Control Method Integrating Clus...

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2023 China Automation Congress, CAC 2023

作者： Sun, Zhe Yue, Lei Sun, Zhixin Post Industry Technology Research and Development Center of State Posts Bureau Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9798350303759

To address the anti-swing problem in the overhead crane system, fuzzy PID is a common approach. However, the requirement for expert involvement in parameter configuration of fuzzy PID poses challenges for its widespread application. In order to address this issue, we introduce clustering operators into the enhancement of the Salp Swarm Algorithm. By integrating Clustering Salp Swarm Algorithm and fuzzy PID and conduct comparative experiments with other classical swarm intelligence algorithms, the proposed approach enables effective fuzzy control even in the absence of expert experience. Simulation validation of this method has been conducted in the overhead crane system. © 2023 IEEE.

关键词： Fuzzy control

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Rankings vs Realities: Exploring Competency Differences in Graduate data Science Programs 53

Rankings vs Realities: Exploring Competency Differences in G...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Li, Duo Milonas, Elizabeth Zhang, Qiping Shenyang Institute of Technology Department of Big Data Management and Application Fushun China Computer Systems Technology Brooklyn United States Palmer School of Library and Information Science Long Island University Brookville United States

ISBN: (纸本)9798350336429

Numerous educational institutions, including those specializing in computer science, engineering, business, and science, now offer graduate programs in data science. However, it remains uncertain whether the data science programs provided by universities with different rankings deliver similar or distinct data science competencies. This study aims to compare the competencies of graduate data science programs offered in the United States. It addressed the research question of 'Are there any differences in graduate data science program competencies based on their university rankings?' The program competencies of 228 data science graduate programs as well as their U.S. News university rankings were collected and compared. The findings indicate that regardless of their national rankings, all universities offer most of the competencies required for data science programs, except for Machine Learning and Sensor Networks. Forty-six percent of data science programs are offered in universities that hold national rankings within the top 75, focusing on majors such as data science and business analytics. Furthermore, the top-ranked universities provide data science programs across all departments or schools, with the most prominent ones being Business, Computer Science, Information Science & technology, and Math/Statistics. The primary research contribution of this study revolves around conducting a comprehensive analysis of data science program competencies within the United States. The implications of the findings have significant relevance to computing education. The study provides valuable guidelines for educational practices in the field of data science: 1) naming the new majors as either 'data Science' or 'Business Analytics';2) housing the new data science programs within existing departments or schools specializing in Business, Computer Science, Information Science & technology, and Math/Statistics;3) incorporating all ACM data Science competencies into the curriculum o

关键词： ACM Competencies Computer Science Computing Education data Science Competence data Science Education Graduate Education

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EPRFL:An Efficient Privacy-Preserving and Robust Federated Learning Scheme for Fog Computing

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China Communications 2025年第4期22卷 202-222页

作者： Ke Zhijie Xie Yong Syed Hamad Shirazi Li Haifeng School of Computer Technology and Application Qinghai UniversityXining 810016China School of Computer and Information Science Qinghai Institute of TechnologyXining 810016China Guangdong Key Laboratory of Blockchain Security Guangzhou UniversityGuangzhou 510006China Department of Information Technology Hazara UniversityBaffa 21110Pakistan Qinghai Provincial Key Laboratory of Big Data in Finance and Artificial Intelligence Application Technology Xining 810016China

Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple *** combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data *** address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing ***,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node ***,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication *** minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive *** analysis demonstrates that EPRFL offers robust security and low *** experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.

关键词： federated learning fog computing internet of things privacy-preserving robustness

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Short-Term Power Load Forecasting and Correlation Analysis of Meteorological Factors and Summer Electricity Demand: A Case Study of Chongqing, Fujian, and Anhui in China

Short-Term Power Load Forecasting and Correlation Analysis o...

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Frontiers of Power and Energy (ACFPE), Asian Conference on

作者： Yachen Xiao Zongpeng Li Hanmei Ma Liangli Su Honggang Wang Application Development Department Big Data Center of State Grid Corporation of China Beijing China

As climate change warms the planet, extreme heat is a dangerous weather condition that is becoming more common and severe. In the summer of 2022, the record-breaking heat wave resulted in frenzied electricity demand and a hydropower production shrinkage in many regions, putting enormous pressure on power grids. In order to improve the climate resilience of power grids for extreme weather peaks, the correlation relationships between meteorological factors and electricity consumption and power load at the granularity of major electricity user types are investigated for the regions of Chongqing, Fujian, and Anhui in China. Utilizing univariate regression and corresponding metrics, we found that residential is the most temperature-sensitive user type (temperature sensitivity 17%-26%) and the largest contributor to meteorological electricity demand (44%-77%), while large industrial is insignificantly related to temperature (temperature sensitivity <2%) but still makes a considerable contribution to electricity demand increment (20%-25%). Finally, the study proposed a new architecture of short-term maximum power load forecast models that takes electricity user categorization into account, which outperforms other proposed models. The mean absolute percentage error (MAPE) of the model for Chongqing, Fujian, and Anhui are 8.1%, 1.3%, and 3.1%, respectively.

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A Lightweight CNN-Transformer Network with Agent Attention Mechanism for 3D Whole Heart Segmentation 7

A Lightweight CNN-Transformer Network with Agent Attention M...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Li, Jiatong Wang, Yifan Du, Dianrong Cui, Hengfei Northwestern Polytechnical University Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China Chongqing Innovation Center Northwestern Polytechnical University Chongqing China

ISBN: (纸本)9798350350890

In recent years, heart diseases have become one of the major global health issues, emphasizing the importance of 3D whole heart segmentation technology for accurate and efficient treatment. Currently, 3D whole heart segmentation algorithms often combine Transformer technologies with 3D Convolutional Neural Networks (3D CNNs), leveraging the self-attention mechanism of Transformers for global information modeling and 3D CNNs for capturing local contextual information. However, the high computational complexity of Transformers leads to huge computational resource wastage. To address this issue, this work proposes a novel whole heart segmentation algorithm named AgentTransBTS, based on an improved 3D Agent Attention mechanism and the TransBTS framework. This algorithm utilizes TransBTS as the baseline model, capitalizing on its deep feature extraction and information integration capabilities to capture global dependencies in images. To reduce computational complexity without sacrificing the model's expressive power, an enhanced 3D Agent Attention mechanism is introduced. Comprehensive experiments were conducted on the Multi-Modality Whole Heart Segmentation (MM-WHS) 2017 dataset to evaluate the proposed algorithm. The experimental results demonstrate that our proposed method achieved an average Dice coefficient of 91.9% on the CT dataset and 79.9% on the MRI dataset of the MM-WHS 2017 challenge. Furthermore, our algorithm's FLOPs are 217G, representing an approximate 9.2% improvement in computational efficiency compared to the baseline network. © 2024 IEEE.

关键词： Convolutional neural networks

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Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

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Computers, Materials & Continua 2024年第8期80卷 2721-2741页

作者： Tareq Hamadneh Khalid Kaabneh Ibraheem AbuFalahah Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Sayan Omarov Zeinab Monrazeri Frank Werner Mohammad Dehghani Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Faculty of Information Technology Al-Ahliyya Amman UniversityAmman19328Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Department of Big Data Analytics and Data Science Software Development Company《QazCode》Almaty050063Kazakhstan Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany

This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization(MFO),inspired by the unique behaviors observed in magnificent frigatebirds in their natural *** foundation of MFO is based on the kleptoparasitic behavior of these birds,where they steal prey from other *** this process,a magnificent frigatebird targets a food-carrying seabird,aggressively pecking at it until the seabird drops its *** frigatebird then swiftly dives to capture the abandoned prey before it falls into the *** theoretical framework of MFO is thoroughly detailed and mathematically represented,mimicking the frigatebird’s kleptoparasitic behavior in two distinct phases:exploration and *** the exploration phase,the algorithm searches for new potential solutions across a broad area,akin to the frigatebird scouting for vulnerable *** the exploitation phase,the algorithm fine-tunes the solutions,similar to the frigatebird focusing on a single target to secure its *** evaluate MFO’s performance,the algorithm is tested on twenty-three standard benchmark functions,including unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** results from these evaluations highlight MFO’s proficiency in balancing exploration and exploitation throughout the optimization *** studies with twelve well-known metaheuristic algo-rithms demonstrate that MFO consistently achieves superior optimization results,outperforming its competitors across various *** addition,the implementation of MFO on four engineering design problems shows the effectiveness of the proposed approach in handling real-world applications,thereby validating its practical utility and robustness.

关键词： Optimization metaheuristic magnificent frigatebirds exploration exploitation

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application of an ADRC based on Wandering Lion Swarm Optimization Algorithm in Overhead Crane Systems Control 41

Application of an ADRC based on Wandering Lion Swarm Optimiz...

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第41届中国控制会议

作者： Zhe Sun Lei Yue Yijun Du Yufei Wang Xiangpeng Xie Zhixin Sun Post Industry Technology Research and Development Center of the State Posts Bureau(Internet of Things Technology) Post Big Data Technology and Application Engineering Research Center of Jiangsu ProvinceNanjing University of Posts and Telecommunications School of Automation Nanjing Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In order to improve the anti-interference and anti sway control performance of overhead crane system,an auto disturbance rej ection controller based on wandering strategy and improved LSO algorithm is designed in this *** at the parameter setting problem of automatic disturbance rejection controller(ADRC),the wandering lion group optimization algorithm(WLSO) is introduced,including Lion King wandering strategy and Lion Group wandering *** implementing numeric experiment test,the effectiveness of the WLSO is confirmed by benchmark ***,by implementing the simulation experiments under different conditions,the results show that the WLSO algorithm based ADRC controller has significantly modified the suppression of load swing and position control as well as the anti-interference.

关键词： Overhead crane Anti-swing control ADRC WLSO algorithm

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