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Proceedings of the 2025 5th International Conference on Bioinformatics and Intelligent Computing

作者： Shuangquan Zhang Jinxin Jia Peng Gao Xuan Li Yan Wang School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu China College of Software Jilin University Changchun Jilin China College of Computer Science and Technology Jilin University Changchun Jilin China

ISBN: (纸本)9798400712203

Transcription Factor Binding Sites (TFBSs) are specific DNA sequences that promote or inhibit gene expression via binding regions, thereby affecting protein synthesis. TFBSs play a crucial role in gene regulatory networks, complex diseases, and the growth of humans. Assay for Transposase-Accessible Chromatin with high-throughput Sequencing (ATAC-seq) is a high-throughput sequencing technology, providing a new perspective for studying transcription factor binding patterns (motifs). Different from sequencing ChIP-seq technologies, ATAC-seq is more efficient in detecting binding sites. Methods based on graph neural network have success on ATAC-seq motifs, but researchers make considerable effort to deploy these models. To address this issue, we developed a web server named GNNMF-ATAC for users. GNNMF-ATAC provides TFBSs prediction, motif finding, and enrichment analysis via deploying tools. Moreover, GNNMF-ATAC provides motif finding results and visualizations of 200 humans and 80 mouse ENCODE ATAC-seq datasets. GNNMF-ATAC is a valuable resource for experimental biologists seeking a user-friendly tool to find ATAC-seq motifs without programming expertise. GNNMF-ATAC is freely available at http://***.

关键词： ATAC-seq

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Policy-Based Access Control System for Delta Lake

Policy-Based Access Control System for Delta Lake

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Zhe Chen Hangyu Shao Yuping Li Hongru Lu Jiahui Jin School of Software Engineering Southeast University Nanjing Jiangsu China School of Computer Science and Engineering Southeast University Suzhou Jiangsu China China Information Consulting & Designing Institute CO LTD Nanjing Jiangsu China School of Information Engineering Nanjing Audit University Nanjing Jiangsu China School of Computer Science and Engineering Southeast University Nanjing Jiangsu China

ISBN: (纸本)9798350309720

Delta lake is a new generation of data storage solutions. It stores both transaction log and data files in one directory, and provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing on top of existing data lakes, such as S3, ADLS, GCS, and HDFS. Different from data warehouses, delta lakes allow data to be stored in the original format, retain complete data information, and provide efficient and low-cost storage solutions for data computing and analysis businesses. However, Since Delta Lake metadata is scattered in different resource files, the lack of a unified metadata view increases the difficulty of data governance. Also, Delta Lake adopts an open source storage system as the underlying storage, and its basic access control does not isolate different users, which may lead the risk of data leakage. At present, most common storage systems use data tables’ row and column fields for access control, while delta lake treats the file group as an object. In this paper, aiming at the difficulty of data governance, we design a data lake metadata management method to achieve unified and efficient management of metadata information in heterogeneous data. Then, we design a policy-based data lake access control mechanism, combined with the open source permission framework, and complete the access request for different users and roles in Delta Lake.

关键词： Access control Memory Lakes Metadata Lead Data warehouses Big Data applications

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An adaptive scaling cuckoo search algorithm 4

An adaptive scaling cuckoo search algorithm

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4th International Conference on Advanced Electronic Materials, computers and software engineering, AEMCSE 2021

作者： Song, Zhenyu Song, Shuangyu Zhang, Bin Qian, Jin Tang, Cheng Ji, Junkai Taizhou University College of Computer Science and Technology Taizhou China Jiangsu University of Technology School of Computer Engineering Changzhou China University of Toyama Faculty of Engineering Toyama Japan Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9781665415965

The cuckoo search algorithm (CSA) is a natureinspired metaheuristic algorithm based on the parasitic behaviour of the cuckoo species, and it has received much attention for solving optimization problems. Nevertheless, the CSA still has some limitations when applied to optimization problems, such as easily falling into local optima and converging slowly. In this paper, we propose a modified local search method with an adaptive boundary scaling mechanism using a chaotic map, which calculates the search radius depending on the position of the current population. Thus, the search ability of the traditional CSA is improved for optimization purposes. To further verify the stability and effectiveness of the proposed adaptive boundary scaling algorithm (ACSA), we compare it with the traditional CSA and nonmodified chaos-embedded CSA (CCSA) on 25 test benchmark functions. The experimental and statistical test results show that the ACSA outperforms its competitors, which means that the proposed algorithm can perform better than these other approaches in terms of their search capabilities for optimization problems. © 2021 IEEE.

关键词： Optimization

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Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Cheng, Zihan Guo, Jintao Zhang, Jian Qi, Lei Zhou, Luping Shi, Yinghuan Gao, Yang Shanghai Jiao Tong University School of Medicine Shanghai200025 China National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210093 China State Key Laboratory for Novel Software Technology the National Institute of Healthcare Data Science Nanjing University Jiangsu Nanjing210093 China School of Computer Science and Engineering Key Lab of Computer Network and Information Integration Southeast University Jiangsu Nanjing211189 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2006 Australia

To segment medical images with distribution shifts, domain generalization (DG) has emerged as a promising setting to train models on source domains that can generalize to unseen target domains. Existing DG methods are mainly based on CNN or ViT architectures. Recently, advanced state space models, represented by Mamba, have shown promising results in various supervised medical image segmentation. The success of Mamba is primarily owing to its ability to capture long-range dependencies while keeping linear complexity with input sequence length, making it a promising alternative to CNNs and ViTs. Inspired by the success, in the paper, we explore the potential of the Mamba architecture to address distribution shifts in DG for medical image segmentation. Specifically, we propose a novel Mamba-based framework, Mamba-Sea, incorporating global-to-local sequence augmentation to improve the model’s generalizability under domain shift issues. Our Mamba-Sea introduces a global augmentation mechanism designed to simulate potential variations in appearance across different sites, aiming to suppress the model’s learning of domain-specific information. At the local level, we propose a sequence-wise augmentation along input sequences, which perturbs the style of tokens within random continuous sub-sequences by modeling and resampling style statistics associated with domain shifts. To our best knowledge, Mamba-Sea is the first work to explore the generalization of Mamba for medical image segmentation, providing an advanced and promising Mamba-based architecture with strong robustness to domain shifts. Remarkably, our proposed method is the first to surpass a Dice coefficient of 90% on the Prostate dataset, which exceeds previous SOTA of 88.61%. The code is available at https://***/orange-czh/Mamba-Sea. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis

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computers, Materials & Continua 2022年第7期72卷 1123-1137页

作者： Xiaorui Zhang Jie Zhou Wei Sun Sunil Kumar Jha Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing 210044 China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

The key to preventing the COVID-19 is to diagnose patients quickly and *** have shown that using Convolutional Neural Networks(CNN)to analyze chest Computed Tomography(CT)images is helpful for timely COVID-19 ***,personal privacy issues,public chest CT data sets are relatively few,which has limited CNN’s application to COVID-19 ***,many CNNs have complex structures and massive *** if equipped with the dedicated Graphics Processing Unit(GPU)for acceleration,it still takes a long time,which is not conductive to widespread *** solve above problems,this paper proposes a lightweight CNN classification model based on transfer *** the lightweight CNN MobileNetV2 as the backbone of the model to solve the shortage of hardware resources and computing *** order to alleviate the problem of model overfitting caused by insufficient data set,transfer learning is used to train the *** study first exploits the weight parameters trained on the ImageNet database to initialize the MobileNetV2 network,and then retrain the model based on the CT image data set provided by *** results on a computer equipped only with the Central Processing Unit(CPU)show that it consumes only 1.06 s on average to diagnose a chest CT *** to other lightweight models,the proposed model has a higher classification accuracy and reliability while having a lightweight architecture and few parameters,which can be easily applied to computers without GPU ***:***/ZhouJie-520/paper-codes.

关键词： Convolutional neural networks chest computed tomography image COVID-19 transfer learning mobileNetv2

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A Time-Aware Recommendation Model with Social Impact

A Time-Aware Recommendation Model with Social Impact

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International Conference on Dependable Systems and Their Applications (DSA)

作者： Yuheng Su Yi Zhu Mei Song Weichao He Guosheng Hao School of Computer Science and Technology Jiangsu Normal University Xuzhou China Jiangsu Engineering Technology Research Center of ICT in Education Xuzhou China Ministry of Industry and Information Technology Key Laboratory of Safety-Critical Software(Nanjing University of Aeronautics and Astronautics) Nanjing China

ISBN: (数字)9798331532390

ISBN: (纸本)9798331532406

Sequential recommendation has emerged as a significant research focus within the realm of recommender systems. This approach often involves modeling user interactions with items as isomorphic graphs, where nodes represent items and edges denote interactions. Graph Neural Networks (GNNs) have been effectively employed to capture higher-order relationships between nodes in these graphs, enhancing the quality of recommendations. Despite the success of graph-based models in sequential recommendation, there is a notable gap in the literature regarding the integration of social influence. Many existing studies concentrate primarily on item interactions without adequately addressing how social factors might affect sequential recommendations. To address this gap, we propose the Time-Aware Recommendation Model with Social Influence (TARMSI). This model emphasizes the role of social relationships in sequential recommendation by incorporating social influence into the recommendation process. TARMSI operates on a fine-grained level, considering both item categories and interaction times to enhance the recommendation quality. Specifically, it introduces social relations into the user representation framework, allowing for a more nuanced understanding of item interactions based on social contexts. In addition to integrating social influence, TARMSI employs temporal self-attention networks to model users’ dynamic preferences. This approach enables the model to adapt to changes in user preferences over time, further improving the accuracy of recommendations. The effectiveness of the TARMSI model is demonstrated through extensive experiments conducted on three mainstream datasets, showcasing its superiority over existing methods in capturing the influence of social interactions and temporal dynamics on sequential recommendations. By addressing the limitations of previous models, TARMSI offers a more comprehensive and contextually aware approach to recommendation systems.

关键词： Hands Adaptation models Accuracy Education Graph neural networks Social factors Recommender systems Context modeling

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Frequency-aware Interaction Network for Ultrasound Image Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2024年

作者： Wang, Dongfang Zhou, Tao Zhang, Yizhe Gao, Shangbing Yang, Jian Nanjing University Of Science And Technology Pca Lab Key Lab Of Intelligent Percept. And Syst. For High-Dimensional Information Of Ministry Of Education Jiangsu Key Lab Of Image And Video Understanding For Social Security School Of Computer Science And Engineering Nanjing210094 China Huaiyin Institute Of Technology Faculty Of Computer And Software Engineering Huaian223003 China

Accurate segmentation of medical ultrasound images is crucial for guiding treatment decisions and assessing intervention effectiveness. The challenge of segmenting lesions in ultrasound images arises from factors such as low contrast, high speckle noise, artifacts, and blurred boundaries. Furthermore, this complexity varies significantly among lesions in different cases. While methods based on Convolutional Neural Networks (CNNs) and Transformers have shown promising results in this field, each approach possesses distinct advantages and limitations. To address these challenges, we propose a novel Frequency-aware Interaction Network (FINet). At the core of our FINet lies the proposed Multi-scale Frequency-aware Self-attention (MFS) module, which effectively captures multi-scale feature information within the self-attention layer. This enables our network to model both local and global features, capitalizing on the strengths of both CNNs and Transformers. Additionally, a frequency-aware network is introduced to learn the interactions between spatial locations in the frequency domain to enhance detailed feature representation such as edges. Furthermore, we present a collaborative interactive decoder network, in which a Selective Feature Interaction (SFI) module is proposed to facilitate the semantic and boundary feature interaction, resulting in more precise segmentation outcomes. Experimental results on four medical ultrasound image datasets show the superiority of our FINet over other state-of-the-art segmentation methods. More importantly, our model achieves an excellent trade-off between performance and computational efficiency. © 1991-2012 IEEE.

关键词： Ultrasonic imaging

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Support Vector Machine Based Automatic Classification Method for IoT Big Data Features

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Journal of computers (Taiwan) 2023年第5期34卷 15-27页

作者： Xu, Yong-Hua School of Computer Engineering Jinling Institute of Technology Nanjing211169 China Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of Technology Nanjing211169 China

As China’s information technology development shifts from a single high-speed growth stage to a multidimensional high-quality development stage, the Internet of Things (IoT) enters all aspects of life and becomes more and more popular. The demand for IoT big data information analysis and processing is increasing, and the important role of feature automatic classification methods becomes increasingly prominent. This research proposes SPO-SVM and WSPO-SVM models based on support vector machine for smart home environment monitoring data under the big data of Internet of Things, and then optimizes them with particle swarm optimization algorithm and adaptive method. Finally, the data set is selected for comparative experimental analysis of each optimization algorithm model. The experimental results show that the optimized WSPO-SVM model has less total misclassification and single class misclassification compared with other algorithms under Wine dataset. In cross-validation, both its classification accuracy performance outperformed other algorithms. Under 10 sets of smart home environment monitoring data sets, the WSPO-SVM algorithm model achieves 100% accuracy in 6 out of 10 test data sets, with an average accuracy of 97.67%, which is about 9% higher than the ordinary SVM algorithm model and about 15% higher than other feature classification algorithms. The experimental results prove that the WSPO-SVM algorithm can complete the feature classification work in the IoT big data environment, which meets the expectation. © 2023 computer Society of the Republic of China. All rights reserved.

关键词： Internet of things

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HyCU: Hybrid Consistent Update for software Defined Network

HyCU: Hybrid Consistent Update for Software Defined Network

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Xudong Mou Jie Sun Yingying Zhong Tianyu Wo School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Gusu Laboratory of Materials Suzhou Jiangsu China SKLSDE School of Software Beihang University Beijing China

software Defined Network (SDN) enables network operators to achieve the customization of network services, which tends to be more dynamic and fine-grained. However, the distributed nature of rule updating in SDN brings consistency problems, i.e., packets travel according to different versions of rules. It leads to the issues of blackholes, loops, congestion, and deadlock in the data plane, which may further affect the service quality of the application plane. With the emergence of new computing paradigms such as edge computing and fog computing, the heterogeneity of network devices and links, as well as the diversity of network application requirements have become increasingly prominent. Traditional update methods ignore these key factors when modeling, so they cannot cope with the increasingly complex network environment, resulting in delays or packet loss rates that do not meet service requirements. This paper proposes HyCU, which takes device performance as a constraint and optimizes updates based on flow service requirements. We conduct experiments under different scenarios and constraints over two real-world topologies with real-time running flows, demonstrating the effectiveness of HyCU.

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Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

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Advances in Manufacturing 2021年第3期9卷 388-402页

作者： Long-Hua Xu Chuan-Zhen Huang Jia-Hui Niu Jun Wang Han-Lian Liu Xiao-Dan Wang Center for Advanced Jet Engineering Technologies Key Laboratory of High-efficiency and Clean Mechanical Manufacture(Ministry of Education)National Experimental Teaching Demonstration Center for Mechanical Engineering(Shandong University)School of Mechanical EngineeringShandong UniversityJinanPeople’s Republic of China School of Mechanical and Manufacturing Engineering The University of New South Wales(UNSW)SydneyNSW2052Australia Department of Computer Science and Software Engineering Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuPeople’s Republic of China

During the actual high-speed machining process,it is necessary to reduce the energy consumption and improve the machined surface ***,the appropriate prediction models and optimal cutting parameters are difficult to obtain in complex machining ***,a novel intelligent system is proposed for prediction and optimization.A novel adaptive neuro-fuzzy inference system(NANFIS)is proposed to predict the energy consumption and surface *** the NANFIS model,the membership functions of the inputs are expanded into:membership superior and membership *** membership functions are varied based on the machining *** inputs of the NANFIS model are cutting parameters,and the outputs are the machining *** optimization,the optimal cutting parameters are obtained using the improved particle swarm optimization(IPSO)algorithm and NANFIS ***,the IPSO algorithm as a learning algorithm is used to train the NANFIS *** proposed intelligent system is applied to the high-speed milling process of compacted graphite *** experimental results show that the predictions of energy consumption and surface roughness by adopting the NANFIS models are up to 91.2%and 93.4%,*** NANFIS models can predict the energy consumption and surface roughness more accurately compared with other intelligent *** on the IPSO algorithm and NANFIS models,the optimal cutting parameters are obtained and validated to reduce both the cutting power and surface roughness and improve the milling *** is demonstrated that the proposed intelligent system is applicable to actual high-speed milling processes,thereby enabling sustainable and intelligent manufacturing.

关键词： Novel adaptive neuro-fuzzy inference system(NANFIS)model Improved particle swarm optimization(IPSO)algorithm Energy consumption Surface roughness Multiobjective optimization

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