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Attention Guided Multi Scale Feature Fusion Network for Automatic Prostate Segmentation

computers, Materials & Continua 2024年第2期78卷 1649-1668页

作者： Yuchun Li Mengxing Huang Yu Zhang Zhiming Bai School of Information and Communication Engineering Hainan UniversityHaikouChina College of Computer Science and Technology Hainan UniversityHaikouChina Urology Department Haikou Municipal People’s Hospital and Central South University Xiangya Medical College Affiliated HospitalHaikouChina School of Information Science and Technology Hainan Normal UniversityHaikouChina

The precise and automatic segmentation of prostate magnetic resonance imaging(MRI)images is vital for assisting doctors in diagnosing prostate *** recent years,many advanced methods have been applied to prostate segmentation,but due to the variability caused by prostate diseases,automatic segmentation of the prostate presents significant *** this paper,we propose an attention-guided multi-scale feature fusion network(AGMSF-Net)to segment prostate MRI *** propose an attention mechanism for extracting multi-scale features,and introduce a 3D transformer module to enhance global feature representation by adding it during the transition phase from encoder to *** the decoder stage,a feature fusion module is proposed to obtain global context *** evaluate our model on MRI images of the prostate acquired from a local *** relative volume difference(RVD)and dice similarity coefficient(DSC)between the results of automatic prostate segmentation and ground truth were 1.21%and 93.68%,*** quantitatively evaluate prostate volume on MRI,which is of significant clinical significance,we propose a unique *** essential performance evaluation and validation experiments have demonstrated the effectiveness of our method in automatic prostate segmentation.

关键词： Prostate segmentation multi-scale attention 3D Transformer feature fusion MRI

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Leveraging Packet Cloning to Achieve Fast Flow-Transmission for Data Center Load Balancing 22

Leveraging Packet Cloning to Achieve Fast Flow-Transmission ...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wang, Yuchen Li, Zhenbo Jing, Haotian Zhang, Tao Zou, Shaojun Luan, Xidao Yin, Hui Li, Fangmin Changsha University School of Computer Science and Engineering ChangSha410022 China Hunan Province Key Laboratory of Industrial Internet Technology and Security ChangSha410022 China Hainan University School of Computer Science and Technology Haikou570228 China

ISBN: (纸本)9798331509712

Modern data center network often possesses multiple end-to-end parallel paths, which undertake the crucial task of transmitting vast heterogeneous data traffic generated by a wide variety of applications. To fully utilize the offered super high bisection network bandwidth thus benefiting application performance, many data center load balancing schemes are proposed to improve path utilization for avoiding network congestion hot-spot. However, these schemes are naturally agnostic to data center traffic pattern and the diverse requirements on flow-transmission, leading to the sub-optimal network transmission performance. To address this issue, this paper presents a new data center load balancing scheme, called PCLB, which selectively generates Clone Packets by considering both flow-transmission phases and path states, thereby helping different types of flows choose more appropriate paths for speeding up their data transmission. Experimental results of numerous NS2 simulations show that PCLB significantly reduces the average and tail flow completion time for delay-sensitive flows, while the performance of throughput-oriented flows can be always maintained at high level. © 2024 IEEE.

关键词： Traffic congestion

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An Adaptive Attitude Controller for A Quadrotor Transporting the Payload With Unknown Mass 38

An Adaptive Attitude Controller for A Quadrotor Transporting...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Qiu, Shihao Chen, Longlong Li, Shuang Lv, Zongyang Tang, Huayi Dalian University of Technology School of Computer Science and Technology Dalian China Dalian University of Technology School of Control Science and Engineering Dalian China Henan University of Technology College of Information Science and Engineering Zhengzhou China Dalian University of Technology School of Mechanical Engineering Dalian China

ISBN: (纸本)9798350303636

An adaptive control approach is presented in this article to deal with the impacts of carrying a payload with unknown mass by a quadrotor. This approach can effectively control the quadrotor's attitude stability. The paper commences by presenting the dynamic model of the quadrotor, followed by the development of a controller to manage its attitude control. Through Lyapunov analysis, the stability of the controller is validated. Ultimately, the proposed controller and a backstepping controller are used to controlling the attitude of the quadrotor, respectively, and experimental outcomes substantiate the efficiency and feasibility of the introduced controller. © 2023 IEEE.

关键词： Adaptive Control Quadrotor Unmanned Aerial Vehicle Unknown Mass Payload

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Improving recommendations utilizing users’ demographic information

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Quality and Quantity 2024年第6期58卷 5559-5575页

作者： Dey, Avick Kumar Dutta Pramanik, Pijush Kanti Singh, Pradeep Kumar Choudhury, Prasenjit Department of Computer Applications DSMS College West Bengal Durgapur India School of Computer Science & amp Engineering Galgotias University Greater Noida India Department of Computer Applications & amp Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science & amp Engineering National Institute of Technology Durgapur West Bengal India

The exponential increase in digital data has increased the amount of available online information. This complicates the user’s decision-making. Most online merchants and service providers utilize recommendation systems to solve this problem and meet customer needs. The traditional collaborative filtering based approach faces enormous challenges in providing potential personalized recommendation results. The demographic information of users may improve personalized recommendation results. This research proposes an improved recommendation approach based on users’ demographic information. Compared with traditional collaborative filtering-based approaches, this approach provides improved results. The experimental results show the enhanced prediction accuracy of the proposed approach and significantly lower errors when experimenting with the MovieLens dataset. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Collaborative filtering Demographic information MAE Recommendation system RMSE

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Knowledge Enhancement and Feature Purification for Single-stage Joint Entity and Relation Extraction 29

Knowledge Enhancement and Feature Purification for Single-st...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhai, Guohao Pei, Songwen University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai China

ISBN: (纸本)9798350330717

Joint entity and relation extraction aim to achieve named entity recognition and relation extraction in unstructured text. We use the form of triples (subject, relation, object) to describe entity and relation. Joint entity and relation extraction play an important role in knowledge graph construction, question and answering, data analysis, and other natural language processing domains. Most of the existing works suffered from insufficient interaction between entity features and relation features due to the extraction order. Moreover, there is a prevalence of heterogeneous representations of entities and relations. They not only increase the amount of model design and the complexity of training, but also lead to the problem of exposure bias due to the extraction order. Therefore, in this paper, we propose KFRel, where we enhance the entity and relation representations by encoding text and relation. Then, the features are fused to enhance the entity and relation representations, and adopt a feature purification module, where the features are enhanced by a feature purification module that removes feature information irrelevant to the joint entity and relation extraction while retaining feature information of relevance. In addition, we adopt a single-stage joint entity and relation extraction module to address the issue of overlapping triples. This module aims to help improve the effectiveness of joint entity and relation extraction. Comprehensive experiments are conducted on two widely-used datasets, and the experimental results demonstrate that the proposed method is effective and outperforms the state-of-the-art baselines. © 2023 IEEE.

关键词： Feature Purification Joint Entity and Relation Extraction Knowledge Enhancement

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Novel soft robotic finger model driven by electrohydrodynamic(EHD)pump

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Journal of Zhejiang University-science A(Applied Physics & engineering) 2024年第7期25卷 596-604页

作者： Xuehang BAI Yanhong PENG Dongze LI Zhuochao LIU Zebing MAO Department of Mechanical Automotive and Materials EngineeringFaculty of EngineeringUniversity of WindsorWindsorN9B 3P4Canada Department of Information and Communication Engineering Graduate School of EngineeringNagoya UniversityNagoya464-8601Japan Department of Intelligent Science and Technology College of Computer Science and TechnologyQingdao UniversityQingdao266071China Design and Research Institute of University of Science and Technology Beijing Co.Ltd. Beijing100083China Faculty of Engineering Yamaguchi UniversityYamaguchi755-8611Japan

1Introduction In the field of robotic-human interactions,soft robotics offers enhanced safety and adaptability.A major challenge in this area is the integration of soft actuators with pump systems,which often increases the system volume and *** study presents the development and testing of a robotic finger powered by electrohydrodynamic(EHD)*** leveraging the electric field-induced flow of dielectric fluids.

关键词： robotic complexity. pump

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Dataset of Large Gathering Images for Person Identification and Tracking

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computers, Materials & Continua 2023年第3期74卷 6065-6080页

作者： Adnan Nadeem Amir Mehmood Kashif Rizwan Muhammad Ashraf Nauman Qadeer Ali Alzahrani Qammer H.Abbasi Fazal Noor Majed Alhaisoni Nadeem Mahmood Faculty of Computer and Information System Islamic University of MadinahMadinah42351Saudi Arabia Department of Computer Science and Information Technology Sir Syed University of Engineering and TechnologyKarachi75300Pakistan Department of Computer Science Federal Urdu University of ArtsScience&TechnologyIslamabad45570Pakistan Department of Physics Federal Urdu University of ArtsScience&TechnologyKarachi75300Pakistan James Watt School of Engineering University of GlasgowGlasgowG128QQUK Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science University of KarachiKarachi75270Pakistan

This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed on the premises of Masjid Al-Nabvi,Madinah,Saudi *** dataset consists of raw and processed images reflecting a highly challenging and unconstraint *** methodology for building the dataset consists of four core phases;that include acquisition of videos,extraction of frames,localization of face regions,and cropping and resizing of detected face *** raw images in the dataset consist of a total of 4613 frames obtained fromvideo *** processed images in the dataset consist of the face regions of 250 persons extracted from raw data images to ensure the authenticity of the presented *** dataset further consists of 8 images corresponding to each of the 250 subjects(persons)for a total of 2000 *** portrays a highly unconstrained and challenging environment with human faces of varying sizes and pixel quality(resolution).Since the face regions in video sequences are severely degraded due to various unavoidable factors,it can be used as a benchmark to test and evaluate face detection and recognition algorithms for research *** have also gathered and displayed records of the presence of subjects who appear in presented frames;in a temporal *** can also be used as a temporal benchmark for tracking,finding persons,activity monitoring,and crowd counting in large crowd scenarios.

关键词： Large crowd gatherings a dataset of large crowd images highly uncontrolled environment tracking missing persons face recognition activity monitoring

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Revolutionizing Malware Detection: Feature-Based Approach for Targeting Diverse Malware Categories 56

Revolutionizing Malware Detection: Feature-Based Approach fo...

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56th International Carnahan Conference on Security technology, ICCST 2023

作者： Jain, Sanyam Thaseen, Sumaiya School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350315875

In today's digital landscape, the task of identifying various types of malicious files has become progressively challenging. Modern malware exhibits increasing sophistication, often evading conventional anti-malware solutions. The scarcity of data on distinct and novel malware strains further complicates effective detection. In response, this research presents an innovative approach to malware detection, specifically targeting multiple distinct categories of malicious software. In the initial stage, Principal Component Analysis (PCA) is performed and achieved a remarkable accuracy rate of 95.39%. Our methodology revolves around leveraging features commonly accessible from user-uploaded files, aligning with the contextual behavior of typical users seeking to identify malignancy. This underscores the efficacy of the unique feature-based detection strategy and its potential to enhance contemporary malware identification methodologies. The outcomes achieved attest to the significance of addressing emerging malware threats through inventive analytical paradigms. © 2023 IEEE.

关键词： Malware

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Polar Region Climate Dynamics: Deep Learning and Remote Sensing Integration for Monitoring Arctic and Antarctic Changes

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 582-595页

作者： Maniraj, Subramanian Pitchiah Rose, Jesuharan Dafni Arunachalam, Rohini Rangasamy, Kotteeswaran Patil, Vishal Ratansing Kathirvelu, Selvakumarasamy Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Computer Science and Engineering Anil Neerukonda Institute of Technology and Sciences Andhra Pradesh Visakhapatnam India Joseph’s College of Engineering Chennai 600119 India Department of CSE(AIML) Vishwakarma Institute of Technology Pune India Department of Electronics and Communication Engineering Saveetha School of Engineering (SIMATS) Chennai India

The polar regions of the Earth, specifically the Arctic and Antarctic, play a pivotal role in regulating the planet’s climate systems. These regions are highly sensitive to climate change, with observable shifts such as diminishing sea ice, retreating glaciers, and rising temperatures. Understanding these dynamics is essential for predicting broader global climate impacts. This paper presents a novel approach for monitoring and predicting these changes by integrating an attention-based transformer model with a 3D convolutional neural network (3D-CNN). The proposed model is designed to capture both the spatial features of satellite imagery and the temporal dynamics of climate data, offering enhanced predictive capabilities for key climate variables such as sea ice extent, glacier movement, and temperature variations. The dataset was split into 80% training and 20% testing with tenfold cross-validation, and the model was trained using the Adam optimizer. The results show that the proposed model achieved a remarkable accuracy of 97.43%, outperforming traditional CNN and RNN models. The model also demonstrated a precision of 98.12% for sea ice extent prediction, a 96.45% precision for glacier movement detection, and a mean squared error (MSE) of 0.35 °C for temperature predictions. These findings suggest that deep learning models, specifically the integration of transformer and 3D-CNN architectures, can significantly contribute to polar region climate monitoring, offering improved tools for scientific research and policymaking. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. corrected publication 2024.

关键词： 3D convolutional neural network Attention-based transformer Climate dynamics Deep learning Glacier movement Polar region Remote sensing Sea ice extent Spatial–temporal analysis Temperature prediction

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Design of artificial biomimetic channels with Na^(+)permeation rate and selectivity potentially outperforming the natural sodium channel

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Nano Research 2024年第9期17卷 8638-8646页

作者： Zhi Zhu Yan Zhao Chao Chang Shaojian Yan Tingyu Sun Shiyu Gu Yangmei Li Dengsong Zhang Chunlei Wang Xiao Cheng Zeng School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Innovation Laboratory of Terahertz Biophysics National Innovation Institute of Defense TechnologyBeijing 100071China School of Physics Peking UniversityBeijing 100871China International Joint Laboratory of Catalytic Chemistry College of SciencesShanghai UniversityShanghai 200444China Department of Materials Science and Engineering City University of Hong KongHong Kong 999077China

Artificial ion channels that enable high-efficiency ion transport have important implications in nanofluidics and biomedical applications such as drug ***,we show a simulation-based chemical design of a biomimetic sodium channel that possesses permeation rate and selectivity potentially higher than those of the state-of-the-art natural vertebrate voltage-gated sodium ***,our theoretical findings have undergone empirical testing,aligning well with the Arrhenius law as derived from a diverse range of experimental *** high-efficiency ion transport is achieved by anchoring the carboxylate functional groups within the channel filter.A key chemical guiding principle underlying the ion channel design is that the free-energy barrier for the Na^(+)passage across the channel should be comparable to typical thermal energy at room *** the implementation of the chemical design,we found that the relatively low free-energy barrier can be attributed to the compensation effect of the carboxylate groups to the partially lost oxygen shell of the ion within the ion channel,as well as to the consonant vibration of the ions inside and outside the *** mechanistic understanding brings new insight,at the molecular level,into the high-efficiency ion transport across the designed membrane *** proof of principle achieved from the simulations will stimulate future experimental confirmation and potential applications of the high-performance artificial channels in nanofluidics and in bioinspired iontronics.

关键词： sodium channels anchoring carboxylate functional groups molecular dynamics simulations low free-energy barrier for ion passage thermal kinetic energy

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