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U-shaped feature extractor used on mask R-CNN for cell nuclei image segmentation 6

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U-shaped feature extractor used on mask R-CNN for cell nucle...

6th Annual International Conference on Network and Information Systems for Computers, ICNISC 2020

作者： Zhu, Qing Huang, Xiaoyang Wang, Liansheng Li, Jonathan Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University XiamenFJ 361005 China Department of Geography and Environmental Management Department of Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

The cell nuclei segmentation of is a challenging task in microscopy image analysis. The problems of noise, small cell nuclei, and few training data samples in the data set will all affect the effectiveness of the model to varying degrees. This paper presents a new approach to nuclear image segmentation based on convolutional neural networks. Our approach is based on Mask R-CNN with some modification, which combines low-level semantic features for model training. In order to make the model specific to each low-level feature map, the attention mechanism was used to assign weights to each low-level feature map, making the model learning more purposeful. Our method achieves an average precision value of 62.8%, which is 2.7% higher than that of the Mask R-CNN (ResNet50) basic model and 6.3% of the Mask R-CNN (ResNet101) basic model. © 2020 Journal of Physics: Conference Series.

关键词： Convolutional neural networks

SuperMeshing: A new deep learning architecture for increasing the mesh density of metal forming stress field with attention mechanism and perceptual features

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arXiv 2021年

作者： Xu, Qingfeng Nie, Zhenguo Xu, Handing Zhou, Haosu Liu, Xinjun Tsinghua University Beijing100084 China Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control Tsinghua University Beijing100084 China School of Computing and Information Systems Melbourne School of Engineering University of Melbourne Melbourne3000 Australia School of Mechatronical Engineering Beijing Institute of Technology Beijing100084 China Dyson School of Design Engineering Imperial College London Exhibition Rd LondonSW7 2AZ United Kingdom

In stress field analysis, the finite element analysis is a crucial approach, in which the mesh-density has a significant impact on the results. High mesh density usually contributes authentic to simulation results but costs more computing resources, leading to curtailing efficiency during the design process. To eliminate this drawback, we propose a new data-driven mesh-density boost model named SuperMeshingNet that strengthens the advantages of finite element analysis (FEA) with low mesh-density as inputs to the deep learning model, which consisting of Res-UNet architecture, to acquire high-density stress field instantaneously, shortening computing time and cost automatically. Moreover, the attention mechanism and the perceptual features are utilized, enhancing the performance of SuperMeshingNet. Compared to the baseline that applied the linear interpolation method, SuperMeshingNet achieves a prominent reduction in the mean squared error (MSE) and mean absolute error (MAE) on test data, which contains prior unseen cases. Based on the data set of metal forming, the comparable experiments are proceeded to demonstrate the high quality and superior precision of the reconstructed results generated by our model. The well-trained model can successfully show more excellent performance than the baseline and other methods on the multiple scaled mesh-density, including 2×, 4×, and 8×. With the refined result owning broaden scaling of mesh density and high precision, the FEA process can be accelerated with seldom cost on computation resources. We publicly share our work with full detail of implementation at https://***/zhenguonie/*** Codes 14J60 (Primary) 14F05, 14J26 (Secondary) Copyright © 2021, The Authors. All rights reserved.

关键词： Finite element method

Hybrid Reinforcement Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum computing

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arXiv 2025年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Chen, Mingzhe Kim, Dong In Shen, Xuemin College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Exploiting quantum computing at the mobile edge holds immense potential for facilitating large-scale network design, processing multimodal data, optimizing resource management, and enhancing network security. In this paper, we propose a pioneering paradigm of mobile edge quantum computing (MEQC) that integrates quantum computing capabilities into classical edge computing servers that are proximate to mobile devices. To conceptualize the MEQC, we first design an MEQC system, where mobile devices can offload classical and quantum computation tasks to edge servers equipped with classical and quantum computers. We then formulate the hybrid classical-quantum computation offloading problem whose goal is to minimize system cost in terms of latency and energy consumption. To solve the offloading problem efficiently, we propose a hybrid discrete-continuous multi-agent reinforcement learning algorithm to learn long-term sustainable offloading and partitioning strategies. Finally, numerical results demonstrate that the proposed algorithm can reduce the MEQC system cost by up to 30% compared to existing baselines. © 2025, CC BY.

关键词： Qubits

Joint Compression and Deadline Optimization for Wireless Federated Learning

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arXiv 2023年

作者： Zhang, Maojun Li, Yang Liu, Dongzhu Jin, Richeng Zhu, Guangxu Zhong, Caijun Quek, Tony Q.S. The College of information Science and Electronic Engineering Zhejiang University Hangzhou China China Academy of Information and Communications Technology Beijing China School of Computing Science University of Glasgow United Kingdom The Department of Information and Communication Engineering Zhejiang University Hangzhou310007 China The Zhejiang–Singapore Innovation and AI Joint Research Lab Hangzhou310007 China Hangzhou310007 China The Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China The Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Guangzhou510555 China The Singapore University of Technology and Design Singapore487372 Singapore

Federated edge learning (FEEL) is a popular distributed learning framework for privacy-preserving at the edge, in which densely distributed edge devices periodically exchange model-updates with the server to complete the global model training. Due to limited bandwidth and uncertain wireless environment, FEEL may impose heavy burden to the current communication system. In addition, under the common FEEL framework, the server needs to wait for the slowest device to complete the update uploading before starting the aggregation process, leading to the straggler issue that causes prolonged communication time. In this paper, we propose to accelerate FEEL from two aspects: i.e., 1) performing data compression on the edge devices and 2) setting a deadline on the edge server to exclude the straggler devices. However, undesired gradient compression errors and transmission outage are introduced by the aforementioned operations respectively, affecting the convergence of FEEL as well. In view of these practical issues, we formulate a training time minimization problem, with the compression ratio and deadline to be optimized. To this end, an asymptotically unbiased aggregation scheme is first proposed to ensure zero optimality gap after convergence, and the impact of compression error and transmission outage on the overall training time are quantified through convergence analysis. Then, the formulated problem is solved in an alternating manner, based on which, the novel joint compression and deadline optimization (JCDO) algorithm is derived. Numerical experiments for different use cases in FEEL including image classification and autonomous driving show that the proposed method is nearly 30X faster than the vanilla FedSGD algorithm, and outperforms the state-of-the-art schemes. © 2023, CC BY.

关键词： Autonomous vehicles

A molecular device: A DNA molecular lock driven by the nicking enzymes

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Computational and structural biotechnology journal 2020年 18卷 2107-2116页

作者： Xiaokang Zhang Qiang Zhang Yuan Liu Bin Wang Shihua Zhou Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian 116622 China. School of Computer Science and Technology Dalian University of Technology Dalian 116024 China.

As people are placing more and more importance on information security, how to realize the protection of information has become a hotspot of current research. As a security device, DNA molecular locks have great potential to realize information protection at the molecular level. However, building a highly secure molecular lock is still a serious challenge. Therefore, taking advantage of the DNA strand displacement and enzyme control technology, we constructed a molecular lock with a self-destructive mechanism. This molecular lock is mainly composed of logic circuits and takes nicking enzymes as inputs. To build this molecular lock, we first constructed a series of cascade circuits, including a YES-YES cascade circuit and a YES-AND cascade circuit. Then, an Inhibit logic gate was constructed to explore the inhibitory properties between different combinations of two nicking enzymes. Finally, using the characteristics of mutual inhibition between several enzymes, a DNA molecular lock driven by three nicking enzymes was constructed. In this molecular device, only the correct sequence of nicking enzymes can be input to ensure the normal operation of the molecular lock. Once the wrong password is entered, the device will be destroyed and cannot be recovered, which effectively prevents intruders from cracking the lock through exhaustive methods. The molecular lock has the function of simulating an electronic keyboard, which can realize the protection of information at the molecular level, and provides a new implementation method for building more advanced and complex molecular devices.

关键词： DNA Information Security Molecular Lock Nicking Enzyme

Foundation of Z-Numbers and engineering Applications 1

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作者： Razvarz, Sina Hernández-Rodríguez, Felipe Jafari, Raheleh Gegov, Alexander Mexico City07360 Mexico School of Engineering and Science Tecnológico de Monterrey Prol. Juan de la Barrera 1241 Saltillo25270 Mexico School of Design University of Leeds LeedsLS2 9JT United Kingdom School of Computing University of Portsmouth Buckingham Building PortsmouthPO1 3HE United Kingdom

ISBN: (数字)9783030454029

ISBN: (纸本)9783030454012;9783030454043

Decision-making problems in economics, engineering, risk assessment, forecasting, manufacturing, design and process analysis are naturally determined by vagueness as well as partial reliability. Generating a formalism for dealing with Z-numbers is essential to handle these kinds of information. In this paper, the applications of Z-numbers in engineering are studied. The most current researches in the area of Z-numbers are covered in this work. Since some engineers have successfully applied Z-numbers, detailed discussions are supplied to stimulate future investigations. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

Ciphertext-policy attribute-based proxy re-encryption via constrained PRFs

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Science China(Information Sciences) 2021年第6期64卷 242-243页

作者： Zengpeng LI Vishal SHARMA Chunguang MA Chunpeng GE Willy SUSILO College of Computer Science and Technology Qingdao University Guizhou Provincial Key Laboratory of Public Big Data Guizhou University ISTD Pillar Singapore University of Technology and Design (SUTD) College of Computer Science and Engineering Shandong University of Science and Technology College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics School of Computing and Information Technology University of Wollongong

Dear editor,To maintain the confidentiality of the sensitive data, users tend to encrypt their data under an associated access policy(or attributes) before outsourcing them to the *** the traditional access control model via cryptographic approaches, e.g., ciphertext-policy attribute-based encryption(or CP-ABE), users are allowed to formulate access policies and let the encrypted data associated with access policies. Only authenticated users, whose own attributes matching their formulated access policies, have the privilege to recover the ciphertext. However, once the access policies are formulated, the authorized user cannot update it unless the user downloads and re-encrypts the file under the new access policy. Hence, to achieve dynamic access control, the model of ciphertext-policy attribute-based proxy re-encryption(or CP-AB-PRE) is adopted by integrating the proxy re-encryption(or PRE).

关键词： Ciphertext-policy attribute-based proxy re-encryption via constrained PRFs

Social vulnerability and power loss mitigation: A case study of Puerto Rico

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International Journal of Disaster Risk Reduction 2022年 82卷

作者： Boyle, Esther Inanlouganji, Alireza Carvalhaes, Thomaz Jevtić, Petar Pedrielli, Giulia Reddy, T. Agami The School of Mathematical and Statistical Sciences Arizona State University A Wing 901 S. Palm Walk Tempe 85281 AZ United States The School of Computing Informatics and Design System Engineering Arizona State University P.O. Box 878809 Tempe 85287-8809 AZ United States Geospatial Science and Human Security Division Oak Ridge National Laboratory 1 Bethel Valley Road Oak Ridge 37830 TN United States The School of Sustainable Engineering and the Built Environment and The Design School Arizona State University 660 S. College Avenue Tempe Arizona 85281 PO Box 871605 Tempe 85287-1605 AZ United States

The increasing occurrence of extreme weather events urges us to reevaluate the resiliency and vulnerability aspects of our most critical infrastructures — such as power grids — as their failures result in both economic loss and severe human hardship. Seen through the lens of alleviating human suffering, it is crucial to be able to identify critical system components of the infrastructure for targeted hardening given resource constraints. This effort is of particular importance in islanded areas such as Puerto Rico where hurricanes are frequent and resources are limited, and where the spatially diverse effects of power loss on human suffering are all the more severe. Recent studies on evaluating infrastructure networks during extreme weather events have taken a simulation based approach that incorporates a variety of component models, such as weather realizations, topological network models, fragility models, and power flow models to estimate expected loss of service. In this work, we expand such a Component Based Event Simulation (CBES) methodology proposed in the literature and integrate it with a social vulnerability modeling component. This paradigm-advancing approach of synthesizing the cutting edge capability of power network modeling and the social impacts of the power transmission network failure is demonstrated for the island of Puerto Rico. Our work exemplifies the efficacy of this integrated modeling framework in developing a decision metric for targeted transmission line hardening. © 2022 Elsevier Ltd

关键词： Grid hardening Power networks Social vulnerability

Beneath the tip of the iceberg: Current challenges and new directions in sentiment analysis research

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arXiv 2020年

作者： Poria, Soujanya Hazarika, Devamanyu Majumder, Navonil Mihalcea, Rada Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Computing National University of Singapore Singapore Electrical Engineering and Computer Science University of Michigan Michigan United States

Sentiment analysis as a field has come a long way since it was first introduced as a task nearly 20 years ago. It has widespread commercial applications in various domains like marketing, risk management, market research, and politics, to name a few. Given its saturation in specific subtasks - such as sentiment polarity classification - and datasets, there is an underlying perception that this field has reached its maturity. In this article, we discuss this perception by pointing out the shortcomings and under-explored, yet key aspects of this field that are necessary to attain true sentiment understanding. We analyze the significant leaps responsible for its current relevance. Further, we attempt to chart a possible course for this field that covers many overlooked and unanswered questions. Copyright © 2020, The Authors. All rights reserved.

关键词： Sentiment analysis