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Combining neural network-based method with heuristic policy for optimal task scheduling in hierarchical edge cloud

Digital Communications and Networks 2023年第3期9卷 688-697页

作者： Zhuo Chen Peihong Wei Yan Li College of Computer Science and Engineering Chongqing University of TechnologyChongqing200433China School of Artificial Intelligence Chongqing University of TechnologyChongqing200433China

Deploying service nodes hierarchically at the edge of the network can effectively improve the service quality of offloaded task requests and increase the utilization of *** this paper,we study the task scheduling problem in the hierarchically deployed edge *** first formulate the minimization of the service time of scheduled tasks in edge cloud as a combinatorial optimization problem,blue and then prove the NP-hardness of the *** from the existing work that mostly designs heuristic approximation-based algorithms or policies to make scheduling decision,we propose a newly designed scheduling policy,named Joint Neural Network and Heuristic Scheduling(JNNHSP),which combines a neural network-based method with a heuristic based *** takes the Sequence-to-Sequence(Seq2Seq)model trained by Reinforcement Learning(RL)as the primary policy and adopts the heuristic algorithm as the auxiliary policy to obtain the scheduling solution,thereby achieving a good balance between the quality and the efficiency of the scheduling ***-depth experiments show that compared with a variety of related policies and optimization solvers,JNNHSP can achieve better performance in terms of scheduling error ratio,the degree to which the policy is affected by re-sources limitations,average service latency,and execution efficiency in a typical hierarchical edge cloud.

关键词： Edge cloud Task scheduling Neural network Reinforcement learning

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3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsule for Medical Image Segmentation 27th

3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsul...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Gan, Dongwei Chang, Ming Chen, Juan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9783031781858

Medical image segmentation (MIS) aims to finely segment various organs. It requires grasping global information from both parts and the entire image for better segmenting, and clinically there are often certain requirements for segmentation efficiency. Convolutional neural networks (CNNs) have made considerable achievements in MIS. However, they are difficult to fully collect global context information and their pooling layer may cause information loss. Capsule networks, which combine the benefits of CNNs while taking into account additional information such as relative location that CNNs do not, have lately demonstrated some advantages in MIS. Vision Transformer (ViT) employs transformers in visual tasks. Transformer based on attention mechanism has excellent global inductive modeling capabilities and is expected to capture long-range information. Moreover, there have been resent studies on making ViT more lightweight to minimize model complexity and increase efficiency. In this paper, we propose a U-shaped 3D encoder-decoder network named 3D-EffiViTCaps, which combines 3D capsule blocks with 3D EfficientViT blocks for MIS. Our encoder uses capsule blocks and EfficientViT blocks to jointly capture local and global semantic information more effectively and efficiently with less information loss, while the decoder employs CNN blocks and EfficientViT blocks to catch finer details for segmentation. We conduct experiments on various datasets, including iSeg-2017, Hippocampus and Cardiac to verify the performance and efficiency of 3D-EffiViTCaps, which performs better than previous 3D CNN-based, 3D Capsule-based and 3D Transformer-based models. We further implement a series of ablation experiments on the main blocks. Our code is available at: https://***/HidNeuron/3D-EffiViTCaps. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decoding

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Exploring the Microcosm: A Comprehensive Survey of Micro Sensor Applications Across Multidisciplinary Research

Exploring the Microcosm: A Comprehensive Survey of Micro ...

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International Conference on Power engineering and Intelligent Systems, PEIS 2024

作者： Nandhini, R. Radha, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819767090

The main aim of this survey is to provide wider insight about the use of MEMS (Micro-Electro-Mechanical-System) technology in various interdisciplinary fields. The areas include IoT (Internet-Of-Things) for smart automations, agricultural field for performing precision farming. Automotive sector to predict failures, environmental aspects to detect factors affecting ecosystem, food control to measure the quality. Healthcare for identifying the disease in prior stages, robotics to carry out multiple operations. Machine Learning algorithms play vital role in classifying and extracting the necessary feature from the raw data. Deep Learning techniques like ANN (Artificial Neural Network) is employed for estimation of optimal input and output parameters. CNN (Convolutional Neural Network) for representing the designed structure in binary pixelated image form. The various types of sensors, energy harvesters, transducers and actuators which are designed using MEMS technology and their predominant role in recent advancements has been discussed in brief. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automotive industry

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Double-Click: dual-click iteration and dual-branch networks for interactive segmentation 16

Double-Click: dual-click iteration and dual-branch networks ...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Jiang, Mingcheng Wang, Di Ren, Benzhe School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The main aim of Interactive image segmentation is to accurately segment regions of interest by utilizing simple user-provided interactive information. Currently, the most popular training approach involves using the previous segmentation result as supplementary information for the next segmentation process to establish a connection between the two interactions. This iterative training method transfers all the information carried by interactions to the global level, without considering its impact at the local level. This will affect the final segmentation results and the training efficiency. To address this problem, we propose an iterative training strategy called Double-Click to involve cropping the image based on the newly added clicks and then separately feeding the cropped image and the newly added clicks into a smaller branch network to obtain predictions for the local region. Subsequently, the locally segmented result is fused with the previous segmentation result to achieve the final outcome. Then, the refined segmentation results are used to guide the next segmentation, ensuring that more precise results guide subsequent segmentations while maintaining the guidance information of the clicks on the global image. Our training strategy improves the training effectiveness by handling two clicks in an iterative process. The accuracy of the segmentation is ensured through both local and global predictions. Additionally, we designed a dual-branch network to accommodate our proposed training strategy. The effectiveness of this strategy has been tested on four validated on four benchmarks, namely GrabCut, Berkeley, SBD, and DAVIS. Experimental results demonstrate that the Double-Click method achieves better performance with fewer interactions compared to previous methods. © 2025 SPIE.

关键词： Semantic Segmentation

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An Initial Risk Assessment for Multimodal with LSTM-Based Trust Evaluation Framework for Autonomous Vehicle Security

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SN computer science 2025年第2期6卷 1-15页

作者： Renjith, P.N. Balasubramani, S. Ramesh, K. Patnala, Eswar School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

This research aims to develop a new approach to increase the safety and reliability of Autonomous Vehicle (AV) through the proposed risk assessment framework, supported by the trust evaluation approach derived from a machine learning algorithm. This work proposes a comprehensive risk assessment model with real-time sensor data, Vehicle-to-Vehicle (V2V), key communication reliability, environmental cognition, and Improved Long Short-Term Memory (I-LSTM). This framework is multidimensional compared to most conventional models, focusing primarily on assessing individual threat factors. Here, the machine learning portion is designed to evaluate and predict trust by quickly processing vast amounts of information from sensors, signals, and prior operation history. Such an approach to trust assessment enables the system to continuously evaluate the dependability of automated vehicle operation and identify threats to security with improved accuracy. The effectiveness of the performance of this method is substantiated by a number of stand-alone and live scenarios, where the model's performance is higher in perceiving possible risks, avoiding threats, and enhancing safety than the conventional approaches. The findings suggest that the safety regulations of the AVs will be improved by using a multi-dimensional risk assessment model and integrating the I-LSTM trust rating with 95.5% accuracy, 96.7% risk identification, and 180 ms response time. This will cause AV and its surrounding environment to be accepted by the public and used on the roads. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autonomous vehicles LSTM model Machine learning Risk assessment Trust evaluation

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Structure-Sensitive Graph Dictionary Embedding for Graph Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2962-2972页

作者： Liu, Guangbu Zhang, Tong Wang, Xudong Zhao, Wenting Zhou, Chuanwei Cui, Zhen Nanjing University of Science and Technology Key Laboratory of Intelligent Perception and Systems for High-Dimensional School of Computer Science and Engineering Nanjing210094 China

Graph structure expression plays a vital role in distinguishing various graphs. In this work, we propose a structure-sensitive graph dictionary embedding (SS-GDE) framework to transform input graphs into the embedding space of a graph dictionary for the graph classification task. Instead of a plain use of a base graph dictionary, we propose the variational graph dictionary adaptation (VGDA) to generate a personalized dictionary (named adapted graph dictionary) for catering to each input graph. In particular, for the adaptation, the Bernoulli sampling is introduced to adjust substructures of base graph keys according to each input, which increases the expression capacity of the base dictionary tremendously. To make cross-graph measurement sensitive as well as stable, multisensitivity Wasserstein encoding is proposed to produce the embeddings by designing multiscale attention on optimal transport. To optimize the framework, we introduce mutual information as the objective, which further deduces variational inference of the adapted graph dictionary. We perform our SS-GDE on multiple datasets of graph classification, and the experimental results demonstrate the effectiveness and superiority over the state-of-the-art methods. © 2020 IEEE.

关键词： Embeddings

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A novel large kernel two-branch semantic segmentation network 24

A novel large kernel two-branch semantic segmentation networ...

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4th International Conference on Signal Processing and Communication technology, SPCT 2024

作者： Zhang, Xinrui Fu, Peng School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400710636

For semantic branching in a two-branch network structure, it is crucial to quickly improve the feeling field, in addition, the feature fusion interaction of two-branching needs to take into account the structural and multi-scale information fusion of features under different feeling fields. In this paper, in order to cope with this challenge, we propose to accumulate the receptive field in dense convolution using separable large kernel convolution through an adaptive semantic extraction module, and design a selectable large kernel attention module for the problem of mismatch of receptive field of feature information. Finally, this paper designs a multiscale feature extraction module that fuses local and global information, and uses large kernel dense separable convolution in the module with the ability to extract high-dimensional semantics serially and capture multiscale in parallel. © 2024 Copyright held by the owner/author(s).

关键词： Semantic Segmentation

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An EETR Approach for Therapeutic Response Prediction Using Gene Expression and Drug Properties 3rd

An EETR Approach for Therapeutic Response Prediction Using G...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Selvi Rajendran, P. Jawahar, Janiel Computer Science and Engineering School of Computing Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9789819726707

In recent years, the role of computational methods such as machine learning and deep learning has evolved to help better understand an individual’s response to drugs. Through advancements in the discipline of precision medicine, cancer therapies are made based on an individual's pharmacogenomics data. In this way, doctors can make informed decisions about patient outcomes and reduce treatment costs. Beyond these advancements, there is enough void to precisely predict drug response. A drug’s response to therapy is dependent on a number of factors such as the drug’s physicochemical properties, pharmacokinetics, metabolism, protein, mutation, somatic variation, environment, and much more. Studying the correlation of more of these factors and their association with the drug is one of the promising ways to bridge the gap in improving results. Extra tree regression is an ensemble-based algorithm and is less prone to overfitting than other ensemble learning algorithms particularly when low bias and reduced overfitting are desired, and computational efficiency is important. In this paper, we have applied the drug’s physicochemical properties to predict the Inhibition values using an enhanced extra tree regression (EETR) algorithm. The EETR algorithm prediction outperformed both the random forest and original ETR algorithm in terms of both MSE and R2 score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Enhancement of charging performance of quantum battery via quantum coherence of bath

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Chinese Physics B 2023年第1期32卷 161-171页

作者：于文莉张允李海魏广芬韩丽萍田峰邹建 School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China School of Science Tianjin University of TechnologyTianjin 300384China School of Physics Beijing Institute of TechnologyBeijing 100081China

An open quantum battery(QB)model of a single qubit system charging in a coherent auxiliary bath(CAB)consisting of a series of independent coherent ancillae is *** to the collision charging protocol we derive a quantum master equation and obtain the analytical solution of QB in a steady *** find that the full charging capacity(or the maximal extractable work(MEW))of QB,in the weak QB-ancilla coupling limit,is positively correlated with the coherence magnitude of *** with the numerical simulations we compare with the charging properties of QB at finite coupling strength,such as the MEW,average charging power and the charging efficiency,when considering the bath to be a thermal auxiliary bath(TAB)and a CAB,*** find that when the QB with CAB,in the weak coupling regime,is in fully charging,both its capacity and charging efficiency can go beyond its classical counterpart,and they increase with the increase of coherence magnitude of *** addition,the MEW of QB in the regime of relative strong coupling and strong coherent magnitude shows the oscillatory behavior with the charging time increasing,and the first peak value can even be larger than the full charging MEW of *** also leads to a much larger average charging power than that of QB with TAB in a short-time charging *** features suggest that with the help of quantum coherence of CAB it becomes feasible to switch the charging schemes between the long-time slow charging protocol with large capacity and high efficiency and the short-time rapid charging protocol with highly charging power only by adjusting the coupling strength of *** work clearly demonstrates that the quantum coherence of bath can not only serve as the role of“fuel”of QB to be utilized to improve the QB's charging performance but also provide an alternative way to integrate the different charging protocols into a single QB.

关键词： quantum battery quantum coherence maximal extractable work charging power

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A Penalty Groups-Assisted Iterated Greedy Integrating Idle Time Insertion:Solving the Hybrid Flow Shop Group Scheduling with Delivery Time Windows

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Complex System Modeling and Simulation 2024年第2期4卷 137-165页

作者： Qianhui Ji Yuyan Han Yuting Wang Biao Zhang Kaizhou Gao the School of Computer Science Liaocheng UniversityLiaocheng 252059China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

The hybrid flow shop group scheduling problem(HFGSP)with the delivery time windows has been widely studied owing to its better flexibility and suitability for the current just-in-time production ***,there are several unresolved challenges in problem modeling and algorithmic design tailored for *** our study,we place emphasis on the constraint of ***,this paper first constructs a mixed integer linear programming model of HFGSP with sequence-dependent setup time and delivery time windows to minimize the total weighted earliness and tardiness(TWET).Then a penalty groups-assisted iterated greedy integrating idle time insertion(PG IG ITI)is proposed to solve the above *** the PG IG ITI,a double decoding strategy is proposed based on the earliest available machine rule and the idle time insertion rule to calculate the TWET ***,to reduce the amount of computation,a skip-based destruction and reconstruction strategy is designed,and a penalty groups-assisted local search is proposed to further improve the quality of the solution by disturbing the penalized groups,i.e.,early and tardy ***,through comprehensive statistical experiments on 270 test instances,the results prove that the proposed algorithm is effective compared to four state-of-the-art algorithms.

关键词： hybrid flow shop group scheduling iterated greedy algorithm delivery time windows sequence-dependent setup time

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