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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Xiangjiang Wu Xuanya Li Kai Hu Zhineng Chen Xieping Gao Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China Baidu Inc. Beijing China Key Laboratory of Medical Imaging and Artifical Intelligence of Hunan Province Xiangnan University Chenzhou China School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9781728176055;9781728176062

Accurate and automatic gland segmentation can help pathologists diagnose the malignancy of colorectal cancers. However, it remains a challenging task because of the large morphological differences between the glands and the presence of sticky glands. In this paper, a hybrid feature enhancement network (HFE-Net) for glandular segmentation is proposed, which includes a multi-scale local feature extraction block (MSLFEB) and a global feature enhancement block (GFEB). Specifically, the MSLFEB is used to extract multiscale features through different sizes of the receptive field to reduce the loss of the local information and effectively alleviate glandular adhesion. The GFEB is used to transfer the underlying features to the decoder by considering the global semantic information. Furthermore, we design a focal and variance (FV) loss function to alleviate the class imbalance and constraint the pixels within the same instance. Finally, we evaluate the proposed method on the 2015 MICCAI GlaS challenge dataset and the CRAG colorectal adenocarcinoma dataset. The results show that our HFE-Net can achieve competitive results with fewer computing resources when compared with the state-of-the-art gland segmentation methods.

关键词： Image segmentation Histopathology Semantics Glands Signal processing Feature extraction Data mining

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Distributed resource scheduling for large-scale MEC systems: A multi-agent ensemble deep reinforcement learning with imitation acceleration

arXiv

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arXiv 2020年

作者： Jiang, Feibo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system. To address this problem, we propose a distributed intelligent resource scheduling (DIRS) framework, which includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. More specifically, we first introduce a novel multi-agent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Secondly, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Lvy flight search. Finally, an imitation acceleration scheme is presented to pre-train all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. Extensive simulations are conducted to demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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A Security Trust Mechanism for Data Collection with Mobile Vehicles in Smart City

A Security Trust Mechanism for Data Collection with Mobile V...

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IEEE International Conference on Communications in China (ICCC)

作者： Qingyong Deng Shaobo Huang Shujuan Tian Haolin Liu Jianglian Cao Shuwen Jia Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Xiangtan China Hunan International Scientific and Technological Cooperation Base of Intelligent network Xiangtan University Xiangtan China Electronic Information and Control of Fujian University Engineering Research Center Minjiang University Saxo Fintech business school University of Sanya Hainan China

ISBN: (数字)9781728173276

ISBN: (纸本)9781728173283

Smart city includes all kinds of advanced technologies and solutions, and its maintenance is achieved through massive data collection. In Delay Tolerant network (DTN), sensors that lack connectivity can store data temporarily and wait for the mobile vehicles in the city to forward, which significantly improves the efficiency of data collection in the city. However, mobile vehicles are not always credible, since the attack from malicious vehicles may be detrimental to data quality. Therefore, there is an urgent need for a secure data collection strategy with mobile vehicles. In this paper, a consistency trust verification strategy for mobile vehicles (CTV-MV) is proposed, including three stages: opportunistic routing, recruitment, and trust verification. Specifically, an average-distance-based outlier detection (ADOD) algorithm is designed to verify heterogeneous data. Then a baseline mechanism is used to trust reasoning for evaluating the trust value. Furthermore, a recruitment strategy that considers trust and data coverage ratio is proposed to maximize data quality. Finally, the performance of CTV-MV is analyzed through experiments in terms of the excellent ratio of data and recruit cost, respectively.

关键词： Smart cities Data integrity Data collection Routing Mobile nodes Security Recruitment

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Adversarial Purification with One-Step Guided Diffusion Model

SSRN

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SSRN 2024年

作者： Li, Yanchun Li, Zemin Hu, Lingzhi Zeng, Li Shen, Dongsu School of Computer Science Xiangtan University Xiangtan411105 China Hunan International Scientific and Technological Cooperation Base of Intelligent network Xiangtan411105 China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan411105 China School of Mathematics and Computer Science Xiangtan University Xiangtan University Xiangtan411105 China Network and Information Center Xiangtan University Xiangtan411105 China

Recently, the employment of diffusion models for adversarial purification has attracted the attention due to their strong generalization ability towards defensive adversarial examples. However, the multi-step sampling required for diffusion model results in time and resource consumption. To address these issues, we propose a novel One-Step Guided Diffusion Model (OSGD) for efficient adversarial purification. OSGD combines a one-step denoising process with a guiding strategy to accelerate sampling speed and achieve superior purification results. Specifically, OSGD utilizes a diffusion model for two rounds of one-step denoising, with the preliminary image obtained in the first round serving as the guidance signal for the second round. In the second round, the adversarial images are purified under the guidance of preliminary images to eliminate adversarial perturbations. We analysis the rationality of using preliminary denoised images as guidance signals and verify the effectiveness of this guidance strategy through experiments. Extensive experiments on Cifar10 and ImageNet using three attack methods including PGD, AutoAttack and BPDA+EOT demonstrate that our method achieves the state-of-the-art performance in terms of accuracy and efficiency. Source code for this work is available at https://***/zmlix/***. © 2024, The Authors. All rights reserved.

关键词： Purification

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Multimode optomechanical cooling via general dark-mode control

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Physical Review A 2022年第1期106卷 013526-013526页

作者： Jian Huang Deng-Gao Lai Cheng Liu Jin-Feng Huang Franco Nori Jie-Qiao Liao Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China Theoretical Quantum Physics Laboratory RIKEN Cluster for Pioneering Research Wako-shi Saitama 351-0198 Japan RIKEN Center for Quantum Computing 2-1 Hirosawa Wako-shi Saitama 351-0198 Japan Physics Department University of Michigan Ann Arbor Michigan 48109-1040 USA

The dark-mode effect is a stubborn obstacle for ground-state cooling of multiple degenerate mechanical modes optomechanically coupled to a common cavity-field mode. Here we propose an auxiliary-cavity-mode method for simultaneous ground-state cooling of two degenerate or near-degenerate mechanical modes by breaking the dark mode. We find that the introduction of the auxiliary cavity mode not only breaks the dark-mode effect, but also provides a new cooling channel to extract the thermal excitations stored in the dark mode. Moreover, we study the general physical-coupling configurations for breaking the dark mode in a generalized network-coupled four-mode optomechanical system consisting of two cavity modes and two mechanical modes. We find the analytical dark-mode-breaking condition in this system. This method is general and it can be generalized to break the dark-mode effect and to realize the simultaneous ground-state cooling in a multiple-mechanical-mode optomechanical system. We also demonstrate the physical mechanism behind the dark-mode breaking by studying the breaking of dark-state effect in the N−type four-level atomic system. Our results not only provide a general method to control various dark-mode and dark-state effects in physics, but also present an opportunity to the study of macroscopic quantum phenomena and applications in multiple-mechanical-resonator systems.

关键词： Optomechanics Quantum optics

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Stacking-Based Attention Temporal Convolutional network for Action Segmentation

Stacking-Based Attention Temporal Convolutional Network for ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Liu Yang Yu Jiang Junkun Hong Zhenjie Wu Zhan Yang Jun Long School of Computer Science and Engineering Central South University China Big Data Institute Central South University Central South University China Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province Central South University Changsha China

Action segmentation plays an important role in video understanding, which is implemented by frame-wise action classification. Recent works on action segmentation capture long-term dependencies by increasing temporal convolution layers in Temporal Convolution networks (TCNs). However, high layers in TCNs are more coarse access to video features, resulting in the loss of fine-grained information for frame-wise action classification. To address the above issues, we propose a novel Attention-based Temporal Convolution (ATC) block to capture fine-grained information of temporal dependencies for frame-wise action classification by self-attention mechanism. Via stacking ATC blocks, we design a Stacking-based Attention Temporal Convolutional network (SATC) to adaptively capture long-term and short-term dependencies, according to the semantic similarity of features on different temporal receptive fields simultaneously. The experimental results demonstrate that our SATC outperforms other baselines on all three challenging datasets: GTEA, 50Salads and Breakfast.

关键词： Convolution Stacking Semantics Acoustics Convolutional neural networks Speech processing

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A Reinforcement Learning-Based Virtual Machine Placement Strategy in Cloud Data Centers

A Reinforcement Learning-Based Virtual Machine Placement Str...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Saiqin Long Zhetao Li Yun Xing Shujuan Tian Dongsheng Li Rong Yu Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science Xiangtan University Xiangtan China School of Computer Science Xiangtan University Xiangtan China College of Computer National University of Defense Technology Changsha China School of Automation Guangdong University of Technology Guangzhou China

ISBN: (纸本)9781728176505

With the widespread use of cloud computing, energy consumption of cloud data centers is increasing which mainly comes from IT equipment and cooling equipment. This paper argues that once the number of virtual machines on the physical machines reaches a certain level, resource competition occurs, resulting in a performance loss of the virtual machines. Unlike most papers, we do not impose placement constraints on virtual machines by giving a CPU cap to achieve the purpose of energy savings in cloud data centers. Instead, we use the measure of performance loss to weigh. We propose a reinforcement learning-based virtual machine placement strategy(RLVMP) for energy savings in cloud data centers. The strategy considers the weight of virtual machine performance loss and energy consumption, which is finally solved with the greedy strategy. Simulation experiments show that our strategy has a certain improvement in energy savings compared with the other algorithms.

关键词： Weight measurement Cloud computing Data centers Energy consumption NP-hard problem High performance computing Virtual machining

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Stacked auto encoder based Deep Reinforcement Learning for online resource scheduling in large-scale MEC networks

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Saiqin Long Zhetao Li Zihao Liu Qingyong Deng Sangyoon Oh Nobuyoshi Komuro Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science Xiangtan University Xiangtan China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) National University of Defense Technology Changsha China Ajou University Suwon South Korea Graduate School of Advanced Integration Science Chiba University Chiba Japan

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.

关键词： Cloud computing Clustering algorithms Estimation Fingerprint recognition Throughput Partitioning algorithms Classification algorithms

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Ai driven heterogeneous MEC system for dynamic environment - Challenges and solutions

arXiv

引用

arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

—By taking full advantage of computing, Communication and Caching (3C) resources at the network edge, Mobile Edge computing (MEC) is envisioned as one of the key enablers for the next generation network and services. However, current fixed-location MEC architecture may not be able to make real-time decision in dynamic environment, especially in large-scale scenarios. To address this issue, in this paper, a Heterogeneous MEC (H-MEC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MEC, e.g., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions are discussed in this paper. In addition, an AI-based joint Resource schEduling (ARE) framework is proposed, with simulation results shown to verify the efficiency of our proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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