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作者： Lin, Yuqing Lin, Jiawen Niu, Yuzhen Zhang, Haifeng College of Mathematics and Computer Science Fujian Key Laboratory of Network Computing and Intelligent Information Processing Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou University Xueyuan Road #2 Fuzhou Fujian350106 China

With the diversified development of the digital devices, such as computer, mobile phone and television, how to resize an image or video to adapt to different display screens has been a heated topic. Seam carving does well in image resizing at most times, however, it sometimes produces discontinuity image content or impaired salient objects. Therefore, we propose an accumulative energy-based seam carving method for image resizing. We distribute the energy of each pixel on the seam to its adjacent 8-connected pixels to avoid the extreme concentration of seams. In addition, we add the image saliency and the edge information into the energy function to reduce the distortion. To compute more efficiently, we use parallel computing environment as well. Experimental results show that compared with the existing methods, our method can both avoid the discontinuity of image content and distortions as well as better maintain the shape of the salient objects. © 2020 Inderscience Enterprises Ltd.

关键词： Digital devices

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A Deep Reinforcement Learning Framework for Vehicle Detection and Pose Estimation in 3D Point Clouds 6th

A Deep Reinforcement Learning Framework for Vehicle Detectio...

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6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Wang, Weipeng Luo, Huan Zheng, Quan Wang, Cheng Guo, Wenzhong Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350003 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350003 China Fujian Key Laboratory of Sensing and Computing for Smart City Xiamen University XiamenFJ361005 China

ISBN: (纸本)9783030578800

As for autonomous driving in urban environments, it is of significance to accurately capture the position and pose of vehicles. Those information can assist self-driving system in making right decisions to avoid potential risks. Currently, 3D point clouds captured by laser scanners are widely used in self-driving systems to sense the real environment. Therefore, in this paper, we propose a deep reinforcement learning framework for vehicle detection and pose estimation by using 3D point clouds. Specifically, to estimate the pose of vehicles, we propose to design a rotation action in Deep Q network (DQN). In addition, by considering the whole detection procedure as Markov Decision process (MDP), our intermediate detected results can further improve the detection performance of our proposed method. The evaluations are carried on outdoor point cloud scenes captured by VMX450 laser scanning system. The experimental results demonstrate the satisfied performance on vehicle detection and pose estimation. © 2020, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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DQPS: An intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge computing networks

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IEEE Transactions on Consumer Electronics 2024年

作者： Lin, Bing Chen, Qiaoxin Chen, Xing Jia, Wen-Kang Lu, Yu Xiong, Neal N. Fujian Normal University College of Physics and Energy Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China Peking University School of Computer Science 100871 China Xiamen University Department of Informatics and Communication Engineering Xiamen361005 China Fuzhou University College of Computer and Data Science Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China Fujian Normal University College of Photonic and Electronic Engineering Fuzhou350117 China Fujian Normal University Concord University College Fuzhou350117 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States

Vehicular Edge computing (VEC) is a feasible solution for autonomous driving as it can offload latency-sensitive and computation-intensive tasks from vehicle terminals to roadside units (RSUs) for real-time processing. Due to the high computational resource the workflow applications (i.e., autonomous driving) required, the small vehicle-to-RSUs communication range, and the scarce available resources for each vehicle, it might be difficult to accomplish complex workflow applications through the single-hop offloading paradigm in the Internet of Vehicles (IoV). In this paper, we propose a reinforcement learning (RL) based multi-hop computation offloading scheme for workflow applications to reduce their execution latency in the VEC networks, which considers the data dependency in workflow applications as well as the trust relationship and communication interference among RSUs. Firstly, a preprocessing method is employed to merge the cut-edges in a workflow to reduce the offloading scale of tasks and compress the encoding dimension of offloading solutions. Then, a Deep Q-network algorithm using Phase-optimal State update (DQPS) is proposed to update the offloading policy distribution, in which the deviation of the phase-optimal state from the current state is estimated as the reward of RL to promote the algorithm's convergence to adapt the dynamic VEC networks. Simulation results show that DQPS has the best performance compared to other benchmark schemes. Moreover, the latency of the identical applications can be reduced by 2.5%-25.3% through our offloading scheme with a multi-hop model compared to that with the single-hop paradigm in IoV. © 1975-2011 IEEE.

关键词： Computation offloading

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Monocular Camera-Based Visual Truck Type Recognition

SSRN

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SSRN 2024年

作者： Luo, Haibo Ye, Huaisheng Feng, Chao Shi, Xin Xie, Guoqi School of Computer and Data Science Minjiang University Fuzhou350018 China College of Computer and Data Science Fuzhou University Fuzhou350018 China College of Network and Information Security Fujian Agriculture and Forestry University Fuzhou350018 China Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha410082 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

Truck overload and over-limit are the primary causes of infrastructure damage and traffic safety accidents. In the past 2 years, researchers have started to deploy intelligent Internet of Things system at the source of truck delivery to enforce permit control. Truck type recognition is the core of such a system. However, existing methods suffer from limitations such as low recognition accuracy, high costs, difficult maintenance, and the need for road surface modifications. This study proposes a monocular camera-based visual truck type recognition method. First, the optimized lightweight deep neural network based on machine vision is utilized to detect truckaxles and recognize wheel hub categories. Next, a tracking algorithm and the video streamcontext information are employed to complete the robust statistics of the axle by neighborhoodcounting. Finally, the proposed method further achieves truck type recognition by leveragingavailable prior knowledge. Experimental results demonstrate that this method exhibits superiortime efficiency compared with existing algorithms, and it reduces average processing time by92.37% to 96.24% while ensuring high accuracy. Moreover, the method has been deployed onembedded devices with low computing power to enable real-time operation onsite. © 2024, The Authors. All rights reserved.

关键词： Object detection

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Direct Participant Recruitment Strategy in Sparse Mobile Crowdsensing

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Jisuanji Xuebao/Chinese Journal of Computers 2022年第7期45卷 1539-1556页

作者： Tu, Chun-Yu Yu, Zhi-Yong Han, Lei Zhu, Wei-Ping Huang, Fang-Wan Guo, Wen-Zhong Wang, Le-Ye College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Department of Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China Key Lab of High Confidence Software Technologies Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China

Sparse Mobile Crowdsensing (Sparse MCS) selects a small part of sub-areas for data collection and infers the data of other sub-areas from the collected data. Compared with Mobile Crowdsensing (MCS) that does not use data inference methods, Sparse MCS saves sensing costs while ensuring the quality of global data. However, the existing research works on Sparse MCS only focus on selecting a small part of sub-areas with higher value. It does not consider whether the recruited participants can collect the data of the required sub-areas, and also ignores the value of other data collected by the participants. In order to solve the limitations of traditional methods in sub-areas selection, this paper starts from the perspective of participants and concentrates on the contribution of the data collected by each participant to the entire collection task. All the data contributions collected by each participant will become the basis for decision-making for the participant's choice. And correspondingly, a new idea to deal with the problem of participant selection under Sparse MCS is proposed. In view of the fact that each person's daily movement trajectory is basically stable, and the data collected by different people on their respective trajectories have different values, this paper uses this regularity and difference to study how to directly recruit participants who can collect high-value data. Furthermore, the participant selection problem considered in this paper is not limited to the data collection in the next cycle, but directly recruits some participants to continue the data collection task in the next multiple cycles. The participant selection problem that spans multiple cycles can be modeled as a dynamic decision-making problem. Since heuristic strategies may fall into a local optimal solution, this paper uses reinforcement learning to solve the participant selection problem: We use the participant selection system as an agent of reinforcement learning, and design the

关键词： Compressed sensing

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Echo State network Based on L0 Norm Regularization for Chaotic Time Series Prediction 15th

Echo State Network Based on L0 Norm Regularization for Chaot...

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15th International Conference on Green, Pervasive, and Cloud computing, GPC 2020

作者： Li, Li Huang, Fangwan Yu, Zhiyong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030642426

The echo state network introduces a large and sparse reservoir to replace the hidden layer of the traditional recurrent neural network, which can solve the gradient-based problem of most recurrent neural networks in the training process. However, there may be an ill-posed problem when the least square method is used to calculate the output weight. In this paper, we proposed an echo state network based on L0 norm regularization. The main idea is to limit the number of output connections to compute the output effectively by removing unimportant ones. The simulation results of the chaotic time series prediction show the effectiveness of the proposed model. © 2020, Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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An Improved Leaky-ESN for Electricity Load Forecasting 15th

An Improved Leaky-ESN for Electricity Load Forecasting

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15th International Conference on Green, Pervasive, and Cloud computing, GPC 2020

作者： Lin, Qiaoying Huang, Fangwan Yu, Zhiyong Li, Li College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030642426

In the recent decade, recurrent neural networks become a hot research field, with the powerful capability to capture temporal information from time series. To avoid the vanishing/exploding gradient problem caused by the gradient descent algorithm involved in most recurrent networks, echo state network (ESN) was proposed to contain a large but sparse reservoir instead of traditional hidden layers. However, the performance of ESN is very sensitive to the parameters of the reservoir. In this paper, we focus on the improvements of ESN in the background of electricity load forecasting. With the goal of effectively and efficiently computation in the context of pervasive and cloud computing, two versions of adaptive echo state network (AESN) are designed to adopt a modular control strategy to automatically adjust some parameters of the reservoir. Applying AESN on two synthetic datasets and two real world electricity datasets, experimental results demonstrate that AESN is viable. © 2020, Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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An adaptive offloading framework for Android applications in mobile edge computing

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Science China(information Sciences) 2019年第8期62卷 114-130页

作者： Xing CHEN Shihong CHEN Yun MA Bichun LIU Ying ZHANG Gang HUANG College of Mathematics and Computer Science Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University School of Software Tsinghua University Key Laboratory of High Confidence Software Technologies Ministry of Education

Mobile edge computing(MEC) provides a fresh opportunity to significantly reduce the latency and battery energy consumption of mobile applications. It does so by enabling the offloading of parts of the applications on mobile edges, which are located in close proximity to the mobile devices. Owing to the geographical distribution of mobile edges and the mobility of mobile devices, the runtime environment of MEC is highly complex and dynamic. As a result, it is challenging for application developers to support computation offloading in MEC compared with the traditional approach in mobile cloud computing, where applications use only the cloud for offloading. On the one hand, developers have to make the offloading adaptive to the changing environment, where the offloading should dynamically occur among available computation *** the other hand, developers have to effectively determine the offloading scheme each time the environment changes. To address these challenges, this paper proposes an adaptive framework that supports mobile applications with offloading capabilities in MEC. First, based on our previous study(DPartner), a new design pattern is proposed to enable an application to be dynamically offloaded among mobile devices, mobile edges,and the cloud. Second, an estimation model is designed to automatically determine the offloading *** this model, different parts of the application may be executed on different computation nodes. Finally, an adaptive offloading framework is implemented to support the design pattern and the estimation model. We evaluate our framework on two real-world applications. The results demonstrate that our approach can aid in reducing the response time by 8%–50% and energy consumption by 9%–51% for computation-intensive applications.

关键词： computation offloading software adaptation mobile edge computing application refactoring Android

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UniEmoX: Cross-modal Semantic-Guided Large-Scale Pretraining for Universal Scene Emotion Perception

arXiv

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arXiv 2024年

作者： Chen, Chuang Sun, Xiao Liu, Zhi School of Artificial Intelligence Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machines School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Department of Computer and Network Engineering The University of Electro-Communications Chofu-shi Tokyo182-8585 Japan

Visual emotion analysis holds significant research value in both computer vision and psychology. However, existing methods for visual emotion analysis suffer from limited generalizability due to the ambiguity of emotion perception and the diversity of data scenarios. To tackle this issue, we introduce UniEmoX, a cross-modal semantic-guided large-scale pretraining framework. Inspired by psychological research emphasizing the inseparability of the emotional exploration process from the interaction between individuals and their environment, UniEmoX integrates scene-centric and person-centric low-level image spatial structural information, aiming to derive more nuanced and discriminative emotional representations. By exploiting the similarity between paired and unpaired image-text samples, UniEmoX distills rich semantic knowledge from the CLIP model to enhance emotional embedding representations more effectively. To the best of our knowledge, this is the first large-scale pretraining framework that integrates psychological theories with contemporary contrastive learning and masked image modeling techniques for emotion analysis across diverse scenarios. Additionally, we develop a visual emotional dataset titled Emo8. Emo8 samples cover a range of domains, including cartoon, natural, realistic, science fiction and advertising cover styles, covering nearly all common emotional scenes. Comprehensive experiments conducted on six benchmark datasets across two downstream tasks validate the effectiveness of UniEmoX. The source code is available at https://***/chincharles/u-emo. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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HTtext: A TextCNN-based pre-silicon detection for hardware Trojans

HTtext: A TextCNN-based pre-silicon detection for hardware T...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yi Xu Zhenyi Chen Binhong Huang Ximeng Liu Chen Dong College of Computer and Data Science Fuzhou University Fuzhou China University of South Floride Tampa USA Key Lab of Information Security of Network Systems (Fuzhou University) Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University)

With the advent of the intelligence era, the usage and investment for chips have risen year by year, causing the demand for chip security. Machine learning (ML) analysis has made progress as a pre-silicon hardware Trojan (HT) detection technology. Whereas the performance of existing methods almost relies on the accuracy of multi-feature representation, moreover, it is difficult to extract features manually and easily cause unstable classifier performance (namely uncertainty). In this paper, an automatic feature extraction detecting model is first proposed, named HTtext, which generates simple path sentences from chip netlists and employs TextCNN, a deep learning algorithm, to distinguish HT circuits. The pre-training for TextCNN only uses the automatic single-feature calculation to avoid the uncertainty problem. Additionally, the model can obtain non-repetitive HT component information expression, which satisfies the stable detection performance. To measure the efficiency and balance of the model, the paper proposes the concept of the Stability Efficiency Index (SEI). In the experimental results for the benchmark netlists, not only the average accuracy (ACC) in TextCNN is as high as 99.26%, but also its SEI value ranks first in all comparison classifiers, which proves that the proposed HTtext model has high stability in generality.

关键词： Ports (computers) Semiconductor device measurement Uncertainty Stability criteria Feature extraction Hardware Circuit stability

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