Query：
学者姓名：程红举
Refining：
Year
Type
Indexed by
Source
Complex
Co-
Language
Clean All
Abstract ：
Edge computing is an emerging promising computing paradigm, which can significantly reduce the service latency by moving computing and storage demands to the edge of the network. Resource -constrained edge servers may fail to process multiple tasks simultaneously when several time -delay -sensitive and computationally demanding tasks are offloaded to only one edge server, and results in some issues such as high task processing costs. In this paper, we introduce a novel idea by dividing one task into several sub -tasks via the dependencies within the task and then offloading the sub -tasks to other edge servers in light of high concurrency for synchronization to minimize the total cost of task processing. To address the challenge of task dependencies and adaptation to dynamic scenes, we propose a Multi -Task Dependency Offloading Algorithm (MTDOA) based on deep reinforcement learning. The task offloading decision is modeled as a Markov decision process, and then a graph attention network is applied to extract the dependency information of different tasks, while LSTM and DQN are combined to deal with sequential problems. The simulation results show that the proposed MTDOA has better convergence ability compared with the baseline algorithms.
Keyword ：
Deep reinforcement learning Deep reinforcement learning Edge computing Edge computing Graph attention network Graph attention network Multi-task dependency Multi-task dependency Task offloading Task offloading
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhang, Xiaoqi , Lin, Tengxiang , Lin, Cheng-Kuan et al. Computational task offloading algorithm based on deep reinforcement learning and multi-task dependency [J]. | THEORETICAL COMPUTER SCIENCE , 2024 , 993 . |
| MLA | Zhang, Xiaoqi et al. "Computational task offloading algorithm based on deep reinforcement learning and multi-task dependency" . | THEORETICAL COMPUTER SCIENCE 993 (2024) . |
| APA | Zhang, Xiaoqi , Lin, Tengxiang , Lin, Cheng-Kuan , Chen, Zhen , Cheng, Hongju . Computational task offloading algorithm based on deep reinforcement learning and multi-task dependency . | THEORETICAL COMPUTER SCIENCE , 2024 , 993 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Sentiment analysis plays an indispensable part in human-computer interaction. Multimodal sentiment analysis can overcome the shortcomings of unimodal sentiment analysis by fusing multimodal data. However, how to extracte improved feature representations and how to execute effective modality fusion are two crucial problems in multimodal sentiment analysis. Traditional work uses simple sub-models for feature extraction, and they ignore features of different scales and fuse different modalities of data equally, making it easier to incorporate extraneous information and affect analysis accuracy. In this paper, we propose a Multimodal Sentiment Analysis model based on Multi-scale feature extraction and Multi-task learning (M(3)SA). First, we propose a multi-scale feature extraction method that models the outputs of different hidden layers with the method of channel attention. Second, a multimodal fusion strategy based on the key modality is proposed, which utilizes the attention mechanism to raise the proportion of the key modality and mines the relationship between the key modality and other modalities. Finally, we use the multi-task learning approach to train the proposed model, ensuring that the model can learn better feature representations. Experimental results on two publicly available multimodal sentiment analysis datasets demonstrate that the proposed method is effective and that the proposed model outperforms baselines.
Keyword ：
multimodal data fusion multimodal data fusion Multimodal sentiment analysis Multimodal sentiment analysis multi-scale feature extraction multi-scale feature extraction Multitasking Multitasking multi-task learning multi-task learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lin, Changkai , Cheng, Hongju , Rao, Qiang et al. M3SA: Multimodal Sentiment Analysis Basedon Multi-Scale Feature Extraction andMulti-Task Learning [J]. | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING , 2024 , 32 : 1416-1429 . |
| MLA | Lin, Changkai et al. "M3SA: Multimodal Sentiment Analysis Basedon Multi-Scale Feature Extraction andMulti-Task Learning" . | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 32 (2024) : 1416-1429 . |
| APA | Lin, Changkai , Cheng, Hongju , Rao, Qiang , Yang, Yang . M3SA: Multimodal Sentiment Analysis Basedon Multi-Scale Feature Extraction andMulti-Task Learning . | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING , 2024 , 32 , 1416-1429 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
With flexible mobility and broad communication coverage, unmanned aerial vehicles (UAVs) have become an important extension of multiaccess edge computing (MEC) systems, exhibiting great potential for improving the performance of federated graph learning (FGL). However, due to the limited computing and storage resources of UAVs, they may not well handle the redundant data and complex models, causing the inference inefficiency of FGL in UAV-assisted MEC systems. To address this critical challenge, we propose a novel LightWeight FGL framework, named LW-FGL, to accelerate the inference speed of classification models in UAV-assisted MEC systems. Specifically, we first design an adaptive information bottleneck (IB) principle, which enables UAVs to obtain well-compressed worthy subgraphs by filtering out the information that is irrelevant to downstream classification tasks. Next, we develop improved tiny graph neural networks (GNNs), which are used as the inference models on UAVs, thus reducing the computational complexity and redundancy. Using real-world graph data sets, extensive experiments are conducted to validate the effectiveness of the proposed LW-FGL. The results show that the LW-FGL achieves higher classification accuracy and faster inference speed than state-of-the-art methods.
Keyword ：
Autonomous aerial vehicles Autonomous aerial vehicles Biological system modeling Biological system modeling Classification inference Classification inference Computational modeling Computational modeling Data models Data models federated graph learning (FGL) federated graph learning (FGL) Graph neural networks Graph neural networks lightweight model lightweight model multiaccess edge computing (MEC) multiaccess edge computing (MEC) Task analysis Task analysis Training Training unmanned aerial vehicle (UAV) unmanned aerial vehicle (UAV)
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhong, Luying , Chen, Zheyi , Cheng, Hongju et al. Lightweight Federated Graph Learning for Accelerating Classification Inference in UAV-Assisted MEC Systems [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (12) : 21180-21190 . |
| MLA | Zhong, Luying et al. "Lightweight Federated Graph Learning for Accelerating Classification Inference in UAV-Assisted MEC Systems" . | IEEE INTERNET OF THINGS JOURNAL 11 . 12 (2024) : 21180-21190 . |
| APA | Zhong, Luying , Chen, Zheyi , Cheng, Hongju , Li, Jie . Lightweight Federated Graph Learning for Accelerating Classification Inference in UAV-Assisted MEC Systems . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (12) , 21180-21190 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Many existing searchable encryption schemes are inflexible in retrieval patterns. The data usage authorization is almost permanent valid as long as the user is not revoked. This "all-or-nothing" authorization mode is not compatible with the "pay-as-you-use" commercial billing model. In this article, we propose a new notion called time controlled expressive predicate query with accountable anonymity. It realizes time controlled data query, where a time server issues time token to authorize search privilege in designated time period. The data users can anonymously query on encrypted data and the anonymity is accountable in a way that the trusted authority is able to deanonymize data users if they misbehave in the system. The underlying techniques are anonymous credential, Pederson commitment and non-interactive zero-knowledge proof. We firstly design an efficient expressive predicate query (EPQ) scheme, which is proved secure to protect the privacy of expressive search predicate. Based on EPQ, we present a concrete system instantiation, which realizes key-escrow free and time token nontransferability. The formal definition and security models are given out. The system is formally proved indistinguishable against chosen keyword-set attacks, unforgeable of time tokens and accountable of anonymous users. The comparison and experiment results demonstrate its scalability and efficiency.
Keyword ：
accountable accountable anonymity anonymity Authorization Authorization Cloud computing Cloud computing Encryption Encryption expressive keyword search expressive keyword search Keyword search Keyword search Protocols Protocols Public key Public key Searchable encryption Searchable encryption Servers Servers time control time control zero-knowledge proof zero-knowledge proof
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yang, Yang , Rong, Chunming , Zheng, Xianghan et al. Time Controlled Expressive Predicate Query With Accountable Anonymity [J]. | IEEE TRANSACTIONS ON SERVICES COMPUTING , 2023 , 16 (2) : 1444-1457 . |
| MLA | Yang, Yang et al. "Time Controlled Expressive Predicate Query With Accountable Anonymity" . | IEEE TRANSACTIONS ON SERVICES COMPUTING 16 . 2 (2023) : 1444-1457 . |
| APA | Yang, Yang , Rong, Chunming , Zheng, Xianghan , Cheng, Hongju , Chang, Victor , Luo, Xiangyang et al. Time Controlled Expressive Predicate Query With Accountable Anonymity . | IEEE TRANSACTIONS ON SERVICES COMPUTING , 2023 , 16 (2) , 1444-1457 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
In this article, we propose dual traceable distributed attribute based encryption with subset keyword search system (DTDABE-SKS, abbreviated as DT) to simultaneously realize data source trace (secure provenance) and user trace (traitor trace) and flexible subset keyword search from polynomial interpolation. Leveraging non-interactive zero-knowledge proof technology, DT preserves privacy for both data providers and users in normal circumstances, but a trusted authority can disclose their real identities if necessary, such as the providers deceitfully uploading false data or users maliciously leaking secret attribute key. Next, we introduce the new conception of updatable and transferable message-lock encryption (UT-MLE) for block-level dynamic encrypted file update, where the owner does not have to download the whole ciphertext, decrypt, re-encrypt and upload for minor document modifications. In addition, the owner is permitted to transfer file ownership to other system customers with efficient computation in an authenticated manner. A nontrivial integration of DT and UT-MLE lead to the distributed ABSE with ownership transfer system (DTOT) to enjoy the above merits. We formally define DT, UT-MLE, and their security model. Then, the instantiations of DT and UT-MLE, and the formal security proof are presented. Comprehensive comparison and experimental analysis based on real dataset affirm their feasibility.
Keyword ：
ABE ABE Cloud computing Cloud computing Data privacy Data privacy Distributed databases Distributed databases Dual traceability Dual traceability Encryption Encryption Hospitals Hospitals Keyword search Keyword search Maximum likelihood estimation Maximum likelihood estimation ownership transferable ownership transferable searchable encryption searchable encryption updatable MLE updatable MLE
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yang, Yang , Deng, Robert H. , Guo, Wenzhong et al. Dual Traceable Distributed Attribute-Based Searchable Encryption and Ownership Transfer [J]. | IEEE TRANSACTIONS ON CLOUD COMPUTING , 2023 , 11 (1) : 247-262 . |
| MLA | Yang, Yang et al. "Dual Traceable Distributed Attribute-Based Searchable Encryption and Ownership Transfer" . | IEEE TRANSACTIONS ON CLOUD COMPUTING 11 . 1 (2023) : 247-262 . |
| APA | Yang, Yang , Deng, Robert H. , Guo, Wenzhong , Cheng, Hongju , Luo, Xiangyang , Zheng, Xianghan et al. Dual Traceable Distributed Attribute-Based Searchable Encryption and Ownership Transfer . | IEEE TRANSACTIONS ON CLOUD COMPUTING , 2023 , 11 (1) , 247-262 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Natural disasters often have an unpredictable impact on human society and can even cause significant problems, such as damage to communication equipment in disaster areas. In such post-disaster emergency rescue situations, unmanned aerial vehicles (UAVs) are considered an effective tool by virtue of high mobility, easy deployment, and flexible communication. However, the limited size of UAVs leads to bottlenecks in battery capacity and computational power, making it challenging to perform overly complex computational tasks. In this paper, we propose a UAV cluster-assisted task-offloading model for disaster areas, by adopting UAV clusters as aerial mobile edge servers to provide task-offloading services for ground users. In addition, we also propose a deep reinforcement learning-based UAV cluster-assisted task-offloading algorithm (DRL-UCTO). By modeling the energy efficiency optimization problem of the system model as a Markov decision process and jointly optimizing the UAV flight trajectory and task-offloading policy to maximize the reward value, DRL-UCTO can effectively improve the energy use efficiency of UAVs under limited-resource conditions. The simulation results show that the DRL-UCTO algorithm improves the UAV energy efficiency by about 79.6% and 301.1% compared with the DQN and Greedy algorithms, respectively.
Keyword ：
deep reinforcement learning deep reinforcement learning emergent disaster scenarios emergent disaster scenarios task offloading task offloading trajectory optimization trajectory optimization UAV cluster UAV cluster
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Shi, Minglin , Zhang, Xiaoqi , Chen, Jia et al. UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios [J]. | APPLIED SCIENCES-BASEL , 2023 , 13 (8) . |
| MLA | Shi, Minglin et al. "UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios" . | APPLIED SCIENCES-BASEL 13 . 8 (2023) . |
| APA | Shi, Minglin , Zhang, Xiaoqi , Chen, Jia , Cheng, Hongju . UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios . | APPLIED SCIENCES-BASEL , 2023 , 13 (8) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
为了解决多视图行人检测算法对多尺度目标检测效果不佳的问题，提出一种采用膨胀卷积编码进行多视图信息聚合的算法。利用不同膨胀率的膨胀卷积在单层特征层中生成不同尺度的感受野，覆盖目标所有尺度的范围，提高对多尺度目标的检测能力。在Wildtrack数据集上进行仿真实验的结果显示，采用所提算法多目标检测精度最高可达90.7%。
Keyword ：
复杂场景 复杂场景 多级检测 多级检测 多视数据 多视数据 特征融和 特征融和 膨胀卷积 膨胀卷积 行人检测 行人检测
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 叶洪滨 , 林政宽 , 程红举 . 基于多相机特征融合的行人检测算法 [J]. | 北京邮电大学学报 , 2023 , 46 (05) : 66-71 . |
| MLA | 叶洪滨 et al. "基于多相机特征融合的行人检测算法" . | 北京邮电大学学报 46 . 05 (2023) : 66-71 . |
| APA | 叶洪滨 , 林政宽 , 程红举 . 基于多相机特征融合的行人检测算法 . | 北京邮电大学学报 , 2023 , 46 (05) , 66-71 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
By effectively assigning and migrating tasks based on service requirements, the success rate of task execution in cloud-edge-end collaborative computing can be significantly enhanced, thereby ensuring the provision of high-quality services for users. The majority of conventional cloud-edge-end task offloading approaches primarily focus on static scenarios, posing challenges in ensuring the success rate of task execution in mobile scenarios. It is imperative to address the problem of constructing a joint optimization scheme for task allocation and migration that is suitable for mobile scenarios. This paper re-define the latency, energy, and migration model for task processing in mobile scenarios. Furthermore, we propose a Deep Reinforcement learning (DRL)-based Task allocation and Migration optimization algorithm (DRTM) to enhance the efficiency of task completion and minimize the total cost. DRTM introduces the traditional Actor-Critic with a mirror deep deterministic policy gradient (DDPG) and establishes a duel Q-network to update parameters on respective gradients for optimal policy acquisition. DRTM incorporates two target networks to effectively improve stability and convergence speed during training while reducing computational complexity. The experimental results demonstrate that DRTM can offer a high-performance task assignment and migration scheme in mobile scenarios, thereby significantly reducing the total cost of the task execution life cycle. © 2023 IEEE.
Keyword ：
Computation offloading Computation offloading Deep learning Deep learning Life cycle Life cycle Reinforcement learning Reinforcement learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhang, Xiaoqi , Cheng, Hongju . Joint Task Assignment and Migration in Cloud-Edge-End Collaborative Computing Based on DRL [C] . 2023 : 265-270 . |
| MLA | Zhang, Xiaoqi et al. "Joint Task Assignment and Migration in Cloud-Edge-End Collaborative Computing Based on DRL" . (2023) : 265-270 . |
| APA | Zhang, Xiaoqi , Cheng, Hongju . Joint Task Assignment and Migration in Cloud-Edge-End Collaborative Computing Based on DRL . (2023) : 265-270 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
To address the issue of poor detection performance for multi-scale objects in multi-view pedestrian detection algorithms, a dilated encoder method is proposed for aggregating multi-view information. The dilated encoder utilizes dilated convolutions with different dilation rates in a single layer to generate receptive fields of different scales, thus covering the entire scale range of the targets and improving the detection capability for multi-scale objects. Experimental results on the Wildtrack dataset show that the algorithm achieves a maximum multiple object detection accuracy of 90. 7% . © 2023 Beijing University of Posts and Telecommunications. All rights reserved.
Keyword ：
crowded scene crowded scene dilated convolution dilated convolution feature fusion feature fusion multi-scale detection multi-scale detection multi-view data multi-view data pedestrian detection pedestrian detection
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Ye, H. , Lin, Z. , Cheng, H. . Pedestrian Detection Algorithm Based on Multi-Camera Feature Fusion; [基于多相机特征融合的行人检测算法] [J]. | Journal of Beijing University of Posts and Telecommunications , 2023 , 46 (5) : 66-71 . |
| MLA | Ye, H. et al. "Pedestrian Detection Algorithm Based on Multi-Camera Feature Fusion; [基于多相机特征融合的行人检测算法]" . | Journal of Beijing University of Posts and Telecommunications 46 . 5 (2023) : 66-71 . |
| APA | Ye, H. , Lin, Z. , Cheng, H. . Pedestrian Detection Algorithm Based on Multi-Camera Feature Fusion; [基于多相机特征融合的行人检测算法] . | Journal of Beijing University of Posts and Telecommunications , 2023 , 46 (5) , 66-71 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The current sharding schemes show some shortcomings, such as poor performance while handling cross-shard transactions, and the transactions are verified in an inefficient way. A sharding blockChain system with output shard Batch Processing and Parallel transaction Verification(BPPV-Chain) is proposed in this study. The core idea of the proposed scheme is elucidated as follows. The output shard is capable of verifying and processing the input availability certificates generated by the input shard in a batch manner, and it can generate the transaction availability certificates of different input shards when coping with the cross-shard transactions. The input shard unlocks or spends UTXO following the transaction avaliability certificates to for the cross-shard collaboration. On that basis, the communication complexity of cross-shard transactions can be reduced. Moreover, a parallel transaction verification scheme is present to increase the efficiency of transaction verification. In this scheme, UTXO is verified in a serial manner to prevent double spending and the signatures and values of multiple transactions are checked in a parallel manner. As indicated by the experimental results, BPPV-Chain outperforms existing sharding blockchain systems especially under the percentage of cross-shard transactions of not more than 80%. Furthermore, BPPV-Chain also ensures linear growth of throughput with the number of shards increasing, such that the scalability of BPPV-Chain can be confirmed.
Keyword ：
Blockchain Blockchain Cross-shard transaction Cross-shard transaction Output shard batch processing Output shard batch processing Parallel verification Parallel verification Sharding Sharding
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Ding, Jinfeng , Hu, Qihua , Lin, Changkai et al. BPPV-Chain: A Sharding Blockchain System with Output Shard Batch Processing and Parallel Transaction Verification [J]. | 2023 5TH INTERNATIONAL CONFERENCE ON BLOCKCHAIN TECHNOLOGY, ICBCT 2023 , 2023 : 8-14 . |
| MLA | Ding, Jinfeng et al. "BPPV-Chain: A Sharding Blockchain System with Output Shard Batch Processing and Parallel Transaction Verification" . | 2023 5TH INTERNATIONAL CONFERENCE ON BLOCKCHAIN TECHNOLOGY, ICBCT 2023 (2023) : 8-14 . |
| APA | Ding, Jinfeng , Hu, Qihua , Lin, Changkai , Shi, Minglin , Cheng, Hongju . BPPV-Chain: A Sharding Blockchain System with Output Shard Batch Processing and Parallel Transaction Verification . | 2023 5TH INTERNATIONAL CONFERENCE ON BLOCKCHAIN TECHNOLOGY, ICBCT 2023 , 2023 , 8-14 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
