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Online Non-Stationary Pricing Incentives for Budget-Limited Crowdsensing

IEEE Transactions on big data 2024年

作者： Sun, Jiajun Wu, Dianliang Huaiyin Normal Universiy College of Computer Science and Technology Huaian223300 China Huaiyin Normal Universiy Huai'an Key Laboratory of Big Data Intelligent Computing and Analysis Huaian223300 China Shanghai Jiao Tong University Shanghai200030 China

The promising applications of mobile crowdsensing (MCS) have attracted much research interest recently, especially for the posted-pricing scenes. However, existing works mainly focus on the stationary MCS, no matter whether in a stochastic or adversarial environment, where each price (or arm) remains identical over time. However, in many realistic MCS applications such as environment monitoring and recommendation systems, stationary bandits do not model the posted-pricing sequential decision problems where the reward distributions of each price (arm) and cost distribution vary over time due to the changes in light intensity and mobile devices' remnant energy. While in this paper, we study a more general submodular crowdsensing scene to address the non-stationary sequential pricing problems, and construct a monotonic submodular function merging the marginal reward and temporal difference errors(TD-errors) of deep reinforcement learning (DRL). Moreover, we explore a weighted budget-limited non-stationary pricing mechanism by using the deep deterministic policy gradient (DDPG) method for submodular MCS from the perspectives of the hard-drop and soft-drop weights. Our mechanism can readily be extended to non-submodular MCS or other MCS scenes. Extensive simulations demonstrate that our mechanism outweighs existing benchmarks. © 2015 IEEE.

关键词： Stochastic systems

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Morl4pdes: data-Driven Discovery of Pdes Based on Multi-Objective Optimization and Reinforcement Learning

SSRN

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SSRN 2023年

作者： Zhang, Xiaoxia Guan, Junsheng Liu, Yanjun Wang, Guoyin Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China Nankai University China

Extracting fundamental behavior patterns or governing equations from data can deepen our understanding and insights into physical systems, it will lead to the better control and application of these systems in science and engineering. Currently, most existing methods in extracting governing equations require a candidate function term library in advance, which results in the limitations of those learned equations. To achieve this goal, in this paper we propose a new method for data-driven discovery of parsimonious partial differential equations (PDEs) by utilizing symbolic regression based on multi-objective optimization and reinforcement learning, we call the MORL4PDEs in short. Specifically, neural network agent aims to generate the pre-order traversal sequence of a binary tree, and through which we can obtain the expression for each PDE. Then the resulting individuals can be used as the initial population in the multi-objective genetic algorithm to ensure the accuracy and parsimony of the equations, whose plausibility is guaranteed according to the constraints generated from the rules of PDEs. Meanwhile, the neural network is optimized through reinforcement learning with the final expression of each PDE as a reward. Finally, several experiments are conduct to demonstrate the effectiveness of the proposed method, and the results show MORL4PDEs can identify governing equations in different dynamic systems, including those PDEs with complex forms and high-order derivatives. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-...

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

作者： Kaijun Deng Dezhi Zheng Jindong Xie Jinbao Wang Weicheng Xie Linlin Shen Siyang Song Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Department of Computer Science University of Exeter

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code is available at https://***/CVI-SZU/DEGSTalk.

关键词： Hair Training Three-dimensional displays Dynamics Signal processing Rendering (computer graphics) Noise measurement Speech processing Faces Videos

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Remaining Useful Life Prediction Method of Lithium-Ion Batteries is Based on Variational Modal Decomposition and Deep Learning Integrated Approach

SSRN

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SSRN 2023年

作者： Wang, Shuai Ma, Hongyan Zhang, Yingda Li, Shengyan He, Wei School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing10044 China Institute of Distributed Energy Storage Safety Big Data Beijing10044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing10044 China

A common method based on variational modal decomposition (VMD) and an integrated depth model is proposed to address the problem that it is difficult to precisely anticipate the remaining useful life (RUL) of lithium-ion batteries (LIBs). Initially, VMD is employed to decompose the LIBs capacity data in multiple scales to obtain the signal's global degradation tendency and local random fluctuation components. Then, the global degradation trend and each fluctuation component are modeled using an echo state network (ESN) and a Bayesian optimized long short-term memory (LSTM) network, respectively. The final LIBs RUL prediction results are obtained by integrating the prediction outcomes. On three public LIBs datasets with distinct degradation characteristics, the performance of the proposed model is tested, and alternative prediction algorithms are compared. The results of the experiments show that the proposed model's maximum average absolute percentage error does not exceed 0.43%. The average relative error not more than 0.5%, indicating great accuracy and stability in prediction. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Change Dino: A Unified Transformer-Based Framework For Object-Level Change Detection and Segmentation in Remote Sensing Imagery

Change Dino: A Unified Transformer-Based Framework For Objec...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yixin Chen Ruiqian Zhang Xiaogang Ning Hanchao Zhang You He Yuxing Xie Jiaming Wang Chinese Academy of Surveying and Mapping Beijing China Chair of Data Science in Earth Observation Technical University of Munich Munich Germany Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

In the realm of remote sensing change detection, deep learning-based pixel-level methods have shown commendable accuracy and speed. However, due to the difficulty in distinguishing between each changed object and the high matching accuracy required, there are still limitations in practical applications. To address these issues, we propose Change DINO, a novel unified object-level change detection and segmentation framework and the inaugural Transformer-based object-level change detection framework, which leverages the Hierarchical Temporal Fusion Module (HTFM) with dual branches to extract change features from bi-temporal images, integrating these features into the Transformer's encoder-decoder and segmentation branches. Experimental results show that compared to other pixel-level (including Transformer-based) change detection methods, Change DINO exhibits superior performance even on pixel-level evaluation strategy, achieving F1 score improvements of 5.09% and 10.31% compared with Transformer-based methods. This capability significantly mitigates the limitations inherent in pixel-level detection, showcasing Change DINO's substantial potential for diverse applications in change detection tasks.

关键词： Natural resources Image segmentation Accuracy Production Transformers Feature extraction Sensors

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Design of a clever carbon monoxide monitoring system

Design of a clever carbon monoxide monitoring system

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第35届中国控制与决策会议

作者： Yanxi Cao Hongyan Ma Yingda Zhang Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big DataBeijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture

Carbon monoxide is a colorless and odorless gas that is extremely *** monoxide cannot be accurately detected or eliminated by the senses in high concentrations in the body under relatively airtight *** a result,the human body can only inhale it *** monoxide inhalation can deplete the body's oxygen supply,causing symptoms such as nausea,dizziness,headaches,and,in severe cases,*** response to the event's insidiousness,urgency,and ***,because the current carbon monoxide monitoring system is not portable,it is proposed to design a microcontroller as the core,with a concentration collection module,a wireless transmission module,an LCD module,and so *** with information between intelligent monitoring systems via wireless transmission module and mobile phone *** system consists of a microcontroller,concentration acquisition module,wireless transmission module,and LCD display *** the performance of the system is tested by changing the environmental conditions,while a control test is *** results show that the system has a fast and stable transmission rate,small size,and wireless monitoring *** can be informed of environmental conditions via their mobile phones,providing early warning and quick alarms.

关键词： Wi-Fi transmission carbon monoxide monitoring microcontroller unit indoor real-time monitoring

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Candidate region aware nested named entity recognition

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NEURAL NETWORKS 2021年 142卷 340-350页

作者： Jiang, Deng Ren, Haopeng Cai, Yi Xu, Jingyun Liu, Yanxia Leung, Ho-fung South China Univ Technol Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou Peoples R China South China Univ Technol Sch Software Engn Guangzhou Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China

Named entity recognition (NER) is crucial in various natural language processing (NLP) tasks. However, the nested entities which are common in practical corpus are often ignored in most of current NER models. To extract the nested entities, two categories of models (i.e., feature-based and neural network-based approaches) are proposed. However, the feature-based models suffer from the complicated feature engineering and often heavily rely on the external resources. Discarding the heavy feature engineering, recent neural network-based methods which treat the nested NER as a classification task are designed but still suffer from the heavy class imbalance issue and the high computational cost. To solve these problems, we propose a neural multi-task model with two modules: Binary Sequence Labeling and Candidate Region Classification to extract the nested entities. Extensive experiments are conducted on the public datasets. Comparing with recent neural network-based approaches, our proposed model achieves the better performance and obtains the higher efficiency. (C) 2021 Published by Elsevier Ltd.

关键词： Named entity recognition Sequence labeling Multi-task learning

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Remote Image Acquisition System Based on STM32 for Spodoptera frugiperda

Remote Image Acquisition System Based on STM32 for Spodopter...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Jumei Chang Quanyuan Xu Ying Lu Hanrui Zhang College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Key Laboratory of Forestry and Ecological Big Data State Forestry Administration Southwest Forestry University China

ISBN: (纸本)9781665479691

To enable remote image acquisition and wireless transmission for field sex lure monitoring of Spodoptera frugiperda, the design consists of a CPU processor module, power supply module, image acquisition module, 4G wireless network transmission module, and external SRAM module. The system employs the STM32F407 control core, the OV2640 camera for image acquisition, the JPEG algorithm to compress and decode the image data collected by the camera, and automatically restore the image collected at the monitoring point, the EC20 as a 4G wireless communication network for image data transmission, and the transmission of real-time image data to the server side of the upper computer to achieve human-computer interaction and real-time interaction of system daemons. The experimental results show that the system achieves remote image acquisition of grassland moths and reliably transmits the collected image data to the server side of the upper computer via a 4G wireless network, allowing the monitor to view images of grassland moth monitoring points collected by the system in real-time and save the collected image data locally. This system accomplishes the automation and intelligence of the grassland moth monitoring system, prepares the data for subsequent grassland moth research, and has a broad application promise for grassland moth population monitoring.

关键词： Costs Automation Wireless networks data visualization Cameras Real-time systems Hardware

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Context-Aware Dynamic Word Embeddings for Aspect Term Extraction

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2024年第1期15卷 144-156页

作者： Xu, Jingyun Xie, Jiayuan Cai, Yi Lin, Zehang Leung, Ho-Fung Li, Qing Chua, Tat-Seng South China Univ Technol SCUT Sch Software Engn Guangzhou 510006 Peoples R China South China Univ Technol SCUT Key Lab Big Data & Intelligent Robot SCUT Minist Educ Guangzhou 510006 Peoples R China Pazhou Lab Guangdong Artificial Intelligence & Digital Econ L Guangzhou 510335 Peoples R China Hong Kong Polytech Univ Dept Comp Kowloon Hong Kong Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Natl Univ Singapore Sch Comp Singapore 119077 Singapore Sea NExT Joint Lab Singapore 117417 Singapore

The aspect term extraction (ATE) task aims to extract aspect terms describing a part or an attribute of a product from review sentences. Most existing works rely on either general or domain embedding to address this problem. Despite the promising results, the importance of general and domain embeddings is still ignored by most methods, resulting in degraded performances. Besides, word embedding is also related to downstream tasks, and how to regularize word embeddings to capture context-aware information is an unresolved problem. To solve these issues, we first propose context-aware dynamic word embedding (CDWE), which could simultaneously consider general meanings, domain-specific meanings, and the context information of words. Based on CDWE, we propose an attention-based convolution neural network, called ADWE-CNN for ATE, which could adaptively capture the previous meanings of words by utilizing an attention mechanism to assign different importance to the respective embeddings. The experimental results show that ADWE-CNN achieves a comparable performance with the state-of-the-art approaches. Various ablation studies have been conducted to explore the benefit of each component. Our code is publicly available at http://***/xiejiajia2018/ADWE-CNN.

关键词： Aspect term extraction attention mechanism sentiment analysis word embedding

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Graph neural topic model with commonsense knowledge

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INFORMATION PROCESSING & MANAGEMENT 2023年第2期60卷

作者： Zhu, Bingshan Cai, Yi Ren, Haopeng South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou 510006 Peoples R China Pazhou Lab Guangzhou 510335 Peoples R China

Traditional topic models are based on the bag-of-words assumption, which states that the topic assignment of each word is independent of the others. However, this assumption ignores the relationship between words, which may hinder the quality of extracted topics. To address this issue, some recent works formulate documents as graphs based on word co-occurrence patterns. It assumes that if two words co-occur frequently, they should have the same topic. Nevertheless, it introduces noise edges into the model and thus hinders topic quality since two words co-occur frequently do not mean that they are on the same topic. In this paper, we use the commonsense relationship between words as a bridge to connect the words in each document. Compared to word co-occurrence, the commonsense relationship can explicitly imply the semantic relevance between words, which can be utilized to filter out noise edges. We use a relational graph neural network to capture the relation information in the graph. Moreover, manifold regularization is utilized to constrain the documents' topic distributions. Experimental results on a public dataset show that our method is effective at extracting topics compared to baseline methods.

关键词： Topic modeling Commonsense knowledge Graph convolutional network Manifold regularization Neural variational inference

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