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Direct Participant Recruitment Strategy in Sparse Mobile Crowdsensing

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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Chinese Lexical Based Sentiment Analysis Framework in Meteorology

Chinese Lexical Based Sentiment Analysis Framework in Meteor...

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

作者： Yinan Li Fuquan Zhang Yifan Zhu Sifan Zhang Yu Mao Zhendong Niu School of Computer Science and Technology Beijing Institute of Technology Beijing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Computing and Information University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Evaluating the performance of meteorological service not only relies on physical measurements but also requires public feedback. As a significant part of the evaluation, assessing the sentiment of these feedbacks gives an insightful view which can help to establish reliable decision support in policy making and emergency responding. However, climate and weather are always accompanying phenomena in the evolutionary process of human beings, making the climate and weather-related words themselves contain a large number of extend meanings in people's daily expression. These polysemy phenomena have led to a decline in the performance of traditional general sentiment calculations in the vertical field of meteorology. As far as we know, there is no Chinese sentiment calculation tool in the meteorological field, due to the different expressions between different languages. Facing with the above problems and issues, this paper proposes and establishes a sentiment analysis based on lexicon-based framework of Chinese text in the meteorological field. The framework fuses two major sentiment assessment method consists of two approaches, namely machine learning-based and knowledge based one. First, word embedding similarity and word co-occurrence methods are utilized to construct the domain sentiment dictionary which includes 8,796 negative words and 4,751 positive words. Then, by using the extend sentiment dictionary to select theme words from original text as feature, machine learning models are introduced to assess the sentiment tendency. Experiment showed that the performance of the proposed framework was improved up to 85.79% in terms of overall accuracy compared with traditional methods.

关键词： Dictionaries Meteorology Sentiment analysis Feature extraction Machine learning Vocabulary Semantics

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Object tracking by the least spatiotemporal searches

arXiv

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arXiv 2020年

作者： Yu, Zhiyong Han, Lei Chen, Chao Guo, Wenzhong Yu, Zhiwen College of Mathematics and Computer Sciences Fuzhou University Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350108 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China School of Computer Science Chongqing University Chongqing400044 China

Tracking a suspicious car or a person in a city efficiently is crucial in urban safety management. But how can we complete the task with the minimal number of spatiotemporal searches when massive camera records are involved? To this end, this study proposes a strategy named intermediate searching at heuristic moments (IHMs). At each step, we determine which moment is the best one for the search based on a heuristic indicator. Then, at that moment, locations are searched one by one in descending order of predicted appearing probabilities until a search hit is obtained. We iterate this step until we derive the object's current location. Five searching strategies are compared via experiments. Among these strategies, the IHMs strategy is validated as the most efficient. IHMs can save up to 1/3 of the total cost. This result provides evidence that "searching at intermediate moments can save cost. Copyright © 2020, The Authors. All rights reserved.

关键词： Heuristic algorithms

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AGNN: Alternating Graph-Regularized Neural networks to Alleviate Over-Smoothing

arXiv

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arXiv 2023年

作者： Chen, Zhaoliang Wu, Zhihao Lin, Zhenghong Wang, Shiping Plant, Claudia Guo, Wenzhong The College of Computer and Data Science Fuzhou University Fuzhou350116 China The Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China The Faculty of Computer Science Austria The research network Data Science @ Uni Vienna University of Vienna Vienna1090 Austria

Graph Convolutional network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-based models suffer from the notorious over-smoothing issue, owing to which shallow networks are extensively adopted. This may be problematic for complex graph datasets because a deeper GCN should be beneficial to propagating information across remote neighbors. Recent works have devoted effort to addressing over-smoothing problems, including establishing residual connection structure or fusing predictions from multi-layer models. Because of the indistinguishable embeddings from deep layers, it is reasonable to generate more reliable predictions before conducting the combination of outputs from various layers. In light of this, we propose an Alternating Graph-regularized Neural network (AGNN) composed of Graph Convolutional Layer (GCL) and Graph Embedding Layer (GEL). GEL is derived from the graph-regularized optimization containing Laplacian embedding term, which can alleviate the over-smoothing problem by periodic projection from the low-order feature space onto the high-order space. With more distinguishable features of distinct layers, an improved Adaboost strategy is utilized to aggregate outputs from each layer, which explores integrated embeddings of multi-hop neighbors. The proposed model is evaluated via a large number of experiments including performance comparison with some multi-layer or multi-order graph neural networks, which reveals the superior performance improvement of AGNN compared with state-of-the-art models. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Image Segmentation Using Fuzzy C-means Optimized by Ant Lion Optimization

Image Segmentation Using Fuzzy C-means Optimized by Ant Lion...

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IEEE International Conference on intelligent Data Acquisition and Advanced computing Systems

作者： Can Jin Zhiwei Ye Lingyu Yan Ye Cao Aixin Zhang Lie Ma Xiang Hu Jiwei Hu School of Computer Science Hubei University of Technology Wuhan China Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Wuhan Jingzhi Technology Co. Ltd. Wuhan China Wuhan FiberHome Technical Services Co. Ltd. Wuhan China

Image segmentation is the indispensable part in the field of computer vision. There are tremendous methods for handling this task such as Otsu-thresholding and Fuzzy C-means (FCM). However, the segmentation results of the images are occasionally unsatisfactory due to the presence of noise. In this paper, two kinds of spatial information consisting of the relative position information and the intensity information of the neighborhood pixels in an image are taken into consideration in constructing the objective function in FCM. Moreover, Ant Lion Optimization, one of the recently proposed optimization algorithms is utilized to optimize the relevant index. Bio-inspired ALO has the robust ability to find optimal parameters in search spaces. So the proposed approach to image segmentation based on Fuzzy C-Means (FCM) and Ant Lion Optimization (ALO) may alleviate this problem to a certain degree. A series of experimental validation has been implemented for demonstrating the performance of the proposed approach in the end of the paper.

关键词： Clustering algorithms Image segmentation Optimization Convergence Linear programming Standards Gaussian noise

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Back-propagation Artificial Neural network for Early Diabetic Retinopathy Detection Based On A Priori Knowledge

Back-propagation Artificial Neural Network for Early Diabeti...

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作者： Huiqun Wu Shenghui Zhao Xiaofeng Zhang Aimin Sang Jiancheng Dong Kui Jiang Department of Medical Informatics Medical School of Nantong University Nantong University The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions (Wuyi University) Department of Ophthalmology Affiliated Hospital of Nantong University

Purpose: We aim to develop a back-propagation artificial neural network（BPANN） improved by a priori knowledge and to compare its efficacy with other methods in early diabetic retinopathy（DR） detection. Methods: A total of 240 fundus images, composed of 120 early-stage DR and 120 normal images, were obtained with the same 45° field of view camera, with the macula at the center, as a cohort for further training. All retinal images were processed, and a priori knowledge features such as blood vessel width and tortuosity were semi-automatically extracted. An improved BP-ANN with a priori knowledge was developed, and its efficacy was compared with that of the traditional BP network and SVM. Besides, k-fold cross validation method was conducted to demonstrate the efficiency of the proposed methods. We also developed a graphical user interface of our proposed BP-ANN to aid in DR screening. Results: Our 10 randomization and 5-fold cross validation results of SVM, traditional BP, and improved BP were compared. The results indicated that the BP-ANN with a priori knowledge can achieve better detection results. Besides, our results were also comparable with other reported state-of-art algorithms. During the training stage, the epoch in the improved BP-ANN was less than that in the traditional BP group（109 vs 254）, indicating that the time cost was shorter when using our improved BP-ANN. Furthermore, the accuracy and epoch of both the traditional BP and our improved BP network obtained better performances when the number of hidden neurons was 20. Conclusions: A priori knowledge-based BP-ANN could be a promising measure for early DR detection.

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Ascl: Accelerating Semi-Supervised Learning Via Contrastive Learning

SSRN

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SSRN 2024年

作者： Liu, Haixiong Li, Zuoyong Wu, Jiawei Zeng, Kun Hu, Rong Zeng, Wei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Data Science Minjiang University Fuzhou350121 China School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Physics and Mechanical and Electrical Engineering Longyan University Longyan364012 China

SSL(Semi-supervised learning) is widely used in machine learning, which leverages labeled and unlabeled data to improve model performance. SSL aims to optimize class mutual information, but noisy pseudo-labels introduce false class information due to the scarcity of labels. Therefore, these algorithms often need significant training time to refine pseudo-labels for performance improvement iteratively. To tackle this challenge, we propose a novel plug-and-play method named ASCL(Accelerating Semi-Supervised Learning via Contrastive Learning). This method combines contrastive learning with uncertainty-based selection for performance improvement and accelerates the convergence of SSL algorithms. Contrastive learning initially emphasizes the mutual information between samples as a means to decrease dependence on pseudo-labels. Subsequently, it gradually turns to maximizing the mutual information between classes, aligning with the objective of semi-supervised learning. Uncertainty-based selection provides a robust mechanism for acquiring pseudo-labels. The combination of the contrastive learning module and the uncertainty-based selection module forms a virtuous cycle to improve the performance of the proposed model. Extensive experiments demonstrate that ASCL outperforms state-of-the-art methods in terms of both convergence efficiency and performance. In the experimental scenario where only one label is assigned per class in the CIFAR-10 dataset, the application of ASCL to Pseudo-label, UDA (Unsupervised Data Augmentation for Consistency Training), and Fixmatch benefits substantial improvements in classification accuracy. Specifically, the results demonstrate notable improvements in respect of 16.32%, 6.9%, and 24.43% when compared to the original outcomes. Moreover, the required training time is reduced by almost 50%. © 2024, The Authors. All rights reserved.

关键词： Image classification

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Label Distribution Learning with Correlation information

SSRN

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SSRN 2024年

作者： Wu, Yilin Lin, Yaojin Guo, Wenzhong Ding, Weiping College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Zhangzhou363000 China School of Information Science and Technology Nantong University Nantong226019 China

Label distribution learning quantifies the label space for each instance and has widely applicability in different fields. However, most existing works primarily focus on label correlation, but they still have a limitation in capturing instance correlation. Meanwhile, traditional label distribution learning operates on individual labels sequentially, leading to limitation in applying common features. Therefore, in this paper, we propose a novel approach for label distribution learning with correlation information i.e. instance correlation and label correlation. Specifically, the instance correlation and label correlation are identified by an optimization function and Pearson correlation coefficient, respectively. Besides, we conduct $\ell_{2,1}-$norm regularization to exploit common features. Comprehensive experiments manifest the superiority of our proposed approach against other well-established label distribution learning algorithms. © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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A Wideband Circularly Polarized Folded Transmitarray Antenna With Linearly Polarized Feed 7

A Wideband Circularly Polarized Folded Transmitarray Antenna...

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7th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2024

作者： Sun, Xiangdong Guo, Xulong Li, Yin Zhang, Yongliang Li, Yingsong Inner Mongolia University School of Electronic Information Engineering Hohhot010021 China Inner Mongolia University Inner Mongolia Branch of National Computer Network Emergency Response Technical Team Coordination Center of China Hohhot010020 China Peng Cheng Laboratory Shenzhen518000 China Inner Mongolia University School of Transportation Institute Hohhot010021 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing China

ISBN: (纸本)9798350384437

A low-profile, high-gain, wideband circularly polarized folded transmitarray antenna (CPFTA) is proposed, and a polarization-selective linear-to-circular polarization (LP-CP) conversion element is introduced. The CPFTA consists of a linearly polarized (LP) magnetoelectric dipole antenna and a circularly polarized (CP) helical patch antenna. The CPFTA demonstrates excellent performance with a 3dB gain bandwidth of 29.5% and a 3dB axial ratio (AR) bandwidth of 40.9%. © 2024 IEEE.

关键词： Circular polarization

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Reversible data hiding based on reducing invalid shifting of pixels in histogram shifting

arXiv

引用

arXiv 2019年

作者： Jia, Yujie Yin, Zhaoxia Zhang, Xinpeng Luo, Yonglong Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230601 China School of Computer Science Fudan University Shanghai201203 China Anhui Provincial Key Laboratory of Network and Information Security Wuhu Anhui240002 China

In recent years, reversible data hiding (RDH), a new research hotspot in the field of information security, has been paid more and more attention by researchers. Most of the existing RDH schemes do not fully take it into account that natural image's texture has influence on embedding distortion. The image distortion caused by embedding data in the image's smooth region is much smaller than that in the unsmooth region, essentially, it is because embedding additional data in the smooth region corresponds to fewer invalid shifting pixels (ISPs) in histogram shifting. Thus, we propose a RDH scheme based on the images texture to reduce invalid shifting of pixels in histogram shifting. Specifically, first, a cover image is divided into two sub-images by the checkerboard pattern, and then each sub-image's fluctuation values are calculated. Finally, additional data can be embedded into the region of sub-images with smaller fluctuation value preferentially. The experimental results demonstrate that the proposed method has higher capacity and better stego-image quality than some existing RDH schemes. Copyright © 2019, The Authors. All rights reserved.

关键词： Pixels

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