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PA-GAN: Graph Attention Network for Preference-Aware Social Recommendation 4

PA-GAN: Graph Attention Network for Preference-Aware Social ...

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2021 4th international conference on Advanced Algorithms and Control Engineering, ICAACE 2021

作者： Hou, Liyang Kong, Wenping Gao, Yali Chen, Yang Li, Xiaoyong Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China

Social recommendation has been recently proposed by incorporating trust relationship to alleviate data-sparsity and cold-start problems. However, most of existing works only focus on friends different contribution to model user representation. they ignore users have different preference on items, and share different preference with friends. To address these problems, in this paper, we propose a novel Preference-Aware Graph Attention Network (PA-GAN) for trust recommendation. And we design three modules: item aggregation for user, friend-preference aggregation for user and user aggregation for item to model users' local and global preference. Experiments on two publicly available datasets shows the proposed model PA-GAN outperforms the state-of-the-art recommendation models, and improves performance greatly. © Published under licence by IOP Publishing Ltd.

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Handwritten Mathematical Expression Recognition with Self-Attention 21

Handwritten Mathematical Expression Recognition with Self-At...

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Proceedings of the 2021 4th international conference on Algorithms, computing and Artificial Intelligence

作者： Xueke Chi Da-Han Wang Yuefeng Wu Yun Wu Fujian Key Laboratory of Pattern Recognition and Image Understanding China and School of Computer and Information Engineering Xiamen University of Technology China

ISBN: (纸本)9781450385053

Attention-based encoder-decoder models have made great success on handwritten mathematical expression recognition in recent years. However, this kind of method has the problem of attention drift, because under the local attention mechanism based on RNN, the high similarity between coding features can cause attention confusion. To settle this problem, we propose an encoder-decoder model with self-attention, which captures the global information of the feature map and fuses the local information of the CNN as complementary features. Experiments are conducted on the CROHME2014 and CROHME 2016 competition datasets. the experimental results show that, when only using the official training dataset, the proposed method achieves recognition accuracies of 51.98% and 50.74% on the CROHME2014 and CROHME2016 competition datasets, respectively, which outperforms the other methods significantly. the improvements demonstrate the effectiveness of the self-attention module.

关键词： handwritten mathematical expression offline recognition non-local self-attention

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RLEPSO: Reinforcement learning based Ensemble particle swarm optimizer 4

RLEPSO: Reinforcement learning based Ensemble particle swarm...

引用

4th international conference on Algorithms, computing and Artificial Intelligence, ACAI 2021

作者： Yin, Shiyuan Liu, Yi Gong, GuoLiang Lu, Huaxiang Li, Wenchang Institute of Semiconductors Chinese Academy of Sciences Beijing China Institute of Semiconductors Chinese Academy of Sciences University of Chinese Academy of Sciences China Academy of Sciences Brain Science and Intelligent Technology Excellence Innovation Center Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China

ISBN: (纸本)9781450385053

Evolution is the driving force behind the evolution of biological intelligence. Learning is the driving force behind human civilization. the combination of evolution and learning can form an entire natural world. Now, reinforcement learning has shown significant effects in many places. However, Currently, researchers in the field of optimization algorithms mainly focus on evolution strategies. And there is very little research on learning. Inspired by these ideas, this paper proposes a new particle swarm optimization algorithm Reinforcement learning based Ensemble particle swarm optimizer (RLEPSO) that combines reinforcement learning. the algorithm uses reinforcement learning for pre-training in the design phase to automatically find a more effective combination of parameters for the algorithm to run better and Complete optimization tasks faster. Besides, this algorithm integrates two robust particle swarm variants. And it sets the weight parameters for different algorithms to better adapt to the solution requirements of a variety of different optimization problems, which significantly improves the robustness of the algorithm. RLEPSO makes a certain number of sub-swarms to increase the probability of finding the global optimum and increasing the diversity of particle swarms. this proposed RLEPSO is evaluated on an optimization test functions benchmark set (CEC2013) with 28 functions and compared with other eight particle swarm optimization variants, including three state-of-the-art optimization algorithms. the results show that RLEPSO has better performance and outperforms all compared algorithms. © 2021 Owner/Author.

关键词： Reinforcement learning

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Monocular Pseudo-LiDAR 3D Object Detection Method Based on Confidence and Feature Optimization 5

Monocular Pseudo-LiDAR 3D Object Detection Method Based on C...

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5th IEEE international conference on Smart Internet of things, SmartIoT 2021

作者： Zhang, Jianlong Fang, Guangzu Wang, Bin Chen, Chen Guo, Xinyu Zhou, Yang Li, Ji School of Electronic Engineering Xidian University Xi’an China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China Ministry of water resources of China Beijing China Goldenwater Information Technology Co. Ltd. Beijing China

ISBN: (纸本)9781665445115

3D object detection based on the monocular Pseudo-LiDAR is cost-effective compared with multi-sensor solutions in autonomous driving, and made great progress in recent. However, these methods still suffer from the following disadvantages, i.e., (1) the depth estimation error of the background points in the Pseudo-LiDAR is larger than that of the object points, which leads to the degradation of the object detection performance. (2) the existing point cloud detection network is difficult to effectively capture the local features and global correlation of Pseudo-LiDAR, which leads to the weak feature representation ability of the point cloud. To this end, we propose a 3D object detection method of Pseudo-LiDAR based on Confidence and Feature optimization. In our method, we first propose a hierarchical structure of the point cloud confidence optimization method, which carries out resampling to filter out the background points according to the confidence distribution, so as to improve the significance of the PseudoLiDAR object point cloud. then, we design a hierarchical feature extraction module, which uses DGCNN to obtain the local features of the keypoints and the point transformer structure to capture the global relevance of the keypoints, so as to improve the feature representation ability of the point cloud. On the popular KITTI benchmark, our approach achieves better performance than the mainstream method. © 2021 IEEE.

关键词： Object detection

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Structured information extraction for bone scan image report texts 4

Structured information extraction for bone scan image report...

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2021 4th international conference on Advanced Algorithms and Control Engineering, ICAACE 2021

作者： Huang, Mengge Zhang, Jingyi Deng, Tao Man, Zhengxing Lin, Qiang Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou730030 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou730030 China School of Mathematics and Computer Science Northwest Minzu University Lanzhou730030 China

Extracting structured information from the bone scan image report text plays a crucial role in supporting clinical analysis and research. this study summarized the structure and characteristics of 3608 bone scan image report text using dictionary-based information extraction method, including data cleaning, entity recognition, building dictionary and extraction rules. this method was used to obtain the structured data of bone scan image report text required for clinical research, and the effect evaluation was carried out on 1000 randomly selected report texts, with the precision rate and recall rate higher than 90%. the method proposed in this study is practical and could have good effect on structured results for bone scan imaging report text. © Published under licence by IOP Publishing Ltd.

关键词： Clinical research

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Whale Image Retrieval with Fused Mask information

Whale Image Retrieval with Fused Mask Information

引用

international conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Qijun Shao Li Yang Bin Wu College of Information Engineering China Jiliang University Zhejiang China Zhejiang Chint Instrument & Meter Co. LTD Hangzhou China

Content-based image retrieval (CBIR) aims to search images from the gallery that are most similar to a query. In this work, we propose a novel image retrieval framework called TM-Net (Two-branch network with Mask). the core of this approach is to incorporate information inherent to the image in the input phase of the network, which operates to force the network to focus more on the object itself in the image and to extract a feature descriptor that is strongly correlated with the object. To further the feature extraction, our network adopts a two-branch structure, where one branch extracts global features and the other branch extracts local features. In addition, we use a classification method with metric learning, so that the loss function of metric learning and the classification loss complement each other to increase the inter-class distance and decrease the intra-class distance. Moreover, to better sample the training samples, we use a hard sample sampling strategy. we conduct extensive experiments on the whale dataset, and the experimental results show that the method is simple and effective, and surpasses the current state-of-the-art methods. We obtained top-1 score of 94.6% on the whale test set, which is better than existing methods.

关键词： Measurement Training Fuses Image retrieval Force Whales Feature extraction

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CPACS: Customer Preference-Aware Customer Service Solutions Recommendation 21

CPACS: Customer Preference-Aware Customer Service Solutions ...

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4th international conference on Big Data Technologies, ICBDT 2021

作者： Guo, Shuangshuang Yin, Mingli Zhang, Wenbin Xu, Ming Wang, Yujia Shi, Yuliang State Grid Shandong Electric Power Company Information and Communication Company Jinan China State Grid Shandong Electric Power Company Jinan China School of Software Shandong University Jinan China Dareway Software Co. Ltd Jinan China

ISBN: (纸本)9781450385091

With the gradual expansion of the work order data of the electric customer service system, the reliance on manual experience has led to low processing timeliness, unable to effectively discover the true demands of customers, and thus unable to provide customers with high-quality solutions. Most of the current methods use deep learning techniques such as Principal Component Analysis and Neural Networks to analyze the semantics of work orders, but it is difficult to fully capture the semantic information hidden in the work order title and work order description, which leads to a decrease in the performance of solution recommendation. therefore, this paper proposes a Customer Preference-Aware Customer Service Solutions Recommendation (CPACS) model. In order to enhance the representation of work order data, the model uses customer preference information to "query"work order title sequence and work order description sequence separately, generates the temporal dependency representation of these two sequences, and then uses 1-D CNN and Transformer to capture the local and global temporal dependency information of these two sequences. then, a new information fusion method, Additive Conv-Transformer Skip (ACT-Skip), is proposed to fuse the local and global dependency information in the work order data to improve the solution recommendation performance. the final experiments show that the CPACS model can perform representation learning on work order data more effectively than the baseline model, thus realizing superior performance in the customer solution recommendation task. © 2021 ACM.

关键词： Principal component analysis

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Load balancing strategy for substation multi-service access edge computing terminal 9

Load balancing strategy for substation multi-service access ...

引用

9th IEEE Joint international information technology and Artificial Intelligence conference, ITAIC 2020

作者： Wang, Xuyan Xu, Han Chai, Ruijun Yan, Xin State Grid Jiangsu Power Co. Ltd. Nanjing Power Supply Branch China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China

With the significant increase in the requirements for power applications in all links of the power network, a large number of sensor deployments and more complex multi-service access scenarios have been formed. In this scenario, there are problems such as a waste of resources caused by the complexity of the network structure and the inability to efficiently use multiple services in a coordinated manner. To this end, edge computing technology is introduced to achieve efficient collaborative processing of multiple services in substations. At the same time, because of the imbalance of intervention, this paper considers two aspects of traffic load and computing load, and proposes a load balancing strategy for substation multi-service access edge computing terminal based on improved differential evolution multi-objective algorithm. Comparing the method proposed in this paper with the benchmark method, we can see that the method in this paper has a significant improvement in both internal and external load balancing and average delay. © 2020 IEEE.

关键词： Edge computing

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Utilization of Banana Peel and Carton Waste in Manufacturing of thermal Insulation Board 13

Utilization of Banana Peel and Carton Waste in Manufacturing...

引用

2021 IEEE 13th international conference on Humanoid, Nanotechnology, information technology, Communication and Control, Environment, and Management, HNICEM 2021

作者： Punongbayan, Allan M. Austria, Elino E. Hemedez, Alvin M. Romero, Rhoniel Gerald C. Duenas, April Jay B. Vanguardia, Sarah C. Agustin, Eugene V. Salazar, Edgardo C. Sugpatan, Jayson Alcaraz, Jesse Ian Lloyd College of Computing and Engineering Pamantasan Ng Cabuyao Cabuyao Philippines

ISBN: (纸本)9781665401678

As the temperature continues to rise due to global warming, the used of polyurethane thermal insulation foam board has become prevalent over the recent years. Despite its usefulness, it has a negative impact on our environment because the production of a pound of polyurethane foam board emits 3.7 pounds of carbon dioxide. Several studies have found that banana peel and carton waste can be used as a potential substitute in the production of polyurethane thermal insulation foam board due to its thermal resistivity. this study employed four different tests which includes flammability test, bend test, screw test and thermal capability test to determine the effectiveness of the product. the tests result showed that the best proportion to produce an affordable, lightweight and environment friendly insulation board is 31.33% banana peel, 31.33% carton waste, 23.50% banana resin, 7.8% polyvinyl acetate and 6% chlorinated rubber paint. the computed payback period is 4 years and 266 days, indicating that the business is feasible. © 2021 IEEE.

关键词： thermal insulation

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Software Tools for global Navigation Satellite System 4th

Software Tools for Global Navigation Satellite System

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4th international conference on information and Communication technology for Intelligent Systems, ICTIS 2020

作者： Soni, Riddhi Gajjar, Sachin Upadhyay, Manisha Fataniya, Bhupendra Nirma University AhmadabadGujarat India

ISBN: (纸本)9789811570773

global Navigation Satellite System (GNSS) is satellite navigation system for global coverage. GNSS system includes global Positioning System (GPS) from USA, global Navigation Satellite System (GLONASS) from Russia, Galileo from Europe, BeiDou from China, Quasi-Zenith Satellite System (QZSS) from Japan, and Indian Regional Navigation Satellite System (IRNSS) from India. A GNSS system consist of a constellation of satellites orbiting earth, continuously transmitting signals that enable users to determine their three-dimensional position with global coverage. Software tools for GNSS provide ability to analyze the navigation system performance. To evaluate the performance of any navigation system a software tool is required. Popular GNSS software tools are discussed in this paper like RTKLIB, GPSTk, gLAB and Bernese. the aim of this study is to compare the software tools for GNSS. these software tools are compared based on their features, support for different GNSS system, support for file formats, and support for operating system platform. Comparison can be helpful to select an appropriate tool as per the requirement. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： global positioning system

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