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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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A Novel Method of Detecting Microangioma in Retinal Image Based on Visual Characteristics

A Novel Method of Detecting Microangioma in Retinal Image Ba...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Xinrong Cao Hongkai Lin Hongliang Kang College of Computer and Control Engineering Minjiang University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

Diabetic retinopathy (DR), an ocular complication of diabetes could threaten vision, has become one of the common causes of blindness in adults' working age. The detection of microangioma (MA), the first feature and persists with the development of DR, is an important step in the diagnosis and follow-up of DR. However, due to complexity of retinal image and the randomness of abnormal lesions, the detection of MA is more susceptible to the influence of background texture noise and other structures in retinal image. A novel method for detecting MA based on visual features is proposed. It establishes an observation function and uses multi-range mean filtering to eliminate texture noise and vascular network. Then, the detection target is confirmed by the visual shape features. A series of tests in the different public retinal images databases show that the proposed method is effective and would be useful for computer-aided diagnosis of early screening of DR.

关键词： Retina Feature extraction Shape Biomedical imaging Blood vessels Visualization Filtering

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Detecting Interest-Factor Influenced Abnormal Evaluation of Teaching via Multimodal Embedding and Priori Knowledge Based Neural Network

Detecting Interest-Factor Influenced Abnormal Evaluation of ...

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

作者： Yu Mao Yifan Zhu Sifan Zhang Dexiu Zhang Fuquan Zhang Xiaozhong Fan School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Computer Science Minnan Normal University Zhangzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Student evaluations of teaching (SET) helps to quantitatively measure the teaching performance of classes. However, concerns arouse when the result of SET and students' final grades are highly positively correlated, which means the students may make the evaluation based on the final grades they take. For the purpose of avoiding retaliation and mutual reward rating, some methods based on statistics or rules are used to detect the presence of anomaly evaluation. Whereas, the problem is still challenging since the characteristics of this abnormal evaluation are implicit and multimodal. Besides, additional information such as the domain of subject, instructors' teaching style, and the reputation spread by students also requires corresponding prior knowledge to supplement the abnormal evaluation detection model. Therefore, in this paper, we proposed a multimodal embedding and prior knowledge based neural network to detect potential so called Interest Factor Influenced Abnormal Evaluation (IFAE). The method proposed in this paper uses SDNE and PV-DM to embed the evaluation network between students and teachers and the comment text of students. Taking into account the continuity of mutual evaluation between students and teachers during the teaching cycles and the different emphasis of students' comment texts on teachers, the features of students and teachers are comprehensively constructed. Then, the attention mechanism is used in the comment text to perform final prediction jointly with above features. The experiment result shows that our proposed model outperforms other state-of-art models which based on single type of features on F1 score by 9.25%.

关键词： Education Feature extraction Neural networks Data mining Knowledge based systems computer science Quality control

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Image Dehazing Network Based on Dilated Convolution Feature Extraction

Image Dehazing Network Based on Dilated Convolution Feature ...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics

作者： Ting Feng Zuoyong Li Chuansheng Wang Xinwei Chen Jiawei Wu Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Computer Science and Technology Harbin University of Science and Technology Harbin China College of Mathematics and Computer Science Fuzhou University Fuzhou China

Hazy weather brings a lot of inconvenience to peoples lives, such as transportation. Therefore, image dehazing is still an important focus point. To achieve image dehazing, we proposed a Deep Dilated Residual Haze Network (DDRHNet) based on self-encoder and self-decoder. The proposed mode is an end-to-end supervised method, which directly estimates image dehazing result instead of estimating the atmospheric light and the transmission in the unsupervised and classical atmospheric scattering model. The DDRHNet method obtains image dehazed result by finding the difference between the input hazy image and the output of haze-free image. Experimental results on an open image dehazing dataset called SOTS demonstrate the superiority of the proposed DDRHNet network.

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Intelligent spectrum learning for wireless networks with reconfigurable intelligent surfaces

arXiv

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arXiv 2021年

作者： Yang, Bo Cao, Xuelin Huang, Chongwen Yuen, Chau Qian, Lijun Renzo, Marco Di Engineering Product Development Pillar Singapore University of Technology and Design Singapore487372 Singapore Zhejiang Provincial Key Lab of information processing communication and networking Zhejiang University No.38 Zheda Road Hangzhou310007 China Department of Electrical and Computer Engineering CREDIT Center Prairie View A&M University Texas A&M University System Prairie ViewTX77446 United States Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 Rue Joliot-Curie Gif-sur-Yvette91192 France

Reconfigurable intelligent surface (RIS) has become a promising technology for enhancing the reliability of wireless communications, which is capable of reflecting the desired signals through appropriate phase shifts. However, the intended signals that impinge upon an RIS are often mixed with interfering signals, which are usually dynamic and unknown. In particular, the received signal-to-interference-plus-noise ratio (SINR) may be degraded by the signals reflected from the RISs that originate from non-intended users. To tackle this issue, we introduce the concept of intelligent spectrum learning (ISL), which uses an appropriately trained convolutional neural network (CNN) at the RIS controller to help the RISs infer the interfering signals directly from the incident signals. By capitalizing on the ISL, a distributed control algorithm is proposed to maximize the received SINR by dynamically configuring the active/inactive binary status of the RIS elements. Simulation results validate the performance improvement offered by deep learning and demonstrate the superiority of the proposed ISL-aided approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Analysis of Hotel Customer Satisfaction in Heilongjiang Province Based on OTA Data

Analysis of Hotel Customer Satisfaction in Heilongjiang Prov...

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International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Yan Liu Xiaowei Han Zhijie Zhao Yanrong Zhang Jiayuan Sun School of Computer and Information Engineering Harbin University of Commerce Harbin China School of Computer and Information Engineering Harbin University of Commerce Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin University of Commerce Harbin China

In order to uphold the concept of high-quality service development and improve the service quality of hotel industry in Heilongjiang Province, on the basis of customer satisfaction theory and combined with the development status of hotel industry in Heilongjiang Province, this paper uses keyword extraction algorithm, AHP analytic hierarchy process and emotional analysis technology to extract 12000 Hotel commentary data on OTA platform from equipment and facilities, services, transportation location, catering, price and health care. Six levels and 20 indicators are used to construct the evaluation model of hotel customer satisfaction in Heilongjiang Province. It is found that the factors that have significant impact on high-star hotels are relatively not obvious. The analysis results show that the overall customer satisfaction of hotels in Heilongjiang Province is relatively high, but there are still some improvements. For high-star hotels, we should pay more attention to equipment, facilities and service factors. Middle and low star hotels should pay attention to traffic location and price factors. Finally, according to the conclusion of the model and local characteristics, the corresponding countermeasures and suggestions are proposed.

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Deep Bilateral Retinex for Low-Light Image Enhancement

arXiv

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arXiv 2020年

作者： Liang, Jinxiu Xu, Yong Quan, Yuhui Wang, Jingwen Ling, Haibin Ji, Hui School of Computer Science and Engineering South China University of Technology Guangzhou China Peng Cheng Laboratory Shenzhen China Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information China Tencent AI Lab Shenzhen China Department of Computer Science Stony Brook University Strony BrookNY United States Department of Mathematics National University of Singapore Singapore

Low-light images, i.e. the images captured in low-light conditions, suffer from very poor visibility caused by low contrast, color distortion and significant measurement noise. Low-light image enhancement is about improving the visibility of low-light images. As the measurement noise in low-light images is usually significant yet complex with spatially-varying characteristic, how to handle the noise effectively is an important yet challenging problem in low-light image enhancement. Based on the Retinex decomposition of natural images, this paper proposes a deep learning method for low-light image enhancement with a particular focus on handling the measurement noise. The basic idea is to train a neural network to generate a set of pixel-wise operators for simultaneously predicting the noise and the illumination layer, where the operators are defined in the bilateral space. Such an integrated approach allows us to have an accurate prediction of the reflectance layer in the presence of significant spatially-varying measurement noise. Extensive experiments on several benchmark datasets have shown that the proposed method is very competitive to the state-of-the-art methods, and has significant advantage over others when processing images captured in extremely low lighting conditions. Copyright © 2020, The Authors. All rights reserved.

关键词： Image enhancement

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An Adaptive Model Update Object Tracking Algorithm based on DenseNet Features

An Adaptive Model Update Object Tracking Algorithm based on ...

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International Conference on Networking and Network Applications (NaNA)

作者： Sugang Ma Lei Zhang Lei Pu Xiaobao Yang Zhiqiang Hou Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an shaanxi China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an shaanxi China School of Information and Navigation Air Force Engineering University Xi'an shaanxi China

ISBN: (数字)9781728189543

ISBN: (纸本)9781728189550

In order to further improve the ability to deal with complex scenes, a visual tracking algorithm based on DenseNet features and model adaptive updating is proposed. Aiming to improve the feature representation ability of correlation filtering algorithm, a more discriminative DenseNet model is used as feature extractor. Meanwhile, in order to improve the long-term tracking performance, an adaptive model update strategy is proposed. The average peak correlation energy (APCE) based on response map is used to judge and evaluate the tracking result and the model is updated only when the confidence is high. The experimental results show that the proposed algorithm achieves higher success rate and accuracy compared to the other five related algorithms, and improves the robustness of object tracking in complex scenes such as occlusion and deformation.

关键词： Adaptation models Visualization Correlation Target tracking Feature extraction Object tracking Strain

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Removing Rain from Single Image Based on Details Preservation and Background Enhancement

Removing Rain from Single Image Based on Details Preservatio...

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IEEE International Conference on information Communication and Signal processing (ICICSP)

作者： Jiacheng Liu Shenghua Teng Zuoyong Li College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Processing and Intelligent Control Fujian Provincial Key Laboratory of Information Minjiang University Fuzhou China

Life is often accompanied by the bad weather. In the rainy days, the quality of images and videos acquired will be greatly degraded, affecting human observation and target detection and recognition in computer vision systems. In this paper, a single image rain removal algorithm is proposed based on details preservation and background enhancement. We first decompose the rainy image into low-frequency part and high-frequency part by low pass smoothing filter. Then, edge detection is performed on the low-frequency part to extract a mask, which will be used to capture rain-free image details from the high-frequency part. Next, the rain-free image details are superimposed on the low-frequency part to obtain an image without rains but well preserved details. Finally, the dark channel prior method is utilized to further alleviate the blur due to raining. Experiments on both synthetic and real rainy images demonstrate the effectiveness and efficiency of the proposed method.

关键词： Rain Image edge detection Filtering Videos Filtering algorithms Snow computer vision

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A Cascaded Co-evolutionary Model for Attribute Reduction and Classification Based on Coordinating Architecture with Bidirectional Elitist Optimization

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Chinese Journal of Electronics 2017年第1期26卷 13-21页

作者： DING Weiping WANG Jiandong LI Yuehua CHENG Xueyun School of Computer Science and Technology Nantong University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Provincial Key Laboratory for Computer Information Processing Technology Soochow University College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics

A cascaded co-evolutionary model for Attribute reduction and classification based on Coordinating architecture with bidirectional elitist optimization(ARC-CABEO) is proposed for the more practical applications. The regrouping and merging coordinating strategy of ordinary-elitist-role-based population is introduced to represent a more holistic cooperative co-evolutionary framework of different populations for attribute reduction. The master-slave-elitist-based subpopulations are constructed to coordinate the behaviors of different elitists, and meanwhile the elitist optimization vector with the strongest balancing between exploration and exploitation is selected out to expedite the bidirectional attribute co-evolutionary reduction process. In addition, two coupled coordinating architectures and the elitist optimization vector are tightly cascaded to perform the co-evolutionary classification of reduction subsets. Hence the preferring classification optimization goal can be achieved better. Some experimental results verify that the proposed ARC-CABEO model has the better feasibility and more superior classification accuracy on different UCI datasets, compared with representative algorithms.

关键词： Attribute reduction for classification Bidirectional elitist optimization Coordinating architecture Cascaded co-evolutionary model

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