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arXiv 2018年

作者： Li, Zhi Zhao, Hongke Liu, Qi Huang, Zhenya Mei, Tao Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China School of Software Engineering University of Science and Technology of China JD AI Research

In the modern e-commerce, the behaviors of customers contain rich information, e.g., consumption habits, the dynamics of preferences. Recently, session-based recommendations are becoming popular to explore the temporal characteristics of customers’ interactive behaviors. However, existing works mainly exploit the short-term behaviors without fully taking the customers’ long-term stable preferences and evolutions into account. In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users’ historical stable preferences and present consumption motivations. Specifically, BINN contains two main components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning. Firstly, a novel item embedding method based on user interactions is developed for obtaining an unified representation for each item. Then, with the embedded items and the interactive behaviors over item sequences, BINN discriminatively learns the historical preferences and present motivations of the target users. Thus, BINN could better perform recommendations of the next items for the target users. Finally, for evaluating the performances of BINN, we conduct extensive experiments on two real-world datasets, i.e., Tianchi and JD. The experimental results clearly demonstrate the effectiveness of BINN compared with several state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Enhancing person-Job fit for talent recruitment: An ability-aware neural network approach

arXiv

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arXiv 2018年

作者： Qin, Chuan Zhu, Hengshu Xu, Tong Zhu, Chen Jiang, Liang Chen, Enhong Xiong, Hui Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Baidu Talent Intelligence Center Baidu Inc Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology an Application China

The wide spread use of online recruitment services has led to information explosion in the job market. As a result, the recruiters have to seek the intelligent ways for Person-Job Fit, which is the bridge for adapting the right job seekers to the right positions. Existing studies on Person-Job Fit have a focus on measuring the matching degree between the talent qualification and the job requirements mainly based on the manual inspection of human resource experts despite of the subjective, incomplete, and inefficient nature of the human judgement. To this end, in this paper, we propose a novel end-to-end Ability-aware Person-Job Fit Neural Network (APJFNN) model, which has a goal of reducing the dependence on manual labour and can provide better interpretation about the fitting results. The key idea is to exploit the rich information available at abundant historical job application data. Specifically, we propose a word-level semantic representation for both job requirements and job seekers’ experiences based on Recurrent Neural Network (RNN). Along this line, four hierarchical ability-aware attention strategies are designed to measure the different importance of job requirements for semantic representation, as well as measuring the different contribution of each job experience to a specific ability requirement. Finally, extensive experiments on a large-scale real-world data set clearly validate the effectiveness and interpretability of the APJFNN framework compared with several baselines. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Automated assessment of necrosis tumor ratio in colorectal cancer using an artificial intelligence-based digital pathology analysis

Medicine Advances

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Medicine Advances 2023年第1期1卷

作者： Huifen Ye Yunrui Ye Yiting Wang Tong Tong Su Yao Yao Xu Qingru Hu Yulin Liu Changhong Liang Guangyi Wang Ke Zhao Xinjuan Fan Yanfen Cui Zaiyi Liu Department of Radiology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou China The Second School of Clinical Medicine Southern Medical University Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou China Contribution: Conceptualization (equal) Methodology (equal) Visualization (equal) Writing - original draft (equal) Contribution: Data curation (equal) Investigation (equal) Validation (equal) Writing - original draft (equal) Department of Pathology The Sixth Affiliated Hospital of Sun Yat-sen University Guangzhou China Contribution: Data curation (equal) Investigation (equal) Validation (equal) Formal analysis (equal) Department of Radiology Fudan University Shanghai Cancer Center Shanghai China Department of Oncology Shanghai Medical College Shanghai China Contribution: Data curation (equal) Investigation (equal) Validation (equal) Department of Pathology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou China Contribution: Formal analysis (equal) Contribution: Formal analysis (equal) Supervision (equal) Writing - review & editing (equal) Guangdong Cardiovascular Institute Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China Xinjuan Fan Department of Pathology The Sixth Affiliated Hospital of Sun Yat-Sen University 26 Erheng Road Guangzhou 510655 China. Yanfen Cui Department of Radiology Shanxi Cancer Hospital Shanxi Medical University No. 3 Xinjie West Alley Taiyuan 030013 China. Zaiyi Liu Department of Radiology Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences) Southern Medical University 106 Zhongshan Er Road Guangzhou 510080 China. Contribution: Formal analysis (equal) Methodology (equal) Writing - original draft (equal) Writing - review & editing (equal) Ke Zhao Depa

Background With the advance in digital pathology and artificial intelligence (AI)-powered approaches, necrosis is proposed as a marker of poor prognosis in colorectal cancer (CRC). However, most previous studies quantified necrosis merely as a tissue type and patch-level segmentation. Thus, it was worth exploring and validating the prognostic and predictive value of necrosis proportion with a pixel-level segmentation in large multicenter cohorts. Methods A semantic segmentation model was trained with 12 tissue types labeled by pathologists. Segmentation was performed using the U-net model with a subsequently derived necrosis tumor ratio (NTR). We proposed the NTR score (NTR-low or NTR-high) to evaluate the prognostic and predictive value of necrosis for disease-free survival (DFS) and overall survival (OS) in the development ( N = 443) and validation cohorts ( N = 333) using 75% as a threshold. Results The 2-category NTR was an independent prognostic factor and NTR-low was associated with significant prolonged DFS (unadjusted HR for high vs. low 1.72 [95% CI 1.19–2.49] and 1.98 [1.22–3.23] in the development and validation cohorts). Similar trends were observed for OS. The prognostic value of NTR was maintained in the multivariate analysis for both cohorts. Furthermore, a stratified analysis showed that NTR-high was a high risk with adjuvant chemotherapy for OS in stage II CRC ( p = 0.047). Conclusion AI-based pixel-level quantified NTR has a stable prognostic value in CRC associated with unfavorable survival. Additionally, adjuvant chemotherapy provided survival benefits for patients with a high NTR score in stage II CRC.

关键词： artificial intelligence colorectal cancer digital pathology necrosis whole-slide images

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Research on the Brain-inspired Cross-modal Neural Cognitive Computing Framework

arXiv

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arXiv 2018年

作者： Liu, Yang Key Laboratory of Big Data Analysis and Processing of Henan province Intelligent Technology and Application Engineering Research Centre of Henan province College of Computer Science and Information Engineering Henan University Kaifeng475004 China

To address modeling problems of brain-inspired intelligence, this thesis is focused on researching in the semantic-oriented framework design for multimedia and multimodal information. The Multimedia Neural Cognitive Computing (MNCC) model was designed based on the nervous mechanism and cognitive architecture. Furthermore, the semantic-oriented hierarchical Cross-modal Neural Cognitive Computing (CNCC) framework was proposed based on MNCC model, and formal description and analysis for CNCC framework was given. It would effectively improve the performance of semantic processing for multimedia and cross-modal information, and has far-reaching significance for exploration and realization brain-inspired computing. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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A Ship Draft Line Detection Method Based on Image Processing and Deep Learning

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Zhong Wang Peibei Shi Chao Wu School of Computer Science and Technology Hefei Normal University No. 1688 Lianhua Road Hefei Anhui China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China No. 96 Jinzhai Road Hefei Anhui China Network and Information Center University of Science and Technology of China No. 96 Jinzhai Road Hefei Anhui China

The traditional ship draft detection method mainly adopts the method of the human eye observation, which has the problems of large precision error and slow detection speed. Aiming at this problem, this paper proposes a ship draft line detection method based on image processing and deep learning. The color image is first transformed into a grayscale image, and the corner detection is carried out by Harris operator. Contour map is obtained using Canny operator for edge detection, and line segment detection is performed using Hough algorithm. The water surface is determined by calculating the lowest position of the corner point. Then the grayscale image is binarized to increase the contrast of the image, the digital area is determined by the corner positions of the highest and lowest points, and finally the convolutional neural network is used to perform character recognition and calculate the draft of the ship. Tested on videos of various scenes, the error of the proposed method is less than 1cm.

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Designing preamplifier for sensing atmospheric electrostatic field strength via supercapacitive sensor

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Journal of Physics: Conference Series 2020年第1期1607卷

作者： Changhao Wu Yi Xiong Wei Zeng National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information Engineering Anhui University Hefei 230601 People's Republic of China Science and Technology Institute Hubei Key Laboratory of Biomass Fibers and Eco-Dyeing and Finishing Wuhan Textile University Wuhan 430073 People's Republic of China School of Physics and Technology MOE Key Laboratory of Artificial Micro- and Nano-Structures and Center for Electron Microscopy Wuhan University Wuhan 430072 People's Republic of China

In order to effectively obtain the signal from sensor, the analogy signal needs to be amplified and then converted into a digital signal for matching to the sensor characteristics. With a supercapacitive electric field sensor based on graphene aerogel, the response current signal from the electric field sensor is weak and unstable. Herein, a high gain and low noise preamplifier is developed, and an amplifier circuit with double T-type feedback network is proposed to reduce the Johnson noise for the amplifier. This design can reduce the thermal noise of resistance by using the smaller resistance under the same gain, and it can effectively reduce the interference of peak noise by adding the feedback capacitance, so as to improve the detection accuracy. The simulation results show that under the same gain condition, the Johnson noise can be reduced by 46% and the detection accuracy can be improved by 12% compared with the traditional T-type feedback network.

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A Fatigue Driving Detection Method based on Deep Learning and Image Processing

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Journal of Physics: Conference Series 2020年第1期1575卷

作者： Zhong Wang Peibei Shi Chao Wu School of Computer Science and Technology Hefei Normal University No. 1688 Lianhua Road Hefei Anhui China. Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China No. 96 Jinzhai Road Hefei Anhui China. Network and Information Center University of Science and Technology of China No. 96 Jinzhai Road Hefei Anhui China

Driving fatigue is one of the important causes of traffic accidents. It is of great significance to study fatigue driving detection algorithms to improve human life and property safety. This paper proposes a fatigue driving detection method based on deep learning and image processing. First, the driver's face image is obtained in real time through the camera and the face image is detected using the MTCNN model. Next the image processing is performed on the face image, including three steps: grayscale processing, binarization processing, and human eye detection. Then we check the legitimacy of the human eye image and calculate the eye closure rate, and finally use the PERCLOS principle to analyze the fatigue state of the driver. The experimental results show that the proposed method has high detection rate and low false alarm rate, and has strong practicality.

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Enhancing network embedding with auxiliary information: An explicit matrix factorization perspective

arXiv

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arXiv 2017年

作者： Guo, Junliang Xu, Linli Huang, Xunpeng Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China

Recent advances in the field of network embedding have shown the low-dimensional network representation is playing a critical role in network analysis. However, most of the existing principles of network embedding do not incorporate auxiliary information such as content and labels of nodes exibly. In this paper, we take a matrix factorization perspective of network embedding, and incorporate structure, content and label information of the network simultaneously. For structure, we validate that the matrix we construct preserves high-order proximities of the network. Label information can be further integrated into the matrix via the process of random walk sampling to enhance the quality of embedding in an unsupervised manner, i.e., without leveraging downstream classifiers. In addition, we generalize the Skip-Gram Negative Sampling model to integrate the content of the network in a matrix factorization framework. As a consequence, network embedding can be learned in a unified framework integrating network structure and node content as well as label information simultaneously. We demonstrate the efficacy of the proposed model with the tasks of semi-supervised node classification and link prediction on a variety of real-world benchmark network datasets. Copyright © 2017, The Authors. All rights reserved.

关键词： Network embeddings

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A Framework for Passengers Demand Prediction and Recommendation

A Framework for Passengers Demand Prediction and Recommendat...

引用

IEEE International Conference on Services Computing (SCC)

作者： Kai Zhang Zhiyong Feng Shizhan Chen Keman Huang Guiling Wang Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China School of Computer Science and Technologv Tianjin University Tianjin China School of Computer Software Tianjin University Tianjin China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing China

With the rapid development of mobile internet and wireless network technologies, more and more people use the mobile app to call a taxicab to pick them up. Therefore, understanding the passengers' travel demand becomes crucial to improve the utilization of the taxicabs and reduce their cost. In this paper, based on spatio-temporal clustering, we propose a demand hotspots prediction framework to generate recommendation for taxi drivers. Specially, an adaptive prediction approach is presented to demand hotspots and their hotness, and then combing the driver's location and the hotness, top candidates are recommended and visually presented to drivers. Based on the dataset provided by CAR INC., the experiment shows that our approach gains a significant improvement in hotspots prediction and recommendation, with 15.21% improvement on average f-measure for prediction and 79.6% hit ratio for recommendation.

关键词： Public transportation Trajectory Vehicles Global Positioning System Mobile communication Clustering algorithms Filtering algorithms

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Using Hourly Measurements to Explore the Role of Secondary Inorganic Aerosol in PM_(2.5)during Haze and Fog in Hangzhou, China

引用

Advances in Atmospheric Sciences 2014年第6期31卷 1427-1434页

作者： Roeland Cornelis JANSEN SHI Yang CHEN Jianmin HU YunJie XU Chang HONG Shengmao LI Jiao ZHANG Min Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention Department of Environmental Science & Engineering Fudan University Hangzhou Municipal Environmental Monitoring Center Key Laboratory of Data Analysis and Application the First Institute of OceanographyState Oceanic Administration

This paper explores the role of the secondary inorganic aerosol （SIA） species ammonium,NH4＋,nitrate,NO3-,and sulfate,SO24-,during haze and fog events using hourly mass concentrations of PM2.5 measured at a suburban site in Hangzhou,China.A total of 546 samples were collected between 1 April and 8 May *** samples were analyzed and classified as clear,haze or fog depending on visibility and relative humidity （RH）.The contribution of SIA species to PM2.5 mass increased to ～50％ during haze and *** mass contribution of nitrate to PM2.5 increased from 11％ during clear to 20％ during haze *** mass exceeded sulfate mass during haze,while near equal concentrations were observed during fog *** role of RH on the correlation between concentrations of SIA and visibility was examined,with optimal correlation at 60％-70％ *** total acidity during clear,haze and fog periods was 42.38,48.38 and 45.51 nmol m-3,respectively,indicating that sulfate,nitrate and chloride were not neutralized by ammonium during any *** nitrate to sulfate molar ratio,as a function of the ammonium to sulfate molar ratio,indicated that nitrate formation during fog started at a higher ammonium to sulfate molar ratio compared to clear and haze *** haze and fog,the nitrate oxidation ratio increased by a factor of 1.6-1.7,while the sulfur oxidation ratio increased by a factor of 1.2-1.5,indicating that both gaseous NO2 and SO2 were involved in the reduced visibility.

关键词： haze secondary inorganic aerosol PM2.5 Yangtze River Delta

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