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JobViz:Skill-driven visual exploration of job advertisements

Visual Informatics 2024年第3期8卷 18-28页

作者： Ran Wang Qianhe Chen Yong Wang Lewei Xiong Boyang Shen School of Journalism and Information Communication Huazhong University of Science and TechnologyWuhanChina School of Computer Science and Technology Huazhong University of Science and TechnologyWuhanChina College of Computing and Data Science Nanyang Technological UniversitySingaporeSingapore Wuhan National Laboratory for Optoelectronics WuhanChina Philosophy and Social Science Laboratory of Big Data and National Communication Strategy Ministry of EducationWuhanChina

Online job advertisements on various job portals or websites have become the most popular way for people to find potential career opportunities ***,the majority of these job sites are limited to offering fundamental filters such as job titles,keywords,and compensation *** often poses a challenge for job seekers in efficiently identifying relevant job advertisements that align with their unique skill sets amidst a vast sea of ***,we propose well-coordinated visualizations to provide job seekers with three levels of details of job information:a skill-job overview visualizes skill sets,employment posts as well as relationships between them with a hierarchical visualization design;a post exploration view leverages an augmented radar-chart glyph to represent job posts and further facilitates users’swift comprehension of the pertinent skills necessitated by respective positions;a post detail view lists the specifics of selected job posts for profound analysis and *** using a real-world recruitment advertisement dataset collected from 51Job,one of the largest job websites in China,we conducted two case studies and user interviews to evaluate *** results demonstrated the usefulness and effectiveness of our approach.

关键词： Visual exploration Job advertisements Skill-driven

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DFGR: Deep Feature Graph Representation for Predicting Knee Osteoarthritis 4

DFGR: Deep Feature Graph Representation for Predicting Knee ...

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4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Xu, Tao Dai, Dawei Wang, Yuqi Li, Gen Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing Chongqing China Shanghai Jiaotong University School of Medicine Department of Orthopedics Ruijin Hospital Shanghai China

ISBN: (纸本)9798350394375

Knee osteoarthritis (KOA) is a common joint disease that severely affects the normal lives of patients. In clinical practice, the severity of KOA is evaluated by observing the X-ray images of the knee joint, which is highly dependent on the subjective experience of the doctor. In this study, we developed a novel deep CNN model that integrates structural information processing to predict KOA severity automatically. Specifically, (1) The knee joint regions of the original X-ray images are segmented using automatic detection;(2) We employed a pre-trained deep CNN models to extract deep features of key regions, and construct their corresponding graph representations;(3) A graph attention network (GAT) was designed as a fine-tuning module to build a KOA prediction model. Experiments demonstrate that our method significantly improves the performance in multiple aspects. In addition, our method demonstrates good interpretability. Codes are available at https://***/ddw2AIGROUP2CQUPT/DFGR © 2024 IEEE.

关键词： Forecasting

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Topic Mining of Alzheimer's Disease Field Based on BERTopic 5

Topic Mining of Alzheimer's Disease Field Based on BERTopic

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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Shen, Juan Mariano, Vladimir Y. College of Computing and Information Technologies National University Manila Philippines School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei230088 China

ISBN: (纸本)9798350357950

Alzheimer's disease is the most common progressive neurodegenerative disease in clinical practice, and it is also one of the main causes of disability and death in the elderly. Alzheimer's disease has become a major brain disease that seriously endangers the health of the elderly and affects the sustainable development of society and economy. As the number of people with Alzheimer's disease (AD) rises, the research on AD continues to heat up. Exploring the topic of literature research on Alzheimer's disease is intended to better sort out the research in this field. In this paper, the topic modeling method of BERTopic was applied to the literature on Alzheimer's disease research from 2000 to 2019, to identify the main research directions in this field, and to analyze the changing trend of topic intensity. Twenty research topics in this area were finally identified, and research on amyloid beta and tau was found to have declined significantly since 2016. In addition, this paper also evaluated and compared the quality of topic extraction of two models, LDA and BERTopic, and verified that the accuracy and effectiveness of topic mining results of Alzheimer's disease research based on BERTopic were superior to those generated by LDA model. © 2023 IEEE.

关键词： Alzheimer's disease BERTopic Deep learning Topic mining

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The Numbers’ Sign Language Detection Based on Deep Learning 3

The Numbers’ Sign Language Detection Based on Deep Learning

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3rd International Conference on Signal Image Processing and Communication, ICSIPC 2023

作者： Lian, Li Sheng, Tang School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China College of Computing and Information Technologies Philippines National University Manila Philippines

ISBN: (纸本)9781510670945

This paper builds an end-to-end custom object detection model that could translate Chinese numbers’ sign language in real time based on deep leaning. The main work of this paper is as follows, collect images for deep learning using OpenCV, label images for sign language detection using LabelImg, setup Tensorflow Object Detection pipeline configuration, and use transfer learning to train a deep learning model. At last, detect sign language in real time using OpenCV. © 2023 SPIE.

关键词： Object detection

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Student Behavior Recognition Using Video-Based Deep Learning 5

Student Behavior Recognition Using Video-Based Deep Learning

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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Shi, Xiangui Mariano, Vladimir Y. College of Computing and Information Technologies National University Manila Philippines School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei230088 China

ISBN: (纸本)9798350357950

The student classroom behavior analysis system based on deep learning can automatically monitor and analyze students' actions and behaviors in the classroom. This method utilizes a camera to collect classroom teaching videos, and uses the Yolo v5 algorithm to track students' movement information in real-time, in order to identify students' classroom behavior patterns. And evaluate the learning status of students. The design of this system aims to provide teachers with more information about students' learning situations and apply AI technology to education and teaching. A student classroom behavior analysis system based on deep learning can help teachers better understand students' learning situations and needs, improve classroom teaching effectiveness, and provide more effective learning guidance and feedback for students. © 2023 IEEE.

关键词： AI Education Classroom behavior testing Deep learning Yolo-v5

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Model Predictive Current Control for Asymmetric Six-phase Permanent Magnet Motors with Less Computation and Optimized Voltage Vector Magnitude

Model Predictive Current Control for Asymmetric Six-phase Pe...

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2022 International Conference on Smart Energy and Electrical Engineering, SEEE 2022

作者： Sun, Jinyu Chen, Fubing Huai'An Key Laboratory of Big Data Intelligent Computing and Analysis China The School of Computer Science and Technology Huaiyin Normal University Huaian223001 China

For the sake of the fixed magnitude of candidate control sets, the conventional model predictive current control (MPCC) for asymmetric six-phase permanent magnet motors suffers from huge harmonic currents. This article designs a novel MPCC that aims to improve operational performance. First, virtual-voltage vector (VV) sets are synthesized as the new candidate vectors to restrain harmonic currents. Then, a simplified method for evaluating voltage vectors is used to alleviate the computation in the enumeration process. Moreover, the duty cycle modulation with the deadbeat solution is introduced to optimize the magnitude of the VV. The modulation range can be expanded by utilizing null vectors, which will significantly reduce the torque fluctuations and flux ripples. The experiments are conducted to verify the designed control strategy. © Published under licence by IOP Publishing Ltd.

关键词： Vectors

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Design and Research of a Dual and Bidirectional Certificateless Identity Authentication Scheme Based on Hybrid SM Series Algorithms 23

Design and Research of a Dual and Bidirectional Certificatel...

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11th International Conference on Computer and Communications Management, ICCCM 2023

作者： Lu, Hejun Juanatas, Roben A. College of Computing and Information Technologies National University Manila Philippines School of Big Data and Artificial Intelligence Anhui Xinhua University Anhui Hefei China

ISBN: (纸本)9798400707735

Aiming at security issues such as user password leakage, password cracking, spoofing, replay attack, man-in-the-middle attack, and server-side attack that may occur during the identity authentication process of instant messaging system users, this paper uses a hybrid algorithms of SM2 and SM3 to design a set of a dual and bidirectional certificateless authentication scheme based on SM2, SM3 and random salt value. This solution includes a two-way password authentication security model based on SM2 and SM3+random salt value, and an intelligent secret medium challenge-response two-way authentication security model based on SM2 and SM3 and random salt value. The dual bidirectional security model implemented by using the hybrid SM series algorithms can achieve the full process of ciphertext transmission, storage, and verification during the authentication process, which can effectively prevent the leakage of personal information in the authentication process, and can effectively prevent fraud, man-in-the-middle attacks, password cracking, and account theft during the authentication process. © 2023 ACM.

关键词： Authentication

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Prediction of O2O coupon usage based on GRU and RNN integrated model 4

Prediction of O2O coupon usage based on GRU and RNN integrat...

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4th International Conference on Computer Science and Management Technology, ICCSMT 2023

作者： Yao, Lisha Abisado, Mideth College of Computing and Information Technologies National University Manila Philippines School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei Anhui China

ISBN: (纸本)9798400709517

With the rapid development of Internet, O2O (Online to Offline) has developed rapidly. O2O coupons are widely used because of their low production and dissemination costs, and the dissemination effect can be accurately quantified. Faced with a large number of electronic coupons, there are some problems, such as the accuracy of the target population, the choice of the time and the type of electronic coupons. There is a certain time series relationship in the use behavior of consumer coupons. To solve the problem of data dependence on time, fully consider the influence of information before and after, introduce the memory module of door-controlled cycle unit into recursive neural network, and propose an integrated GRU and RNN model based on time series and memory as a prediction model. Based on the integrated model of GRU and RNN, this paper proposes an O2O coupon usage prediction method. According to the user's consumption in a certain date interval, it is predicted whether the O2O coupons will be used in the next 15 days. The experimental results show that the model proposed in this paper has strong advantages. © 2023 ACM.

关键词： Time series

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Glare-SNet: Unsupervised Glare Suppression Balance Network 27th

Glare-SNet: Unsupervised Glare Suppression Balance Network

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Pei Zuo, Chengyu Wei, Wangjuan Pan, Xiaoying Wang, Zhanhao School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an China Shaanxi Key Laboratory of Intelligent Media Computing and interaction Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an China

ISBN: (纸本)9783031781681

In light of the problems associated with glare and halo effects in low-light images, as well as the inadequacy of existing processing algorithms in handling details, a glare suppression balance network based on unsupervised learning has been designed and implemented in this study. A network that perceives brightness through light decomposition has been proposed, and an illumination learning network has been designed. The idea of integration has been employed to form an unsupervised glare suppression balance network, aiming to tackle the issues of glare and the accompanying halo effects. Through objective and subjective evaluations of real images, it has been proven that the method introduced in this paper surpasses other methods in suppressing glare and enhancing the overall quality of low-light images. Furthermore, it has been shown to exhibit good performance and effectiveness in target detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Policy Network-Based Dual-Agent Deep Reinforcement Learning for Multi-Resource Task Offloading in Multi-Access Edge Cloud Networks

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China Communications 2024年第4期21卷 53-73页

作者： Feng Chuan Zhang Xu Han Pengchao Ma Tianchun Gong Xiaoxue School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of Intelligent Communication and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China School of Information Engineering Guangdong University of TechnologyGuangzhou 510006China

The Multi-access Edge Cloud(MEC) networks extend cloud computing services and capabilities to the edge of the networks. By bringing computation and storage capabilities closer to end-users and connected devices, MEC networks can support a wide range of applications. MEC networks can also leverage various types of resources, including computation resources, network resources, radio resources,and location-based resources, to provide multidimensional resources for intelligent applications in 5/***, tasks generated by users often consist of multiple subtasks that require different types of resources. It is a challenging problem to offload multiresource task requests to the edge cloud aiming at maximizing benefits due to the heterogeneity of resources provided by devices. To address this issue,we mathematically model the task requests with multiple subtasks. Then, the problem of task offloading of multi-resource task requests is proved to be NP-hard. Furthermore, we propose a novel Dual-Agent Deep Reinforcement Learning algorithm with Node First and Link features(NF_L_DA_DRL) based on the policy network, to optimize the benefits generated by offloading multi-resource task requests in MEC networks. Finally, simulation results show that the proposed algorithm can effectively improve the benefit of task offloading with higher resource utilization compared with baseline algorithms.

关键词： benefit maximization deep reinforcement learning multi-access edge cloud task offloading

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