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JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer

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arXiv 2024年

作者： Guan, ZhiHao Yang, Jia-Qi Yang, Yang Zhu, Hengshu Li, Wenjie Xiong, Hui the Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Career Science Lab BOSS Zhipin Beijing100028 China the Hong Kong Polytechnic University Hong Kong the Artificial Intelligence Thrust The Hong Kong University of Science and Technology Guangzhou China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

Job recommendation aims to provide potential talents with suitable job descriptions (JDs) consistent with their career trajectory, which plays an essential role in proactive talent recruitment. In real-world management scenarios, the available JD-user records always consist of JDs, user profiles, and click data, in which the user profiles are typically summarized as the user’s skill distribution for privacy reasons. Although existing sophisticated recommendation methods can be directly employed, effective recommendation still has challenges considering the information deficit of JD itself and the natural heterogeneous gap between JD and user profile. To address these challenges, we proposed a novel skill-aware recommendation model based on the designed semantic-enhanced transformer to parse JDs and complete personalized job recommendation. Specifically, we first model the relative items of each JD and then adopt an encoder with the local-global attention mechanism to better mine the intra-job and inter-job dependencies from JD tuples. Moreover, we adopt a two-stage learning strategy for skill-aware recommendation, in which we utilize the skill distribution to guide JD representation learning in the recall stage, and then combine the user profiles for final prediction in the ranking stage. Consequently, we can embed rich contextual semantic representations for learning JDs, while skill-aware recommendation provides effective JD-user joint representation for click-through rate (CTR) prediction. To validate the superior performance of our method for job recommendation, we present a thorough empirical analysis of large-scale real-world and public datasets to demonstrate its effectiveness and interpretability. Copyright © 2024, The Authors. All rights reserved.

关键词： User profile

The Role of AI in Healthcare Policy Development and Management

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The Role of AI in Healthcare Policy Development and Manageme...

Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Sapna Yadav Pravin Mane Virendra Kumar Swarnkar Shital kumar Rawandale Alaknanda S. Patil Kiran S. Katke Niyati Bhat Entrepreneurship Mindset Curriculum State Council of Educational Research and Training Delhi Bharati Vidyapeeth (Deemed to be University) Institute of Management and Entrepreneurship Development Pune Department of Computer Science & Engineering Bharti Vishwavidyalaya Durg Chhattisgarh Dean Industry Institute Interaction PCET's Pimpri Chinchwad College of Engineering Pune Maharashtra India E&TC department JSPM NTC Pune Department of Computer Engineering Sanjivani College of engineering Kopergaon Ahmednagar Software Developer Master of Science california state university long beach CA USA

This study looks at how artificial intelligence (AI) is incorporated into healthcare policy, with an emphasis on how it affects administrative procedures, treatment optimization, especially clinical choice support. Using a deductive methodology and interpretive thinking as a philosophy, an exploratory design with secondary data collecting was used. In the present-day environment, AI is being used more frequently in healthcare legislation, especially within medical settings. Clinical decision-making systems driven by AI have shown to offer a great promise for lowering diagnostic mistakes and increasing treatment precision. Improving patient outcomes through individualized AI-driven treatments for treatment improvement appears promising. But there are issues with algorithm prejudices as well as information privacy, among other ethical and legal issues. Establishing thorough guidelines, ongoing education, and auditing procedures are among the suggestions that are made. In order to guarantee fair and efficient integration of health care legislation, future research should prioritize longitudinal research and improving artificial intelligence algorithms

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Cardiac Evidence Backtracking for Eating Behavior Monitoring using Collocative Electrocardiogram Imagining

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arXiv 2025年

作者： Zhang, Xu-Lu Yang, Zhen-Qun Jiang, Dong-Mei Liao, Ga Li, Qing Jain, Ramesh Wei, Xiao-Yong College of Computer Science Sichuan University Chengdu610065 China Department of Biomedical Engineering Chinese University of Hong Kong Kowloon Hong Kong Center for Artificial Intelligence Peng Cheng Lab Shenzhen518055 China School of Computer Science Northwestern Polytechnical University Xi’an China State Key Laboratory of Oral Diseases National Clinical Research Center for Oral Diseases West China Hospital of Stomatology Sichuan University Chengdu610041 China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Institute for Future Health University of California IrvineCA United States Donald Bren School of Information and Computer Sciences University of California IrvineCA United States

Eating monitoring has remained an open challenge in medical research for years due to the lack of non-invasive sensors for continuous monitoring and the reliable methods for automatic behavior detection. In this paper, we present a pilot study using the wearable 24-hour ECG for sensing and tailoring the sophisticated deep learning for ad-hoc and interpretable detection. This is accomplished using a collocative learning framework in which 1) we construct collocative tensors as pseudo-images from 1D ECG signals to improve the feasibility of 2D image-based deep models;2) we formulate the cardiac logic of analyzing the ECG data in a comparative way as periodic attention regulators so as to guide the deep inference to collect evidence in a human comprehensible manner;and 3) we improve the interpretability of the framework by enabling the backtracking of evidence with a set of methods designed for Class Activation Mapping (CAM) decoding and decision tree/forest generation. The effectiveness of the proposed framework has been validated on the largest ECG dataset of eating behavior with superior performance over conventional models, and its capacity of cardiac evidence mining has also been verified through the consistency of the evidence it backtracked and that of the previous medical studies. © 2025, CC BY.

关键词： Decision trees

Effects of Architectural Interventions on Psychological, Cognitive, Social, and Pro-Environmental Aspects of Occupant Well-Being: Results from an Immersive Online Study

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SSRN

SSRN 2023年

作者： Bianchi, Eva Bencharit, Lucy Zhang Murnane, Elizabeth L. Altaf, Basma Douglas, Isabella P. Landay, James A. Billington, Sarah L. Department of Civil & Environmental Engineering Stanford University United States Department of Psychology and Child Development California Polytechnic State University United States School of Engineering Dartmouth College United States Department of Computer Science Stanford University United States Institute for Human-Centered Artificial Intelligence Stanford University United States Woods Institute for the Environment Stanford University United States

The impact of the built environment on human well-being is a growing area of research due to the increasing amount of time people spend indoors. This paper presents a novel research approach utilizing an immersive online survey to evaluate the influence of built features on four well-being outcomes: sense of belonging, stress, creativity, and pro-environmental concern. The survey employs pictures and videos to immerse participants (N = 411) in different settings, focusing on the effects of natural versus artificial materials, the presence versus absence of windows, and photographic decor with representations of diverse versus non-diverse people. Results indicate statistically significant impacts of these built features on our well-being metrics. Specifically, the presence of windows promotes a stronger sense of belonging compared to the absence of windows. Natural materials result in lower negative affect, one important aspect of stress, compared to artificial materials, while the presence of windows result in higher positive affect compared to the absence of windows. Decor with representations of diverse people enhances self-reported creativity and divergent creativity scores, compared to non-diverse decor. Furthermore, the study demonstrates that immersive online survey approaches serve as a viable experimental paradigm to test hypotheses about the built environment. In fact, such approaches offer advantages in terms of streamlining administration time, financial costs, utilization of human resources, and flexibility in systematically manipulating the study space. These findings highlight the important influence that the built environment has on well-being outcomes and the potential of immersive online surveys in advancing research in this field. © 2023, The Authors. All rights reserved.

关键词： Crowdsourcing

Leveraging Intermediate Artifacts to Improve Automated Trace Link Retrieval

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Leveraging Intermediate Artifacts to Improve Automated Trace...

International Conference on software Maintenance (ICSM)

作者： Alberto D. Rodriguez Jane Cleland-Huang Davide Falessi Computer Science and Software Eng. California Polytechnic State University CA USA Computer Science and Eng. University of Notre Dame Notre Dame IN USA Civil Eng. and Computer Science University of Rome Tor Vergata Rome Italy

software traceability establishes a network of connections between diverse artifacts such as requirements, design, and code. However, given the cost and effort of creating and maintaining trace links manually, researchers have proposed automated approaches using information retrieval techniques. Current approaches focus almost entirely upon generating links between pairs of artifacts and have not leveraged the broader network of interconnected artifacts. In this paper we investigate the use of intermediate artifacts to enhance the accuracy of the generated trace links - focusing on paths consisting of source, target, and intermediate artifacts. We propose and evaluate combinations of techniques for computing semantic similarity, scaling scores across multiple paths, and aggregating results from multiple paths. We report results from five projects, including one large industrial project. We find that leveraging intermediate artifacts improves the accuracy of end-to-end trace retrieval across all datasets and accuracy metrics. After further analysis, we discover that leveraging intermediate artifacts is only helpful when a project's artifacts share a common vocabulary, which tends to occur in refinement and decomposition hierarchies of artifacts. Given our hybrid approach that integrates both direct and transitive links, we observed little to no loss of accuracy when intermediate artifacts lacked a shared vocabulary with source or target artifacts.

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Network security situation prediction method based on support vector machine optimized by artificial bee colony algorithms

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Journal of computers (Taiwan) 2021年第1期32卷 144-153页

作者： Ke, Gang Chen, Ruey-Shun Chen, Yeh-Cheng Yeh, Jyh-Haw Department of Computer Engineering Dongguan Polytechnic Dongguan Guangdong China Department of Computer Science University of California DavisCA United States Department of Computer Science Boise State University Idaho United States

The validity and accuracy of the network security situation prediction algorithm is of great significance to network security. Aiming at the shortcomings of the basic artificial bee colony algorithm, such as easy to fall into the local optimal solution and slow convergence in the late stage of the algorithm, this paper proposes a network security situation prediction model based on support vector machine(SVM) optimized by improved artificial bee colony algorithm(I-ABC), using I-ABC algorithm for SVM. The penalty factor a and the kernel parameter b are optimized. Finally, the simulation test is performed using real network security situation data. The simulation results show that the proposed algorithm can accurately track the change of situation value and effectively improve the prediction accuracy of network security situation. © 2021 computer Society of the Republic of China. All rights reserved.

关键词： Support vector machines

Neural networks enforcing physical symmetries in nonlinear dynamical lattices: The case example of the Ablowitz-Ladik model

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arXiv 2021年

作者： Zhu, Wei Khademi, Wesley Charalampidis, Efstathios G. Kevrekidis, Panayotis G. Department of Mathematics and Statistics University of Massachusetts Amherst AmherstMA01003-4515 United States Computer Science and Software Engineering Department California Polytechnic State University San Luis ObispoCA93407-0403 United States School of Electrical Engineering and Computer Science Oregon State University OR97331-5501 United States Mathematics Department California Polytechnic State University San Luis ObispoCA93407-0403 United States

In this work we introduce symmetry-preserving, physics-informed neural networks (S-PINNs) motivated by symmetries that are ubiquitous to solutions of nonlinear dynamical lattices. Although the use of PINNs have recently attracted much attention in data-driven discovery of solutions chiefly to partial differential equations, we demonstrate that they fail at enforcing important physical laws including symmetries of solutions and conservation laws. Through the correlation of parity symmetries in both space and time of solutions to differential equations with their group equivariant representation, we construct group-equivariant NNs which respect spatio-temporal parity symmetry. Moreover, we adapt the proposed architecture to enforce different types of periodicity (or localization) of solutions to nonlinear dynamical lattices. We do so by applying S-PINNs to the completely integrable Ablowitz-Ladik model, and performing numerical experiments with a special focus on waveforms that are related to rogue structures. These include the Kuznetsov-Ma soliton, and Akhmediev breather as well as the Peregrine soliton. Our numerical results demonstrate the superiority and robustness of the proposed architecture over standard PINNs. Copyright © 2021, The Authors. All rights reserved.

关键词： Solitons

Towards Transmission-Friendly and Robust CNN Models over Cloud and Device

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arXiv 2022年

作者： Ding, Chuntao Lu, Zhichao Juefei-Xu, Felix Boddeti, Vishnu Naresh Li, Yidong Cao, Jiannong The School of Computer and Information Technology Beijing Jiaotong University Beijing China The School of Software Engineering Sun Yat-sen University Zhuhai519082 China Alibaba Group United States The Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States The Department of Computing The Hong Kong Polytechnic University Hong Kong

Deploying deep convolutional neural network (CNN) models on ubiquitous Internet of Things (IoT) devices has attracted much attention from industry and academia since it greatly facilitates our lives by providing various rapid-response services. Due to the limited resources of IoT devices, cloud-assisted training of CNN models has become the mainstream. However, most existing related works suffer from a large amount of model parameter transmission and weak model robustness. To this end, this paper proposes a cloud-assisted CNN training framework with low model parameter transmission and strong model robustness. In the proposed framework, we first introduce MonoCNN, which contains only a few learnable filters, and other filters are nonlearnable. These nonlearnable filter parameters are generated according to certain rules, i.e., the filter generation function (FGF), and can be saved and reproduced by a few random seeds. Thus, the cloud server only needs to send these learnable filters and a few seeds to the IoT device. Compared to transmitting all model parameters, sending several learnable filter parameters and seeds can significantly reduce parameter transmission. Then, we investigate multiple FGFs and enable the IoT device to use the FGF to generate multiple filters and combine them into MonoCNN. Thus, MonoCNN is affected not only by the training data but also by the FGF. The rules of the FGF play a role in regularizing the MonoCNN, thereby improving its robustness. Experimental results show that compared to state-of-the-art methods, our proposed framework can reduce a large amount of model parameter transfer between the cloud server and the IoT device while improving the performance by approximately 2.2% when dealing with corrupted data. The code is available at https://***/zhichao-lu/mono-cnn-pytorch. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

A High-Fidelity Virtual Liver Model Incorporating Biological Characteristics

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SSRN

SSRN 2023年

作者： Zhang, Xiaorui Zhang, Wenzheng Sun, Wei Song, Aiguo Xu, Tong Engineering Research Center of Digital Forensics Ministry of Education Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China Wuxi Research Institute Nanjing University of Information Science & Technology Wuxi214100 China School of Automation Nanjing University of Information Science & Technology Nanjing210044 China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and Control School of Instrument Science and Engineering Southeast University Nanjing210096 China University of Southern California Los AngelesCA United States

Flexible tissue modeling plays an important role in the field of telemedicine. It is related to whether the soft tissue deformation process can be accurately, real-time and vividly simulated during surgery. However, most existing models lack the unique biological characteristics. To solve this problem, a high-fidelity virtual liver model incorporating biological characteristics, such as the viscoelastic, anisotropic and nonlinear biological characteristics, is proposed based on the finite element model. When simulating the resection of diseased livers, the model undergoes a plastic deformation process where the model loses elasticity due to excessive deformation, but does not reach the cutting fracture threshold. To improve the fidelity of the liver model, this study is the first to introduce the viscoplasticity of biological tissues to the liver model. The 3DMax2020 and OpenGL4.6 were used to build a liver surgery simulation platform, and the PHANTOM OMNI manual controller was used to sense the feedback force during the operation. The proposed model was verified from three aspects of accuracy, fidelity and real-time performance. The experimental results show that the proposed virtual liver model can enhance visual perception ability, improve deformation accuracy and fidelity. © 2023, The Authors. All rights reserved.

关键词： Simulation platform