ResumeNet: A Learning-based Framework for Automatic Resume Quality Assessment

Recruitment of appropriate people for certain positions is critical for any companies or organizations. Manually screening to select appropriate candidates from large amounts of resumes can be exhausted and time-consuming. However, there is no public tool that can be directly used for automatic resume quality assessment (RQA). This motivates us to develop a method for automatic RQA. Since there is also no public dataset for model training and evaluation, we build a dataset for RQA by collecting around 10K resumes, which are provided by a private resume management company. By investigating the dataset, we identify some factors or features that could be useful to discriminate good resumes from bad ones, e.g., the consistency between different parts of a resume. Then a neural-network model is designed to predict the quality of each resume, where some text processing techniques are incorporated. To deal with the label deficiency issue in the dataset, we propose several variants of the model by either utilizing the pair/triplet-based loss, or introducing some semi-supervised learning technique to make use of the abundant unlabeled data. Both the presented baseline model and its variants are general and easy to implement. Various popular criteria including the receiver operating characteristic (ROC) curve, F-measure and ranking-based average precision (AP) are adopted for model evaluation. We compare the different variants with our baseline model. Since there is no public algorithm for RQA, we further compare our results with those obtained from a website that can score a resume. Experimental results in terms of different criteria demonstrate the effectiveness of the proposed method. We foresee that our approach would transform the way of future human resources management.Comment: ICD

Discovery of An Active Intermediate-Mass Black Hole Candidate in the Barred Bulgeless Galaxy NGC 3319

We report the discovery of an active intermediate-mass black hole (IMBH) candidate in the center of nearby barred bulgeless galaxy $\rm NGC~3319$. The point X-ray source revealed by archival Chandra and XMM-Newton observations is spatially coincident with the optical and UV galactic nuclei from Hubble Space Telescope observations. The spectral energy distribution derived from the unresolved X-ray and UV-optical flux is comparable with active galactic nuclei (AGNs) rather than ultra-luminous X-ray sources, although its bolometric luminosity is only $3.6\times10^{40}~\rm erg~s^{-1}$. Assuming an Eddington ratio range between 0.001 and 1, the black hole mass (M_\rm{BH}) will be located at $3\times10^2-3\times10^5~M_{\odot}$, placing it in the so-called IMBH regime and could be the one of the lowest reported so far. Estimates from other approaches (e.g., fundamental plane, X-ray variability) also suggest M_\rm{BH}\lesssim10^5~M_{\odot}.Similar to other BHs in bulgeless galaxies, the discovered IMBH resides in a nuclear star cluster with mass of $\sim6\times10^6~M_{\odot}$. The detection of such a low-mass BH offers us an ideal chance to study the formation and early growth of SMBH seeds, which may result from the bar-driven inflow in late-type galaxies with a prominent bar such as $\rm NGC~3319$.Comment: ApJ accepted, 2 tables, 6 figure