Measuring the Popularity of Job Skills in Recruitment Market: A Multi-Criteria Approach

To cope with the accelerating pace of technological changes, talents are urged to add and refresh their skills for staying in active and gainful employment. This raises a natural question: what are the right skills to learn? Indeed, it is a nontrivial task to measure the popularity of job skills due to the diversified criteria of jobs and the complicated connections within job skills. To that end, in this paper, we propose a data driven approach for modeling the popularity of job skills based on the analysis of large-scale recruitment data. Specifically, we first build a job skill network by exploring a large corpus of job postings. Then, we develop a novel Skill Popularity based Topic Model (SPTM) for modeling the generation of the skill network. In particular, SPTM can integrate different criteria of jobs (e.g., salary levels, company size) as well as the latent connections within skills, thus we can effectively rank the job skills based on their multi-faceted popularity. Extensive experiments on real-world recruitment data validate the effectiveness of SPTM for measuring the popularity of job skills, and also reveal some interesting rules, such as the popular job skills which lead to high-paid employment.Comment: 8 pages, 14 figures, AAAI 201

Miniaturized PM2.5 Particulate Sensor Based on Optical Sensing

© ASEE 2015With the rapid economic growth in developing countries, the air pollution is becoming more and more serious. The air in some big cities becomes foggy due to various pollution sources. Particulate matter with diameter of 2.5 microns or less is especially dangerous because they can be inhaled into deep part of lung tissues and eventually absorbed into blood circulation system. This is very harmful for human health, leading to lung cancer and other related diseases. It is important for people to have a handheld PM2.5 sensor to monitor the air pollution to decide whether they should travel or do exercises outdoor. In this research, a miniaturized PM2.5 particulate sensor based on optical sensing is proposed. The air sample flows through a chamber with constant flow rate. Incident laser light pass through the chamber and is reflected by a set of mirrors for multiple times. The light is scattered by the PM2.5 particles in the air sample so that the light intensity is weakened. The higher concentration the PM2.5 is, the more light scattering it will be. By sensing the reflected light intensity with a photodetector, the PM2.5 concentration of the air sample can be measured. Unlike PM2.5 sensing by absorption, optical sensing does not cause any pollution to the device and it can be reused for a long time. The device is designed, analyzed and simulated with COMSOL. It offers a low-cost portable solution for PM2.5 sensing