JobHam-place with smart recommend job options and candidate filtering options

Due to the increasing number of graduates, many applicants experience the situation about finding a job, and employers experience difficulty filtering job applicants, which might negatively impact their effectiveness. However, most job-hunting websites lack job recommendation and CV filtering or ranking functionality, which are not integrated into the system. Thus, a smart job hunter combined with the above functionality will be conducted in this project, which contains job recommendations, CV ranking and even a job dashboard for skills and job applicant functionality. Job recommendation and CV ranking starts from the automatic keyword extraction and end with the Job/CV ranking algorithm. Automatic keyword extraction is implemented by Job2Skill and the CV2Skill model based on Bert. Job2Skill consists of two components, text encoder and Gru-based layers, while CV2Skill is mainly based on Bert and fine-tunes the pre-trained model by the Resume- Entity dataset. Besides, to match skills from CV and job description and rank lists of jobs and candidates, job/CV ranking algorithms have been provided to compute the occurrence ratio of skill words based on TFIDF score and match ratio of the total skill numbers. Besides, some advanced features have been integrated into the website to improve user experiences, such as the calendar and sweetalert2 plugin. And some basic features to go through job application processes, such as job application tracking and interview arrangement

Comparison of Different Wind Time Series Simulation Methods

The assessment of power system reliability under increasing penetration of wind power requires long-term wind data that is not available or does not exist and hence must be simulated. In this research, autoregressive models (AR) ranging from 1st order to 12th order and Markov-switching autoregressive models (MS-AR) ranging from MS(2)-AR(2) to MS(5)-AR(5) are used for wind simulation using 10-minutes wind speed data from NREL for years 2004 and 2005. Simulation results are compared between models, across different seasons, and different data lengths. Consistent with the literature, we find that AR models can efficiently replicate the autocorrelation function (ACF) but not the probability distribution function (PDF) observed in the original data. MS-AR models perform better than AR models in terms of both ACF and PDF and their performance improves with the increasing number of states in the Markov Chain

Analysis of water saving in the construction process based on green building

Under the premise of ensuring their own food, clothing, housing and transportation, We aim for sustainable development to make the building and nature coordinate with each other and to create a healthier and more comfortable living space. This article analyzes the water resources in the construction process, discusses why they are wasted and how to reduce their waste In addition, a water-saving combination method will be proposed to optimize this situation

Federated Learning over a Wireless Network: Distributed User Selection through Random Access

User selection has become crucial for decreasing the communication costs of federated learning (FL) over wireless networks. However, centralized user selection causes additional system complexity. This study proposes a network intrinsic approach of distributed user selection that leverages the radio resource competition mechanism in random access. Taking the carrier sensing multiple access (CSMA) mechanism as an example of random access, we manipulate the contention window (CW) size to prioritize certain users for obtaining radio resources in each round of training. Training data bias is used as a target scenario for FL with user selection. Prioritization is based on the distance between the newly trained local model and the global model of the previous round. To avoid excessive contribution by certain users, a counting mechanism is used to ensure fairness. Simulations with various datasets demonstrate that this method can rapidly achieve convergence similar to that of the centralized user selection approach