142 research outputs found
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
Understanding and Addressing the Unbounded “Likelihood” Problem
The joint probability density function, evaluated at the observed data, is commonly used as the likelihood function to compute maximum likelihood estimates. For some models, however, there exist paths in the parameter space along which this density-approximation likelihood goes to infinity and maximum likelihood estimation breaks down. In all applications, however, observed data are really discrete due to the round-off or grouping error of measurements. The “correct likelihood” based on interval censoring can eliminate the problem of an unbounded likelihood. This article categorizes the models leading to unbounded likelihoods into three groups and illustrates the density-approximation breakdown with specific examples. Although it is usually possible to infer how given data were rounded, when this is not possible, one must choose the width for interval censoring, so we study the effect of the round-off on estimation. We also give sufficient conditions for the joint density to provide the same maximum likelihood estimate as the correct likelihood, as the round-off error goes to zero
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
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