Personality Mining System for Automated Applicant Ranking

Abstract. In the last decades the explosion of ICT has opened up new avenues regarding peoples' accessibility to new job opportunities. Current technological advances in conjunction with people's online presence provide a great opportunity to automate the recruitment process and make it more effective. In this paper, we propose a novel approach for improving the efficiency of erecruitment systems. Our approach relies on the linguistic analysis of data available for job applicants, in order to infer the applicants' personality traits and rank them accordingly. To showcase the functionality of our method, we employed it in a web based e-recruitment system that we implemented

Institutional Profile: Golden Helix Institute of Biomedical Research: interdisciplinary research and educational activities in pharmacogenomics and personalized medicine

The Golden Helix Institute of Biomedical Research is an international non-profit scientific organization with interdisciplinary research and educational activities in the field of genome medicine in Europe, Asia and Latin America. These activities are supervised by an international scientific advisory council, consisting of world leaders in the field of genomics and translational medicine. Research activities include the regional coordination of the Pharmacogenomics for Every Nation Initiative in Europe, in an effort to integrate pharmacogenomics in developing countries, the development of several National/Ethnic Genetic databases and related web services and the critical assessment of the impact of genetics and genomic medicine to society in various countries. Also, educational activities include the organization of the Golden Helix Symposia®, which are high profile scientific research symposia in the field of personalized medicine, and the Golden Helix Pharmacogenomics Days, an international educational activity focused on pharmacogenomics, as part of its international pharmacogenomics education and outreach efforts

Expanded national database collection and data coverage in the FINDbase worldwide database for clinically relevant genomic variation allele frequencies

FINDbase (http://www.findbase.org) is a comprehensive data repository that records the prevalence of clinically relevant genomic variants in various populations worldwide, such as pathogenic variants leadingmostly tomonogenic disorders and pharmacogenomics biomarkers. The database also records the incidence of rare genetic diseases in various populations, all in well-distinct data modules. Here, we report extensive data content updates in all data modules, with direct implications to clinical pharmacogenomics. Also, we report significant new developments in FINDbase, namely (i) the release of a new version of the ETHNOS software that catalyzes development curation of national/ethnic genetic databases, (ii) the migration of all FINDbase data content into 90 distinct national/ethnicmutation databases, all built around Microsoft's PivotViewer (http://www.getpivot.com) software (iii) new data visualization tools and (iv) the interrelation of FINDbase with DruGeVar database with direct implications in clinical pharmacogenomics. The abovementioned updates further enhance the impact of FIND-base, as a key resource for Genomic Medicine applications

Expanded national database collection and data coverage in the FINDbase worldwide database for clinically relevant genomic variation allele frequencies

Peer reviewe

FINDbase: a worldwide database for genetic variation allele frequencies updated

Frequency of INherited Disorders database (FIND base; http://www.findbase.org) records frequencies of causative genetic variations worldwide. Database records include the population and ethnic group or geographical region, the disorder name and the related gene, accompanied by links to any related external resources and the genetic variation together with its frequency in that population. In addition to the regular data content updates, we report the following significant advances: (i) the systematic collection and thorough documentation of population/ethnic group-specific pharmacogenomic markers allele frequencies for 144 markers in 14 genes of pharmacogenomic interest from different classes of drug-metabolizing enzymes and transporters, representing 150 populations and ethnic groups worldwide; (ii) the development of new data querying and visualization tools in the expanded FINDbase data collection, built around Microsoft’s PivotViewer software (http://www.getpivot.com), based on Microsoft Silverlight technology (http://www.silverlight.net) that facilitates querying of large data sets and visualizing the results; and (iii) the establishment of the first database journal, by affiliating FINDbase with Human Genomics and Proteomics, a new open-access scientific journal, which would serve as a prime example of a non-profit model for sustainable database funding

Mining Domain-Specific Design Patterns: A Case Study †

Domain-specific design patterns provide developers with proven solutions to common design problems that arise, particularly in a target application domain, facilitating them to produce quality designs in the domain contexts. However, research in this area is not mature and there are no techniques to support their detection. Towards this end, we propose a methodology which, when applied on a collection of websites in a specific domain, facilitates the automated identification of domain-specific design patterns. The methodology automatically extracts the conceptual models of the websites, which are subsequently analyzed in terms of all of the reusable design fragments used in them for supporting common domain functionalities. At the conceptual level, we consider these fragments as recurrent patterns consisting of a configuration of front-end interface components that interrelate each other and interact with end-users to support certain functionality. By performing a pattern-based analysis of the models, we locate the occurrences of all the recurrent patterns in the various website designs which are then evaluated towards their consistent use. The detected patterns can be used as building blocks in future designs, assisting developers to produce consistent and quality designs in the target domain. To support our case, we present a case study for the educational domain

Generating Synthetic Resume Data with Large Language Models for Enhanced Job Description Classification

In this article, we investigate the potential of synthetic resumes as a means for the rapid generation of training data and their effectiveness in data augmentation, especially in categories marked by sparse samples. The widespread implementation of machine learning algorithms in natural language processing (NLP) has notably streamlined the resume classification process, delivering time and cost efficiencies for hiring organizations. However, the performance of these algorithms depends on the abundance of training data. While selecting the right model architecture is essential, it is also crucial to ensure the availability of a robust, well-curated dataset. For many categories in the job market, data sparsity remains a challenge. To deal with this challenge, we employed the OpenAI API to generate both structured and unstructured resumes tailored to specific criteria. These synthetically generated resumes were cleaned, preprocessed and then utilized to train two distinct models: a transformer model (BERT) and a feedforward neural network (FFNN) that incorporated Universal Sentence Encoder 4 (USE4) embeddings. While both models were evaluated on the multiclass classification task of resumes, when trained on an augmented dataset containing 60 percent real data (from Indeed website) and 40 percent synthetic data from ChatGPT, the transformer model presented exceptional accuracy. The FFNN, albeit predictably, achieved lower accuracy. These findings highlight the value of augmented real-world data with ChatGPT-generated synthetic resumes, especially in the context of limited training data. The suitability of the BERT model for such classification tasks further reinforces this narrative