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Register a new userCareerMapper: An Automated Resume Evaluation Tool
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Added by
Richard Oentaryo
Publication date
2016
fields
Informatics Engineering
and research's language is
English
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The advent of the Web brought about major changes in the way people search for jobs and companies look for suitable candidates. As more employers and recruitment firms turn to the Web for job candidate search, an increasing number of people turn to the Web for uploading and creating their online resumes. Resumes are often the first source of information about candidates and also the first item of evaluation in candidate selection. Thus, it is imperative that resumes are complete, free of errors and well-organized. We present an automated resume evaluation tool called CareerMapper. Our tool is designed to conduct a thorough review of a users LinkedIn profile and provide best recommendations for improved online resumes by analyzing a large number of online user profiles.
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An automated counterexample reproducibility tool based on MATLAB is presented, called DSValidator, with the goal of reproducing counterexamples that refute specific properties related to digital systems. We exploit counterexamples generated by the Digital System Verifier (DSVerifier), which is a model checking tool based on satisfiability modulo theories for digital systems. DSValidator reproduces the execution of a digital system, relating its input with the counterexample, in order to establish trust in a verification result. We show that DSValidator can validate a set of intricate counterexamples for digital controllers used in a real quadrotor attitude system within seconds and also expose incorrect verification results in DSVerifier. The resulting toolbox leverages the potential of combining different verification tools for validating digital systems via an exchangeable counterexample format.
Asteroseismic observations are crucial to constrain stellar models with precision. Bayesian Estimation of STellar Parameters (BESTP) is a tool that utilizes Bayesian statistics and nested sampling Monte Carlo algorithm to search for the stellar models that best match a given set of classical and asteroseismic constraints from observations. The computation and evaluation of models are efficiently performed in an automated and a multi-threaded way. To illustrate the capabilities of BESTP, we estimate fundamental stellar properties for the Sun and the red-giant star HD 222076. In both cases, we find models that are consistent with the observations. We also evaluate the improvement in the precision of stellar parameters when the oscillation frequencies of individual modes are included as constraints, compared to the case when only the the large frequency separation is included. For the solar case, the uncertainties of estimated masses, radii and ages are reduced by 0.7%, 0.3% and 8% respectively. For HD 222076, they are reduced even more noticeably by 2%, 0.5% and 4.7%. We also note an improvement of 10% for the age of HD 222076 when the Gaia parallax is included as a constraint compared to the case when only the large separation is included as constraint.
Research institutions are bound to contribute to greenhouse gas emission (GHG) reduction efforts for several reasons. First, part of the scientific communitys research deals with climate change issues. Second, scientists contribute to students education: they must be consistent and role models. Third the literature on the carbon footprint of researchers points to the high level of some individual footprints. In a quest for consistency and role models, scientists, teams of scientists or universities have started to quantify their carbon footprints and debate on reduction options. Indeed, measuring the carbon footprint of research activities requires tools designed to tackle its specific features. In this paper, we present an open-source web application, GES 1point5, developed by an interdisciplinary team of scientists from several research labs in France. GES 1point5 is specifically designed to estimate the carbon footprint of research activities in France. It operates at the scale of research labs, i.e. laboratoires, which are the social structures around which research is organized in France and the smallest decision making entities in the French research system. The application allows French research labs to compute their own carbon footprint along a standardized, open protocol. The data collected in a rapidly growing network of labs will be used as part of the Labos 1point5 project to estimate Frances research carbon footprint. At the time of submitting this manuscript, 89 research labs had engaged with GES 1point5 to estimate their greenhouse gas emissions. We expect that an international adoption of GES 1point5 (adapted to fit domestic specifics) could contribute to establishing a global understanding of the drivers of the research carbon footprint worldwide and the levers to decrease it.
Critical task and cognition-based environments, such as in military and defense operations, aviation user-technology interaction evaluation on UI, understanding intuitiveness of a hardware model or software toolkit, etc. require an assessment of how much a particular task is generating mental workload on a user. This is necessary for understanding how those tasks, operations, and activities can be improvised and made better suited for the users so that they reduce the mental workload on the individual and the operators can use them with ease and less difficulty. However, a particular task can be gauged by a user as simple while for others it may be difficult. Understanding the complexity of a particular task can only be done on user level and we propose to do this by understanding the mental workload (MWL) generated on an operator while performing a task which requires processing a lot of information to get the task done. In this work, we have proposed an experimental setup which replicates modern day workload on doing regular day job tasks. We propose an approach to automatically evaluate the task complexity perceived by an individual by using electroencephalogram (EEG) data of a user during operation. Few crucial steps that are addressed in this work include extraction and optimization of different features and selection of relevant features for dimensionality reduction and using supervised machine learning techniques. In addition to this, performance results of the classifiers are compared using all features and also using only the selected features. From the results, it can be inferred that machine learning algorithms perform better as compared to traditional approaches for mental workload estimation.
Digital contact tracing is being used by many countries to help contain COVID-19s spread in a post-lockdown world. Among the various available techniques, decentralized contact tracing that uses Bluetooth received signal strength indication (RSSI) to detect proximity is considered less of a privacy risk than approaches that rely on collecting absolute locations via GPS, cellular-tower history, or QR-code scanning. As of October 2020, there have been millions of downloads of such Bluetooth-based contract-tracing apps, as more and more countries officially adopt them. However, the effectiveness of these apps in the real world remains unclear due to a lack of empirical research that includes realistic crowd sizes and densities. This study aims to fill that gap, by empirically investigating the effectiveness of Bluetooth-based contact tracing in crowd environments with a total of 80 participants, emulating classrooms, moving lines, and other types of real-world gatherings. The results confirm that Bluetooth RSSI is unreliable for detecting proximity, and that this inaccuracy worsens in environments that are especially crowded. In other words, this technique may be least useful when it is most in need, and that it is fragile when confronted by low-cost jamming. Moreover, technical problems such as high energy consumption and phone overheating caused by the contact-tracing app were found to negatively influence users willingness to adopt it. On the bright side, however, Bluetooth RSSI may still be useful for detecting coarse-grained contact events, for example, proximity of up to 20m lasting for an hour. Based on our findings, we recommend that existing contact-tracing apps can be re-purposed to focus on coarse-grained proximity detection, and that future ones calibrate distance estimates and adjust broadcast frequencies based on auxiliary information.
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