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RésuMatcher: A personalized résumé-job matching system

Shiqiang Guo, Folami Alamudun, Tracy Hammond
2016 Expert systems with applications  
Today, online recruiting web sites such as Monster and have become one of the main channels for people to find jobs. These web platforms have provided their services for more than ten years, and have saved a lot of time and money for both job seekers and organizations who want to hire people. However, traditional information retrieval techniques may not be appropriate for users. The reason is because the number of results returned to a job seeker may be huge, so job seekers are
more » ... ed to spend a significant amount of time reading and reviewing their options. One popular approach to resolve this difficulty for users are recommender systems, which is a technology that has been studied for a long time. In this thesis we have made an effort to propose a personalized job-résumé matching system, which could help job seekers to find appropriate jobs more easily. We create a finite state transducer based information extraction library to extract models from résumés and job descriptions. We devised a new statistical-based ontology similarity measure to compare the résumé models and the job models. Since the most appropriate jobs will be returned first, the users of the system may get a better result than current job finding web sites. To evaluate the system, we computed Normalized Discounted Cumulative Gain (NDCG) and precision@k of our system, and compared to three other existing models as well as the live result from
doi:10.1016/j.eswa.2016.04.013 fatcat:72ipxgc6dbgufh22n2px7ws2ie

Modeling sequential context effects in diagnostic interpretation of screening mammograms

Folami Alamudun, Paige Paulus, Hong-Jun Yoon, Georgia Tourassi
2018 Journal of Medical Imaging  
Prior research has shown that physicians' medical decisions can be influenced by sequential context, particularly in cases where successive stimuli exhibit similar characteristics when analyzing medical images. This type of systematic error is known to psychophysicists as sequential context effect as it indicates that judgments are influenced by features of and decisions about the preceding case in the sequence of examined cases, rather than being based solely on the peculiarities unique to the
more » ... present case. We determine if radiologists experience some form of context bias, using screening mammography as the use case. To this end, we explore correlations between previous perceptual behavior and diagnostic decisions and current decisions. We hypothesize that a radiologist's visual search pattern and diagnostic decisions in previous cases are predictive of the radiologist's current diagnostic decisions. To test our hypothesis, we tasked 10 radiologists of varied experience to conduct blind reviews of 100 four-view screening mammograms. Eye-tracking data and diagnostic decisions were collected from each radiologist under conditions mimicking clinical practice. Perceptual behavior was quantified using the fractal dimension of gaze scanpath, which was computed using the Minkowski-Bouligand boxcounting method. To test the effect of previous behavior and decisions, we conducted a multifactor fixed-effects ANOVA. Further, to examine the predictive value of previous perceptual behavior and decisions, we trained and evaluated a predictive model for radiologists' current diagnostic decisions. ANOVA tests showed that previous visual behavior, characterized by fractal analysis, previous diagnostic decisions, and image characteristics of previous cases are significant predictors of current diagnostic decisions. Additionally, predictive modeling of diagnostic decisions showed an overall improvement in prediction error when the model is trained on additional information about previous perceptual behavior and diagnostic decisions.
doi:10.1117/1.jmi.5.3.031408 pmid:29564370 pmcid:PMC5858736 fatcat:wnnjrzsl2zaufmx32cdyomygvq

Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

Hong-Jun Yoon, Folami Alamudun, Kathy Hudson, Garnetta Morin-Ducote, Georgia Tourassi
2018 Human Performance in Extreme Environments  
Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a
more » ... NN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed that the CNN classifier is superior compared to alternative classification methods based on macro F 1 -scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.
doi:10.7771/2327-2937.1088 fatcat:jswaett745ghviijwbzquz4udy

Removal of Subject-Dependent and Activity-Dependent Variation in Physiological Measures of Stress

Folami Alamudun, Jongyoon Choi, Hira Khan, Beena Ahmed, Ricardo Gutierrez-Osuna
2012 Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare  
The ability to monitor stress levels in daily life can provide valuable information to patients and their caretakers, help identify potential stressors, determine appropriate interventions, and monitor their effectiveness. Wearable sensor technology makes it now possible to measure non-invasively a number of physiological correlates of stress, from skin conductance to heart rate variability. These measures, however, show large individual differences and are also correlated with the physical
more » ... vity of the subject. In this paper, we propose two multivariate signal processing techniques to reduce the effect of both forms of interference. The first method is an unsupervised technique that removes any systematic variation that is orthogonal to the dependent variable, in this case physiological stress. In contrast, the second method is a supervised technique that first projects the data into a subspace that emphasizes these systematic variations, and then removes them from the data. The two methods were validated on an experimental dataset containing physiological recordings from multiple subjects performing physical and/or mental activities. When compared to z-score normalization, the standard method for removing individual differences, our methods can reduce stress prediction errors by as much as 50%.
doi:10.4108/icst.pervasivehealth.2012.248722 dblp:conf/ph/AlamudunCGKA12 fatcat:ugnnevdlybdhzjfhrzrybduqw4

Let Me Relax: Toward Automated Sedentary State Recognition and Ubiquitous Mental Wellness Solutions

Vijay Rajanna, Folami Alamudun, Daniel Goldberg, Tracy Hammond
2015 Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies"  
Advances in ubiquitous computing technology improve workplace productivity, reduce physical exertion, but ultimately result in a sedentary work style. Sedentary behavior is associated with an increased risk of stress, obesity, and other health complications. Let Me Relax is a fully automated sedentary-state recognition framework using a smartwatch and smartphone, which encourages mental wellness through interventions in the form of simple relaxation techniques. The system was evaluated through
more » ... comparative user study of 22 participants split into a test and a control group. An analysis of NASA Task Load Index pre-and post-study survey revealed that test subjects who followed relaxation methods, showed a trend of both increased activity as well as reduced mental stress. Reduced mental stress was found even in those test subjects that had increased inactivity. These results suggest that repeated interventions, driven by an intelligent activity recognition system, is an effective strategy for promoting healthy habits, which reduce stress, anxiety, and other health risks associated with sedentary workplaces.
doi:10.4108/eai.14-10-2015.2261900 dblp:journals/amsys/RajannaAGH16 fatcat:d4l63jdo3ne4fovt73xiaqft3m

Development and Evaluation of an Ambulatory Stress Monitor Based on Wearable Sensors

Jongyoon Choi, B. Ahmed, R. Gutierrez-Osuna
2012 IEEE Transactions on Information Technology in Biomedicine  
The authors are thankful to Daniel Felps and Folami Alamudun for their assistance with editing this manuscript.  ... 
doi:10.1109/titb.2011.2169804 pmid:21965215 fatcat:zgod4j22efeu7jstzfb25zazxi

Step up life

Vijay Rajanna, Raniero Lara-Garduno, Dev Jyoti Behera, Karthic Madanagopal, Daniel Goldberg, Tracy Hammond
2014 Proceedings of the Third ACM SIGSPATIAL International Workshop on the Use of GIS in Public Health - HealthGIS '14  
Andruid Kerne, Rhema Linder, Stephanie Valentine, Ayobami Olubeku, and Folami Alamudun for their valuable feedback during this work.  ... 
doi:10.1145/2676629.2676636 dblp:conf/gis/RajannaLBMGH14 fatcat:c2p6walfw5bfpcl2pmwwymcbpi


2018 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN)  
, Folami (Oak Ridge National Laboratory) 15:00-15:12 WeCT1.6 Real World Assessment of Eldercare Interventions Efficacy Billis, Antonis (Aristotle University of Thessaloniki); Zilidou, Vasiliki (  ...  Modeling Radiologists' Perceptual and Cognitive Behavior during Diagnostic Image Interpretation Tourassi, Georgia* (Oak Ridge National Laboratory); Yoon, Hong- Jun (Oak Ridge National Laboratory); Alamudun  ... 
doi:10.1109/bsn.2018.8329643 fatcat:2v33mwoo2jb4jdyrakoqc2rvhu