Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science
Title: Design of a Resilient Information System   for Disaster Response
Abstract: The devastating 2011 Great East Japan Earthquake made people aware of the importance of Information and Communication Technology (ICT) for sustaining life during and soon after a disaster. The difficulty in recovering information systems, because of the failure of ICT, hindered all recovery processes. The paper explores ways to make information systems resilient in disaster situations. Resilience is defined as quickly regaining essential capabilities to perform critical post disaster missions and to smoothly return to fully stable operations thereafter. From case studies and the literature, we propose that a frugal IS design that allows creative responses will make information systems resilient in disaster situations. A three-stage model based on a chronological sequence was employed in structuring the proposed design principles.
Extracted statement — Emergency Management Phase: response
Research field: Science
Title: ICT-Enabled Community Empowerment in Crisis
Response: Social Media in Thailand Flooding 2011
Abstract: In this paper, we examine the emerging use of ICT in social phenomena such as natural disasters. Researchers have acknowledged that a community possesses the capacity to manage the challenges in crisis response on its own. However, extant IS studies focus predominantly on IS use from the crisis response agency’s perspective, which undermines communities’ role. By adopting an empowerment perspective, we focus on understanding how social media empowers communities during crisis response. As such, we present a qualitative case study of the 2011 Thailand flooding. Using an interpretive approach, we show how social media can empower the community from three dimensions of empowerment process (structural, psychological, and resource empowerment) to achieve collective participation, shared identification, and collaborative control in the community. We make two contributions: 1) we explore an emerging social consequence of ICT by illustrating the roles of social media in empowering communities when responding to crises, and 2) we address the literature gap in empowerment by elucidating the actualization process of empowerment that social media as a mediating structure enables.
Extracted statement — Emergency Management Phase: Response
Research field: Science
Title: Digitally enabled disaster response: the
emergence of social media as boundary
objects in a flooding disaster
Abstract: In recent times, social media has been increasingly playing a critical role in response actions following natural catastrophes. From facilitating the recruitment of volunteers during an earthquake to supporting emotional recovery after a hurricane, social media has demonstrated its power in serving as an effective disaster response platform. Based on a case study of Thailand flooding in 2011 – one of the worst flooding disasters in more than 50 years that left the country severely impaired – this paper provides an in‐depth understanding on the emergent roles of social media in disaster response. Employing the perspective of boundary object, we shed light on how different boundary spanning competences of social media emerged in practice to facilitate cross‐boundary response actions during a disaster, with an aim to promote further research in this area. We conclude this paper with guidelines for response agencies and impacted communities to deploy social media for future disaster response.
Extracted statement — Emergency Management Phase: response
Research field: Science
Title: ICT-Enabled Community Empowerment in Crisis
Response: Social Media in Thailand Flooding 2011.
Abstract: In this paper, we examine the emerging use of ICT in social phenomena such as natural disasters. Researchers have acknowledged that a community possesses the capacity to manage the challenges in crisis response on its own. However, extant IS studies focus predominantly on IS use from the crisis response agency’s perspective, which undermines communities’ role. By adopting an empowerment perspective, we focus on understanding how social media empowers communities during crisis response. As such, we present a qualitative case study of the 2011 Thailand flooding. Using an interpretive approach, we show how social media can empower the community from three dimensions of empowerment process (structural, psychological, and resource empowerment) to achieve collective participation, shared identification, and collaborative control in the community. We make two contributions: 1) we explore an emerging social consequence of ICT by illustrating the roles of social media in empowering communities when responding to crises, and 2) we address the literature gap in empowerment by elucidating the actualization process of empowerment that social media as a mediating structure enables.
Extracted statement — Emergency Management Phase: Response
Research field: Science
Title: Segmentation of Ocular Pathologies Using Deep Convolutional Neural Network
Abstract: Diabetes Mellitus (DM) is a chronic, progressive and life-threatening disease. The ocular manifestations of DM, Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME), are the leading causes of blindness in the adult population throughout the world. Early diagnosis of DR and DM through screening tests and successive treatments can reduce the threat to visual acuity. In this context, we propose an encoder decoder based semantic segmentation network SOP-Net (Segmentation of Ocular Pathologies Using Deep Convolutional Neural Network) for simultaneous delineation of retinal pathologies (hard exudates, soft exudates, hemorrhages, microaneurysms). The proposed semantic segmentation framework is capable of providing segmentation results at pixel-level with good localization of object boundaries. SOP-Net has been trained and tested on IDRiD dataset which is publicly available with pixel level annotations of retinal pathologies. The network achieved average accuracies of 98.98%, 90.46%, 96.79%, and 96.70% for segmentation of hard exudates, soft exudates, hemorrhages, and microaneurysms. The proposed methodology has the capability to be used in developing a diagnostic system for organizing large scale ophthalmic screening programs.
Extracted statement — contains: Results
Research field: Science
Title: Measuring the predictability of life outcomes with a scientific mass collaboration
Abstract: <jats:p>How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.</jats:p>
Extracted statement — contains: Results
Research field: Science
Title: Investigating Interactive Search Behaviour of Medical Students: An Exploratory Survey
Abstract: In this paper, we investigate medical students medical search behavior on a medical domain. We use two behavioral signals: detailed query analysis (qualitative and quantitative) and task completion time to understand how medical students perform medical searches based on varying task complexity. We also investigate how task complexity and topic familiarity affect search behavior. We gathered 80 interactive search sessions from an exploratory survey with 20 medical students. We observe information searching behavior using 3 simulated work task scenarios and 1 personal scenario. We present quantitative results from two perspectives: overall and user perceived task complexity. We also analyze query properties from a qualitative aspect. Our results show task complexity and topic familiarity affect search behavior of medical students. In some cases, medical students demonstrate different search traits on a personal task in comparison to the simulated work task scenarios. These findings help us better understand medical search behavior. Medical search engines can use these findings to detect and adapt to medical students' search behavior to enhance a student's search experience.
Extracted statement — contains: Results
Research field: Science
Title: Knowledge-based derivation of document logical structure
Abstract: The analysis of a document image to derive a symbolic description of its structure and contents involves using spatial domain knowledge to classify the different printed blocks (e.g., text paragraphs), group them into logical units (e.g., newspaper stories), and determine the reading order of the text blocks within each unit. These steps describe the conversion of the physical structure of a document into its logical structure. We have developed a computational model for document logical structure derivation, in which a rule-based control strategy utilizes the data obtained from analyzing a digitized document image, and makes inferences using a multi-level knowledge base of document layout rules. The knowledge-based document logical structure derivation system (DeLoS) based on this model consists of a hierarchical rule-based control system to guide the block classification, grouping and read-ordering operations; a global data structure to store the document image data and incremental inferences; and a domain knowledge base to encode the rules governing document layout.
Extracted statement — Key Idea: rule-based
Research field: Science
Title: Knowledge-based derivation of document logical structure
Abstract: The analysis of a document image to derive a symbolic description of its structure and contents involves using spatial domain knowledge to classify the different printed blocks (e.g., text paragraphs), group them into logical units (e.g., newspaper stories), and determine the reading order of the text blocks within each unit. These steps describe the conversion of the physical structure of a document into its logical structure. We have developed a computational model for document logical structure derivation, in which a rule-based control strategy utilizes the data obtained from analyzing a digitized document image, and makes inferences using a multi-level knowledge base of document layout rules. The knowledge-based document logical structure derivation system (DeLoS) based on this model consists of a hierarchical rule-based control system to guide the block classification, grouping and read-ordering operations; a global data structure to store the document image data and incremental inferences; and a domain knowledge base to encode the rules governing document layout.
Extracted statement — Physical Layout Representation: rules
Research field: Science
Title: Eye Localization based on Multi-Scale Gabor Feature Vector Model
Abstract: Eye localization is necessary for face recognition and related application areas. Most of eye localization algorithms reported thus far still need to be improved about precision and computational time for successful applications. In this paper, we propose an improved eye localization method based on multi-scale Gator feature vector models. The proposed method first tries to locate eyes in the downscaled face image by utilizing Gabor Jet similarity between Gabor feature vector at an initial eye coordinates and the eye model bunch of the corresponding scale. The proposed method finally locates eyes in the original input face image after it processes in the same way recursively in each scaled face image by using the eye coordinates localized in the downscaled image as initial eye coordinates. Experiments verify that our proposed method improves the precision rate without causing much computational overhead compared with other eye localization methods reported in the previous researches.
Extracted statement — Challenges: scale
Research field: Science
Title: 2D cascaded AdaBoost for eye localization
Abstract: In this paper, 2D cascaded AdaBoost, a novel classifier designing framework, is presented and applied to eye localization. By the term "2D", we mean that in our method there are two cascade classifiers in two directions: The first one is a cascade designed by bootstrapping the positive samples, and the second one, as the component classifiers of the first one, is cascaded by bootstrapping the negative samples. The advantages of the 2D structure include: (1) it greatly facilitates the classifier designing on huge-scale training set; (2) it can easily deal with the significant variations within the positive (or negative) samples; (3) both the training and testing procedures are more efficient. The proposed structure is applied to eye localization and evaluated on four public face databases, extensive experimental results verified the effectiveness, efficiency, and robustness of the proposed method
Extracted statement — Challenges: scale
Research field: Science
Title: A robust eye localization method for low quality face images
Abstract: Eye localization is an important part in face recognition system, because its precision closely affects the performance of face recognition. Although various methods have already achieved high precision on the face images with high quality, their precision will drop on low quality images. In this paper, we propose a robust eye localization method for low quality face images to improve the eye detection rate and localization precision. First, we propose a probabilistic cascade (P-Cascade) framework, in which we reformulate the traditional cascade classifier in a probabilistic way. The P-Cascade can give chance to each image patch contributing to the final result, regardless the patch is accepted or rejected by the cascade. Second, we propose two extensions to further improve the robustness and precision in the P-Cascade framework. There are: (1) extending feature set, and (2) stacking two classifiers in multiple scales. Extensive experiments on JAFFE, BioID, LFW and a self-collected video surveillance database show that our method is comparable to state-of-the-art methods on high quality images and can work well on low quality images. This work supplies a solid base for face recognition applications under unconstrained or surveillance environments.
Extracted statement — Challenges: scale
Research field: Science
Title: Robust Facial Features Localization on Rotation Arbitrary Multi-View face in Complex Background
Abstract: Focused on facial features localization on multi-view face arbitrarily rotated in plane, a novel detection algorithm based improved SVM is proposed. First, the face is located by the rotation invariant multi-view (RIMV) face detector and its pose in plane is corrected by rotation. After the searching ranges of the facial features are determined, the crossing detection method which uses the brow-eye and nose-mouth features and the improved SVM detectors trained by large scale multi-view facial features examples is adopted to find the candidate eye, nose and mouth regions,. Based on the fact that the window region with higher value in the SVM discriminant function is relatively closer to the object, and the same object tends to be repeatedly detected by near windows, the candidate eyes, nose and mouth regions are filtered and merged to refine their location on the multi-view face. Experiments show that the algorithm has very good accuracy and robustness to the facial features localization with expression and arbitrary face pose in complex background.
Extracted statement — Challenges: scale
Research field: Science
Title: Eye localization through multiscale sparse dictionaries
Abstract: This paper presents a new eye localization method via Multiscale Sparse Dictionaries (MSD). We built a pyramid of dictionaries that models context information at multiple scales. Eye locations are estimated at each scale by fitting the image through sparse coefficients of the dictionary. By using context information, our method is robust to various eye appearances. The method also works efficiently since it avoids sliding a search window in the image during localization. The experiments in BioID database prove the effectiveness of our method.
Extracted statement — Challenges: scale
Research field: Science
Title: Diagnosis of Schistosomiasis by Reagent Strip Test for Detection of Circulating Cathodic Antigen
Abstract: ABSTRACT A newly developed reagent strip assay for the diagnosis of schistosomiasis based on parasite antigen detection in urine of infected individuals was evaluated. The test uses the principle of lateral flow through a nitrocellulose strip of the sample mixed with a colloidal carbon conjugate of a monoclonal antibody specific for Schistosoma circulating cathodic antigen (CCA). The strip assay to diagnose a group of highly infected schoolchildren in Mwanza, Tanzania, demonstrated a high sensitivity and association with the intensity of infection as measured both by egg counts, and by circulating anodic antigen and CCA levels determined by enzyme-linked immunosorbent assay. A specificity of ca. 90% was shown in a group of schistosome-negative schoolchildren from Tarime, Tanzania, an area where schistosomiasis is not endemic. The test is easy to perform and requires no technical equipment or special training. The stability of the strips and the conjugate in the dry format lasts for at least 3 months at ambient temperature in sealed packages, making it suitable for transport and use in areas where schistosomiasis is endemic. This assay can easily be developed to an end-user format.
Extracted statement — Application: Schistosomiasis
Research field: Science
Title: Development of an infection screening system for entry inspection at airport quarantine stations using ear temperature, heart and respiration rates
Abstract: After the outbreak of severe acute respiratory syndrome (SARS) in 2003, many international airport quarantine stations conducted fever-based screening to identify infected passengers using infrared thermography for preventing global pandemics. Due to environmental factors affecting measurement of facial skin temperature with thermography, some previous studies revealed the limits of authenticity in detecting infectious symptoms. In order to implement more strict entry screening in the epidemic seasons of emerging infectious diseases, we developed an infection screening system for airport quarantines using multi-parameter vital signs. This system can automatically detect infected individuals within several tens of seconds by a neural-network-based discriminant function using measured vital signs, i.e., heart rate obtained by a reflective photo sensor, respiration rate determined by a 10-GHz non-contact respiration radar, and the ear temperature monitored by a thermography. In this paper, to reduce the environmental effects on thermography measurement, we adopted the ear temperature as a new screening indicator instead of facial skin. We tested the system on 13 influenza patients and 33 normal subjects. The sensitivity of the infection screening system in detecting influenza were 92.3%, which was higher than the sensitivity reported in our previous paper (88.0%) with average facial skin temperature.
Extracted statement — Objective: Screening
Research field: Science
Title: A Pediatric Infection Screening System with a Radar Respiration Monitor for Rapid Detection of Seasonal Influenza among Outpatient Children
Abstract: Background: Seasonal influenza virus outbreaks cause annual epidemics, mostly during winter in temperate zone countries, especially resulting in increased morbidity and higher mortality in children. In order to conduct rapid screening for influenza in pediatric outpatient units, we developed a pediatric infection screening system with a radar respiration monitor. Methods: The system conducts influenza screening within 10 seconds based on vital signs (i.e., respiration rate monitored using a 24 GHz microwave radar; facial temperature, using a thermopile array; and heart rate, using a pulse photosensor). A support vector machine (SVM) classification method was used to discriminate influenza children from healthy children based on vital signs. To assess the classification performance of the screening system that uses the SVM, we conducted influenza screening for 70 children (i.e., 27 seasonal influenza patients (11 ± 2 years) at a pediatric clinic and 43 healthy control subjects (9 ± 4 years) at a pediatric dental clinic) in the winter of 2013-2014. Results: The screening system using the SVM identified 26 subjects with influenza (22 of the 27 influenza patients and 4 of the 43 healthy subjects). The system discriminated 44 subjects as healthy (5 of the 27 influenza patients and 39 of the 43 healthy subjects), with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 81.5%, 90.7%, 84.6%, and 88.6%, respectively. Conclusion: The SVM-based screening system achieved classification results for the outpatient children based on vital signs with comparatively high NPV within 10 seconds. At pediatric clinics and hospitals, our system seems potentially useful in the first screening step for infections in the future.
Extracted statement — Objective: Screening
Research field: Science
Title: An empirical study on the relationship between the use of agile practices and the success of Scrum projects
Abstract: In this article, factors considered critical for the success of projects managed using Scrum are correlated to the results of software projects in industry. Using a set of 25 factors compiled in by other researchers, a cross section survey was conducted to evaluate the presence or application of these factors in 11 software projects that used Scrum in 9 different software companies located in Recife-PE, Brazil. The questionnaire was applied to 65 developers and Scrum Masters, representing 75% (65/86) of the professionals that have participated in the projects. The result was correlated with the level of success achieved by the projects, measured by the subjective perception of the project participant, using Spearman's rank correlation coefficient. The main finding is that only 32% (8/25) of the factors correlated positively with project success, raising the question of whether the factors hypothesized in the literature as being critical to the success of agile software projects indeed have an effect on project success. Given the limitations regarding the generalization of this result, other forms of empirical results, in particular case-studies, are needed to test this question.
Extracted statement — Agile
Method: Scrum