Label propagation based saliency detection via graph design

Integrating Open Government Data with Stratosphere for More Transparency

BackgroundGovernments worldwide are facing growing pressure to increase transparency, as citizens demand greater insight into decision-making processes and public spending. An example is the release of open healthcare data to researchers, as healthcare is one of the top economic sectors. Significant information systems development and computational experimentation are required to extract meaning and value from these datasets. We use a large open health dataset provided by the New York State Statewide Planning and Research Cooperative System (SPARCS) containing 2.3 million de-identified patient records. One of the fields in these records is a patient’s length of stay (LoS) in a hospital, which is crucial in estimating healthcare costs and planning hospital capacity for future needs. Hence it would be very beneficial for hospitals to be able to predict the LoS early. The area of machine learning offers a potential solution, which is the focus of the current paper.MethodsWe investigated multiple machine learning techniques including feature engineering, regression, and classification trees to predict the length of stay (LoS) of all the hospital procedures currently available in the dataset. Whereas many researchers focus on LoS prediction for a specific disease, a unique feature of our model is its ability to simultaneously handle 285 diagnosis codes from the Clinical Classification System (CCS). We focused on the interpretability and explainability of input features and the resulting models. We developed separate models for newborns and non-newborns.ResultsThe study yields promising results, demonstrating the effectiveness of machine learning in predicting LoS. The best R2 scores achieved are noteworthy: 0.82 for newborns using linear regression and 0.43 for non-newborns using catboost regression. Focusing on cardiovascular disease refines the predictive capability, achieving an improved R2 score of 0.62. The models not only demonstrate high performance but also provide understandable insights. For instance, birth-weight is employed for predicting LoS in newborns, while diagnostic-related group classification proves valuable for non-newborns.ConclusionOur study showcases the practical utility of machine learning models in predicting LoS during patient admittance. The emphasis on interpretability ensures that the models can be easily comprehended and replicated by other researchers. Healthcare stakeholders, including providers, administrators, and patients, stand to benefit significantly. The findings offer valuable insights for cost estimation and capacity planning, contributing to the overall enhancement of healthcare management and delivery.

Предметом вивчення у статті є процес тестування методів побудови рекомендаційних систем на основі відкритих наборів даних у мережі Інтернет. Метою є дослідження відкритих наборів даних веб-ресурсів у контексті застосування їх для тестування різних методів побудови рекомендаційних систем. Завдання: дослідити сучасні вебплатформи з відкритими наборами даних та можливість застосування їх даних для тестування якості роботи різних рекомендаційних систем. Отримані такі результати: Розглянуто найбільш популярні веб-платформи з відкритими наборами різнотипних мережевих даних. Здійснено порівняльний аналіз цих платформ з точки зору наявності вільного доступу до завантаження даних, їх функціональності та територіальної приналежності, формату даних та зручності для подальшого використання для машинного навчання, а також можливості застосування для тестування рекомендаційних систем. Також проведено оцінку актуальності даних, що зберігаються у репозиторіях з вільним доступом та наявності їх оновлення з часом. Висновки. Досліджено веб-платформи, що містять відкриті набори даних, які можна використати для тестування рекомендаційних систем. Основними перевагами більшості платформ є підтримка сучасних форматів даних та умовно вільний або вільний доступ. Серед недоліків розглянутих платформ слід зазначити недостатню структурованість деяких наборів даних, зокрема текстових, що значно обмежує їх застосування для тестування методів контентної фільтрації. Окрім того, одним з факторів, що обмежує використання відкритих наборів даних є їхня актуальність, тому що деякі набори, що зберігаються на платформах, є застарілими та не оновлюються. Усі розглянуті набори даних можуть бути застосовані для дослідницьких цілей та тестування роботи рекомендаційних систем.

e13604 Background: Cancer is a complex and polymorphic disease. Large open datasets are currently available, though unstructured and heterogeneous. As such, expertise from various fields is needed to curate and analyze these datasets. Epidemium was set up as an innovative multidisciplinary community-based program to tackle these challenges and offer a new approach to cancer epidemiology research using big data. Methods: Epidemium combines cancer knowledge and data science expertise with an open, inclusive and collaborative approach. It invites any willing participant to submit a project or to join an existing one, regardless of his background. To foster synergies within the open community, regular conferences, meetings, and data challenges were set up, combined with a dedicated web platform - built using open source software - hosting selected resources and open data sets (WHO, Roche, FAO, World Bank, ILO, etc.). A six-month community-oriented Challenge allowed teams to make contributions that were reviewed by an Independent Ethics Committee and a Scientific Committee. Results: As of June 2016, Epidemium had gathered 678 members with different backgrounds: 19% Health Professionals, 28% Computer Scientists, and 53% Data Scientists. 15 projects were submitted to the Challenge and 3 were awarded: Baseline, CancerViz, and ELSE, focusing respectively on risk and protective factors of cancer, distribution of cancer in time and space, and environmental changes and cancer. All contributions (3.600 on the wiki) were shared in open source. Conclusions: Epidemium provides a proof of concept showing that collective intelligence can be leveraged to tackle complex problems such as cancer epidemiology and big data management. The proposed framework accelerates research initiatives through interconnected tools, allowing real time analysis and collaboration within an open multidisciplinary ecosystem.

Many application scenarios for image recognition require learning of deep networks from small sample sizes in the order of a few hundred samples per class. Then, avoiding overfitting is critical. Common techniques to address overfitting are transfer learning, reduction of model complexity and artificial enrichment of the available data by, e.g., data augmentation. A key idea proposed in this paper is to incorporate additional samples into the training that do not belong to the classes of the target task. This can be accomplished by formulating the original classification task as an open set classification task. While the original closed set classification task is not altered at inference time, the recast as open set classification task enables the inclusion of additional data during training. Hence, the original closed set classification task is augmented with an open set task during training. We therefore call the proposed approach open set task augmentation. In order to integrate additional task-unrelated samples into the training, we employ the entropic open set loss originally proposed for open set classification tasks and also show that similar results can be obtained with a modified sum of squared errors loss function. Learning with the proposed approach benefits from the integration of additional “unknown” samples, which are often available, e.g., from open data sets, and can then be easily integrated into the learning process. We show that this open set task augmentation can improve model performance even when these additional samples are rather few or far from the domain of the target task. The proposed approach is demonstrated on two exemplary scenarios based on subsets of the ImageNet and Food-101 data sets as well as with several network architectures and two loss functions. We further shed light on the impact of the entropic open set loss on the internal representations formed by the networks. Open set task augmentation is particularly valuable when no additional data from the target classes are available—a scenario often faced in practice.

Open data sets that contain personal information are susceptible to adversarial attacks even when anonymized. By performing low-cost joins on multiple datasets with shared attributes, malicious users of open data portals might get access to information that violates individuals' privacy. However, open data sets are primarily published using a release-and-forget model, whereby data owners and custodians have little to no cognizance of these privacy risks. We address this critical gap by developing a visual analytic solution that enables data defenders to gain awareness about the disclosure risks in local, joinable data neighborhoods. The solution is derived through a design study with data privacy researchers, where we initially play the role of a red team and engage in an ethical data hacking exercise based on privacy attack scenarios. We use this problem and domain characterization to develop a set of visual analytic interventions as a defense mechanism and realize them in PRIVEE, a visual risk inspection workflow that acts as a proactive monitor for data defenders. PRIVEE uses a combination of risk scores and associated interactive visualizations to let data defenders explore vulnerable joins and interpret risks at multiple levels of data granularity. We demonstrate how PRIVEE can help emulate the attack strategies and diagnose disclosure risks through two case studies with data privacy experts.

PurposeThis paper aims to examine the use and acceptance of open government data (OGD) of different stakeholder groups in India.Design/methodology/approachFollowing an empirical investigation among 244 respondents in India, the paper deploys path analysis via LISREL. The conceptual construction of the Unified Theory of Acceptance and Use of Technology (UTAUT) framework is used to assess the behavioral intention to use and accept OGD. Multiple regression analysis informs the relationship of demographic variables with the behavioral intention to use and accept OGD.FindingsPath analysis showed that there is an increased use and acceptance of OGD among the respondents. Multiple regression analysis shows that men are more likely to use open data sets than women. Individual differences are found among the respondents with regard to OGD usage. Thus, while men may tap open data sets in line with their purposes and professional backgrounds, women are likely to tap the data sets even for non-professional purposes. Furthermore, the respondents who are younger in age are more behaviorally inclined to accepting and using OGD than their older counterparts.Practical implicationsThe Indian government needs to popularize and familiarize OGD initiatives among the people to a greater extent. The utilitarian value of OGD may be provided when data sets are more user-friendly, frequently updated and accurate. This would facilitate in better provision of public services besides appreciating the public sentiment. Further, with increased interaction between citizens and the government, public accountability and transparency may be better realized.Social implicationsThe study shows that different groups of people are engaging in tapping information through government websites and related portals. Societal influence was found to be an important factor which predicts the acceptance and usage of OGD. An infrastructure can help to enable the use of OGD.Originality/valueHitherto, studies have focused on OGD initiatives in the developed countries, but extant literature is scarce on developing countries. Therefore, this study seeks to fill the literature gap by probing OGD use and acceptance among different stakeholder groups in India.

Large open health datasets present unique opportunities for studies that when well-designed, conducted, and reported, can offer valuable contributions to health and medicine. However, recent years have seen a concerning proliferation of analyses lacking robust or novel findings. In this Editorial, we provide guidance to authors for conducting and reporting high-quality secondary analyses using these datasets.

ImportanceThe PAWPER tape system is one of the three most accurate paediatric weight estimation systems in the world. The latest version of the tape, which does not rely on a subjective assessment of habitus, is the PAWPER XL-MAC method which uses length and mid-arm circumference (MAC) to estimate weight. It was derived and validated in a population in the USA and has not yet been fully validated in a population from a resource-limited setting.ObjectiveThe objective of this study was to evaluate the performance of the PAWPER XL-MAC tape weight estimation system in a large dataset sample of children from resource-limited settings.MethodsThis was a “virtual” study in which weight estimates were generated using the PAWPER XL-MAC tape and Broselow tape 2007B and 2011A editions in a very large open access dataset. The dataset contained anthropometric information of children aged 6 to 59 months from standardised nutritional surveys in 51 low- and middle-income countries. The performance of PAWPER XL-MAC method was compared with the Broselow tape and a new length- and habitus-based tape, the Ralston method.Main outcomes and measuresThe bias of the weight estimation methods was assessed using the mean percentage error (MPE) and precision using the 95% limits of agreement (LOA) of the MPE. The overall accuracy was denoted by the percentage of weight estimates falling within 10% and 20% of actual weight (abbreviated as p10 and p20 respectively).ResultsThe MPE (LOA) for the PAWPER XL-MAC tape, the Broselow 2007B and 2011A and Ralston method were 1.9 (-15.3, 19.2), 5.4 (-15.9, 26.7), 7.7 (-13.3, 30.5) and -0.7 (-20.2, 19.3) respectively. The p10 and p20 for each method were 79.3% and 96.9% for the PAWPER XL-MAC tape, 64.3% and 91.0% for the Broselow tape 2007B, 55.5% and 85.9% for the Broselow tape 2011A and 67.4 and 94.0% for the Ralston method respectively. The PAWPER XL-MAC system was statistically significantly more accurate than the Broselow tape 2011A, the Broselow tape 2007B and the Ralston method. The relative difference in accuracy (p10) was 43% (odds ratio 4.4 (4.4, 4.5), p<0.001), 23% (odds ratio 2.9 (2.8, 2.9), p<0.001) and 18% (odds ratio 1.8 (1.8, 1.8), p<0.001) compared to each method, respectively.Conclusions and relevanceThe PAWPER XL-MAC tape performed well in this study and was statistically significantly more accurate than both the Broselow tape editions and the Ralston method. This difference was substantial and clinically important. The tape did not perform as well at extremes of habitus-type, however, and might benefit from recalibration.

Applied thin-layer chromatography: best practice and avoidance of mistakes

Open data is becoming increasingly popular in a wide range of service domains; however, most open datasets in Taiwan remain separate. The lack of linked open data (LOD) makes it difficult to locate and combine open datasets for the creation of innovative applications. In this study, we sought to facilitate the spread of open data in Taiwan using a novel approach referred to as define–produce–invoke (DPI). The proposed scheme employs a newly defined data query language, called LODQL (LOD query language), to allow the definition of rules for the generation of LOD by data experts. We also developed an LOD engine, which is able to produce linked open datasets and allow application developers to access LOD by invoking RESTful services. This scheme also allows data visualizations indicating the relevance of open datasets and the associations among open data items. Experiments demonstrate the feasibility and effectiveness of the proposed DPI approach.

Rich heterogeneous data provided by social networks can be very big, which imposes considerable challenges for object extraction and image classification. Therefore, the objective of this work is to propose an unsupervised object cosegmentation method that could be notably efficient to improve image classification performance. The main goal of cosegmentation is to extract the salient common objects within each image. To this end, we propose to minimize an energy function based on the Markov Random Field using the saliency detection, while considering linear dependence of generated foreground histograms of the input image collection. In fact, the saliency detection is processed in two steps. In each image, we detect salient objects, by considering appearance similarity and spatial distributions of image pixels. Then, fuzzy quantification is used to correct the belonging of pixels to the foreground objects. Finally, an iterative optimization permits to enhance the final segmentation results. The proposed method has been validated as a preprocessing step for image classification. Indeed, to enhance cosegmentation-based classification performance, we have applied a semi-supervised object classification based on ensemble projection. Qualitative and quantitative evaluations of the proposed cosegmentation and classification techniques on the iCoseg, CDS and Oxford Flowers 17 datasets demonstrate the effectiveness of the proposed framework.

Convolutional neural networks (CNN) are a powerful deep learning method for medical image segmentation. However performance in clinical practice often deteriorates when attempting to generalize models trained from a particular source domain to a different target domain (e.g. different vendor, acquisition parameters, protocols). To address this issue, domain adaptation has attracted increasing attention because it can minimize distribution differences among different but related domains. Extending from this prior work, we introduce Co-Unet-GAN, a co-learning domain adaptation and segmentation model addressing the domain shift problem. In this model, we train a Unet segmentation network and an image translation generative adversarial network (GAN) together to generalize performance across domains given supervised data only in the source domain. We evaluate our model on two large open echocardiography datasets, using the CAMUS set as supervised source domain and EchoNet-Dynamic as the unsupervised target. In this context, we obtain mean absolute error on ejection fraction of 9.67% on Co-Unet-GAN compared to 11.28% for a previously published Unet-GAN. Our Co-Unet-GAN for image translation and segmentation is a promising solution to the domain shift problem.

Spontaneous fluctuations of resting-state functional connectivity have been studied in many ways, but grasping the complexity of brain activity has been difficult. Dimensional complexity measures, which are based on Eigenvalue (EV) spectrum analyses (e.g., Ω entropy) have been successfully applied to EEG data, but have not been fully evaluated on functional MRI recordings, because only through the recent introduction of fast multiband fMRI sequences, feasable temporal resolutions are reached. Combining the Eigenspectrum normalization of Ω entropy and the scalable architecture of the so called Multivariate Principal Subspace Entropy (MPSE) leads to a new complexity measure, namely normalized MPSE (nMPSE). It allows functional brain complexity analyses at varying levels of EV energy, independent from global shifts in data variance. Especially the restriction of the EV spectrum to the first dimensions, carrying the most prominent data variance, can act as a filter to reveal the most discriminant factors of dependent variables. Here we look at the effects of healthy aging on the dimensional complexity of brain activity. We employ a large open access dataset, providing a great number of high quality fast multiband recordings. Using nMPSE on whole brain, regional, network and searchlight approaches, we were able to find many age related changes, i.e., in sensorimotoric and right inferior frontal brain regions. Our results implicate that research on dimensional complexity of functional MRI recordings promises to be a unique resource for understanding brain function and for the extraction of biomarkers.

Sharing research artifacts is known to help people to build upon existing knowledge, adopt novel contributions in practice, and increase the chances of papers receiving attention. In Model-Driven Engineering (MDE), openly providing research artifacts plays a key role, even more so as the community targets a broader use of AI techniques, which can only become feasible if large open datasets and confidence measures for their quality are available. However, the current lack of common discipline-specific guidelines for research data sharing opens the opportunity for misunderstandings about the true potential of research artifacts and subjective expectations regarding artifact quality. To address this issue, we introduce a set of guidelines for artifact sharing specifically tailored to MDE research. To design this guidelines set, we systematically analyzed general-purpose artifact sharing practices of major computer science venues and tailored them to the MDE domain. Subsequently, we conducted an online survey with 90 researchers and practitioners with expertise in MDE. We investigated our participants' experiences in developing and sharing artifacts in MDE research and the challenges encountered while doing so. We then asked them to prioritize each of our guidelines as essential, desirable, or unnecessary. Finally, we asked them to evaluate our guidelines with respect to clarity, completeness, and relevance. In each of these dimensions, our guidelines were assessed positively by more than 92\% of the participants. To foster the reproducibility and reusability of our results, we make the full set of generated artifacts available in an open repository at \texttt{\url{https://mdeartifacts.github.io/}}.