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Purpose: To give an overview of the multicenter Prostate Cancer Outcomes (PCO) study, involving paper-based and web-based collection of patient-reported outcome measures (PROM) in patients undergoing local treatment for prostate cancer in certified centers in Germany. The PCO study is part of the larger Movember-funded TrueNTH Global Registry. The article reports on the study's design and provides a brief progress report after the first 2 years of data collection. Methods: Prostate cancer centers (PCCs) certified according to German Cancer Society requirements were invited to participate in collecting patient-reported information on symptoms and function before and at least once (at 12 months) after treatment. The data were matched with disease and treatment information. This report describes progress in patient inclusion, response rate, and variations between centers relative to online/paper use, and also data quality, including recruitment variations relative to treatment in the first participating PCCs. Results: PCC participation increased over time; 44 centers had transferred data for 3094 patients at the time of this report. Patient recruitment varied widely across centers. Recruitment was highest among patients undergoing radical prostatectomy. The completeness of the data was good, except for comorbidity information. Conclusions: The PCO study benefits from a quality improvement system first established over 10 years ago, requiring collection and harmonization of a predefined clinical dataset across centers. Nevertheless, establishing a PROM routine requires substantial effort on the part of providers and constant monitoring in order to achieve high-quality data. The findings reported here may be useful for guiding implementation in similar initiatives. [ABSTRACT FROM AUTHOR]

The article discusses the papers presented at the European Conference on Machine Learning (ECML) and the European Conference on Principles of Data Mining and Knowledge Discovery (PKDD). It notes that the papers are related to machine learning and data mining communities including "Sparse kernel SVMs via cutting-plane training," which features the state of the art efficiency in inducting and evaluating support vector machine (SVM) models. Other topics of the papers are also presented.

In recent years, the business ecosystem has focused on understanding new ways of automating, collecting, and analyzing data in order to improve products and business models. These actions allow operations management to improve prediction, value creation, optimization, and automatization. In this study, we develop a novel methodology based on data-mining techniques and apply it to identify insights regarding the characteristics of new business models in operations management. The data analyzed in the present study are user-generated content from Twitter. The results are validated using the methods based on Computer-Aided Text Analysis. Specifically, a sentimental analysis with TextBlob on which experiments are performed using vector classifier, multinomial naïve Bayes, logistic regression, and random forest classifier is used. Then, a Latent Dirichlet Allocation is applied to separate the sample into topics based on sentiments to calculate keyness and p-value. Finally, these results are analyzed with a textual analysis developed in Python. Based on the results, we identify 8 topics, of which 5 are positive (Automation, Data, Forecasting, Mobile accessibility and Employee experiences), 1 topic is negative (Intelligence Security), and 2 topics are neutral (Operational CRM, Digital teams). The paper concludes with a discussion of the main characteristics of the business models in the OM sector that use DDI. In addition, we formulate 26 research questions to be explored in future studies. [ABSTRACT FROM AUTHOR]

With the popularization of 5G, the number of mobile devices has gone through explosive growth, the information and contents in the network have become richer, and the scale of the Social Internet of Things (SIoT) has been expanding. However, massive content transmission has also increased the burden of SIoT. In order to break through the limitations of IP architecture in addressing scheme, user interest mining and content caching, this article introduces Information-Centric Networking (ICN) into SIoT and proposes an ICN-SIoT solution. To reduce network latency, lower energy consumption, and avoid node death in large-scale data transmission, this paper designs a cloud-edge collaboration-based ICN-SIoT framework and constructs a content transmission algorithm based on multi-community and edge-caching (CTME). CTME dynamically detects two kinds of communities for mobility-supported routing of two types of packets defined in ICN, which effectively reduces packet forwarding times and node energy consumption, and avoid node death due to energy exhaustion. It also uses edge caching to manage node cache space to contribute to effective data transmission. The experimental results show that CTME has an outstanding performance in reducing energy consumption and improving data transmission efficiency. This study will be of great relevance to the development of SIoT. [ABSTRACT FROM AUTHOR]

Background: The concept of physical literacy has received increased research and international attention recently. Where intervention programs and empirical research are gaining momentum, their operationalizations differ significantly.Objective: The objective of this study was to inform practice in the measure/assessment of physical literacy via a systematic review of research that has assessed physical literacy (up to 14 June, 2017).Methods: Five databases were searched using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols guidelines, with 32 published articles meeting the inclusion criteria. English-language, peer-reviewed published papers containing empirical studies of physical literacy were analyzed using inductive thematic analysis.Results: Qualitative methods included: (1) interviews; (2) open-ended questionnaires; (3) reflective diaries; (4) focus groups; (5) participant observations; and (6) visual methods. Quantitative methods included: (1) monitoring devices (e.g., accelerometers); (2) observations (e.g., of physical activity or motor proficiency); (3) psychometrics (e.g., enjoyment, self-perceptions); (4) performance measures (e.g., exergaming, objective times/distances); (5) anthropometric measurements; and (6) one compound measure. Of the measures that made an explicit distinction: 22 (61%) examined the physical domain, eight (22%) the affective domain; five (14%) the cognitive domain; and one (3%) combined three domains (physical, affective, and cognitive) of physical literacy. Researchers tended to declare their philosophical standpoint significantly more in qualitative research compared with quantitative research.Conclusions: Current research adopts diverse often incompatible methodologies in measuring/assessing physical literacy. Our analysis revealed that by adopting simplistic and linear methods, physical literacy cannot be measured/assessed in a traditional/conventional sense. Therefore, we recommend that researchers are more creative in developing integrated philosophically aligned approaches to measuring/assessing physical literacy. Future research should consider the most recent developments in the field of physical literacy for policy formation. [ABSTRACT FROM AUTHOR]

Purpose: Asia Partnership Conference of Pharmaceutical Associations (APAC) promote regulatory agility of four important best practices i.e. reliance, digital platform, accepting electronic document and process integration. Dialogues and strong partnership witnessed reforms and efficiencies amidst the pandemic. In tracking the progress of regulatory agility, APAC identifies areas for improvement and recommends prioritizing these areas for change. Methods: As one voice, 13 main industry associations under the umbrella of APAC sent joint letters to our National Regulatory Authorities (NRAs) with a call to maintain regulatory agility and consider new ways of working. Consequently, APAC surveyed its member associations to measure regulatory agilities implemented by the NRAs during 2020 and 2021 in view of the pandemic. Results: This paper reports progress in implementing regulatory agility, e.g. the number of economies that can accept electronic Certificate of Pharmaceutical Products (eCPP) has reached 100% for the economies that require CPP and more than 90% can waive onsite inspection in the presence of Good Manufacturing Practice (GMP) certificate and/or inspection report. The paper also features the progress made in Malaysia, the Philippines, and the ASEAN (Association of South East Asian Nations) regional reliance initiative to reduce inefficiencies and duplications. Conclusions: We have demonstrated the power of working together to enable regulatory agilities and efficiencies. APAC will continue to track the progress of all economies including India within the areas for improvement prioritized and discussed in this paper. APAC is also committed to working with key stakeholders including our NRAs in Asia to sustain and enable a new era of innovation ushered in by COVID-19 to benefit patients. [ABSTRACT FROM AUTHOR]

Semantic Web content extracting are the augmentation of the present web where the data is given in the better importance and allowing users to work close by close by utilizing the assets of web. Due to expanding of web content at speedier rate, it is difficult to adapt up to new procedures of separating to create the exact data in light of the user query. Later, number of scientists has taking a shot at enhancing the aftereffects of Web content mining by abusing Web Semantic Structure and can be executed in the web expresses through machine-processable data to bolsters the errands of users. But these methods bring about low precision [unseemly data from different inquiry result (equivocal)] and getting of off base data (i.e. low recall) with high surfing time. Therefore, the paper proposes the Semantic Web content mining utilizing two ways to deal with dodge low precision and low recall: the one is Kalman–Bucy filters, to channel the comparable information; the next is adaptive Helmholtz machine, to get the exact data in light of the user query. The proposed Kalman–Bucy filtering approach with adaptive Helmholtz machine execution is be assessed and contrasted with existing methodologies regarding retrieval accuracy and surfing time by dual predicted output and the optimized information retrieval rather than probability. [ABSTRACT FROM AUTHOR]

Electronic cigarette use among adolescents has increased in the past decade. Little is known about the perception and use of e-cigarettes in Vietnam, especially among young people. This study examines the perceptions and use of electronic cigarettes among young adults in Vietnam. The study uses data from a survey of adult tobacco consumption among adults at the provincial level in 2020, which was funded and managed by the Vietnam Tobacco Control Fund—Ministry of Health. Data were collected from 34 provinces and cities throughout Vietnam and managed using REDCap software. The survey engaged 80,166 participants, of which 9478 were young adults aged 15–24 were selected and reported in this paper. The results show that, at the time of the survey, 2.4% of young Vietnamese adults were smoking e-cigarettes. Many survey participants cited more than one reason for smoking e-cigarettes. These reasons included preferring the taste of e-cigarettes (64.0%), disliking the odour of other cigarettes (46.2%), following the example of family members (44.9%), and believing e-cigarettes threatened fewer health risks (39.1%). Almost half of the e-cigarette users wanted to quit (48%). E-cigarette use was associated with males living in urban areas who are aware of the Law on Prevention and Control of Tobacco Harms. The findings of this study provide evidential data that support public health policies aimed at reducing and ceasing e-cigarette use among young people. [ABSTRACT FROM AUTHOR]

Introduction: Maternal and child health (MCH) services are critical for vulnerable populations. Workforce shortages, poor retention, and gaps in necessary trainings impede the capacity of public health systems to address needs. This manuscript characterizes the current MCH workforce, MCH program applicants and graduates, and describe findings within a national context to devise elements of a recruitment and retention strategy. Methods: Data were obtained for public health program applicants, first-destination employment outcomes, and worker perceptions and demographics. Data were stratified according to the MCH and total public health workforce and by local, state, and national totals. Data were characterized by degree type, discipline, demographics, and employment outcomes. Results: MCH staff constitute 11% of the state and local governmental public health workforce. MCH staff are approximately as diverse, have higher educational attainment, and are more likely to hold nursing degrees than the rest of the public health workforce. Yet, just 14% of MCH staff hold any type of public health degree. The MCH pipeline from academia appears modestly sized, with approximately 5% of applicants between 2017 and 2021 applying to a MCH master's degree. Discussion: The MCH workforce has a lower proportion of formal training or degrees in public health, though trends seem to indicate improvements. However, it is critical that a multi-faceted recruitment and retention strategy be coordinated by a broad range of stakeholders. These efforts will serve to improve the capability and capacity of the public health system to address critical needs of increasingly diverse MCH populations. Significance: In order to modernize and reimagine the academic-public health pipeline, it is critical to better understand how many applicants and graduates exist within Maternal and Child Health programs across the US, and their characteristics. This manuscript connects that information with the most recently available public health workforce information on demographics, workplace perceptions, and intent to leave among staff at state and local health departments. Data presented in this paper allow the most comprehensive characterization of the MCH academia->practice pipeline to-date, identifies a fundamental disconnect in those career pathways, and offers options to repair that break. [ABSTRACT FROM AUTHOR]

An asymmetric correlation measure commonly used in social economics, called the Gini correlation, is defined between a numerical response and a rank. We generalize the definition of this correlation so that it can be applied to data mining. The new definition, called the generalized Gini correlation, is found to include special cases that are equivalent to common evaluation measures used in data mining, for example, the LIFT measures for a binary response and the expected profit measure for a monetary response. We consider estimation and inference regarding this generalized Gini correlation. The asymptotic distribution of the estimated correlation is derived with the help of some empirical process theory. We consider several ways of constructing confidence intervals and demonstrate their performance numerically. Our paper is interdisciplinary and makes contributions to both the Gini literature and the literature of statistical inference of performance measures in data mining. [ABSTRACT FROM AUTHOR]

The aim of this paper is to describe the design and the preliminary validation of a platform developed to collect and automatically analyze biomedical signals for risk assessment of vascular events and falls in hypertensive patients. This m-health platform, based on cloud computing, was designed to be flexible, extensible, and transparent, and to provide proactive remote monitoring via data-mining functionalities. A retrospective study was conducted to train and test the platform. The developed system was able to predict a future vascular event within the next 12 months with an accuracy rate of 84 % and to identify fallers with an accuracy rate of 72 %. In an ongoing prospective trial, almost all the recruited patients accepted favorably the system with a limited rate of inadherences causing data losses (<20 %). The developed platform supported clinical decision by processing tele-monitored data and providing quick and accurate risk assessment of vascular events and falls. [ABSTRACT FROM AUTHOR]

Human preverbal development refers to the period of steadily increasing vocal capacities until the emergence of a child's first meaningful words. Over the last decades, research has intensively focused on preverbal behavior in typical development. Preverbal vocal patterns have been phonetically classified and acoustically characterized. More recently, specific preverbal phenomena were discussed to play a role as early indicators of atypical development. Recent advancements in audio signal processing and machine learning have allowed for novel approaches in preverbal behavior analysis including automatic vocalization-based differentiation of typically and atypically developing individuals. In this paper, we give a methodological overview of current strategies for collecting and acoustically representing preverbal data for intelligent audio analysis paradigms. Efficiency in the context of data collection and data representation is discussed. Following current research trends, we set a special focus on challenges that arise when dealing with preverbal data of individuals with late detected developmental disorders, such as autism spectrum disorder or Rett syndrome. [ABSTRACT FROM AUTHOR]

Citations are commonly used to measure academic impacts of scientific publications, including books. However, citation frequencies of books are single numerical evaluation metrics. It neglects details about books (e.g. contents), which may lead to the decline in comprehensiveness of evaluation results. Hence, fine-grained mining on books' citation information to integrate frequency metrics and content metrics can obtain more reliable evaluation results. Books' citation literatures (i.e. literatures cited books) present citation frequencies of books, and reflect citation intentions, topics and domains simultaneously. Existing research focused on analysing citation frequencies, authors or citation contexts of citation literatures to conduct citation analysis. It may be costly for collecting citation contexts and neglected latent information of citation literatures, such as impact scopes or topics of books reflected by citation literatures. Therefore, in this paper, we conducted fine-grained analysis on books' citation literatures to assess whether citation literatures could be systematically used for indicators of books' wider impacts. Specifically, we firstly collected books and corresponding information about their citation literatures. Then, we extracted multi-dimensional metrics via multi-granularity mining on citation literatures, and got assessment results by integrating content-level and frequency-level metrics. Finally, we compared assessment results based on citation literatures and existing metrics for assessing books' impacts to verify assessment results. Experimental results infer that citation literatures are a promising source for book impact assessment, especially books' academic impacts. [ABSTRACT FROM AUTHOR]

There exists a large and growing number of proposed estimation methods but little conclusive evidence ranking one method over another. Prior effort estimation studies suffered from “conclusion instability”, where the rankings offered to different methods were not stable across (a) different evaluation criteria; (b) different data sources; or (c) different random selections of that data. This paper reports a study of 158 effort estimation methods on data sets based on COCOMO features. Four “best” methods were detected that were consistently better than the “rest” of the other 154 methods. These rankings of “best” and “rest” methods were stable across (a) three different evaluation criteria applied to (b) multiple data sets from two different sources that were (c) divided into hundreds of randomly selected subsets using four different random seeds. Hence, while there exists no single universal “best” effort estimation method, there appears to exist a small number (four) of most useful methods. This result both complicates and simplifies effort estimation research. The complication is that any future effort estimation analysis should be preceded by a “selection study” that finds the best local estimator. However, the simplification is that such a study need not be labor intensive, at least for COCOMO style data sets. [ABSTRACT FROM AUTHOR]

Several studies have revealed that homeless people suffering from mental health problems are more vulnerable than homeless without those mental health problems. Nevertheless, there is a lack of evidence describing the real circumstances of homeless women. This paper explores the differences between homeless women at high risk of mental ill-health compared with those who do not present this risk. The sample consisted of a group of 120 homeless women in Madrid (Spain). For this study, we collected data on background information (trajectory of homelessness and stressful life events experienced) and current aspects (living conditions, physical health, and social support). The risk of mental ill-health has been measured by the short version of the General Health Questionnaire (GHQ-28). The results showed that homeless women with higher risk of mental ill-health had become homeless at a younger age, had experienced more stressful life events in their lives, had a poorer physical health, felt less happy, had less social support, and a greater feeling of loneliness when compared with homeless women who did not present risk of mental ill-health. Improving knowledge about the risk of mental ill-health among homeless women is essential for the design of specific psychological interventions within this population. [ABSTRACT FROM AUTHOR]

Ranking of search results and ads has traditionally been studied separately. The probability ranking principle is commonly used to rank the search results while the ranking based on expected profits is commonly used for paid placement of ads. These rankings try to maximize the expected utilities based on the user click models. Recent empirical analysis on search engine logs suggests unified click models for both ranked ads and search results (documents). These new models consider parameters of (i) probability of the user abandoning browsing results (ii) perceived relevance of result snippets. However, current document and ad ranking methods do not consider these parameters. In this paper we propose a generalized ranking function-namely Click Efficiency (CE)-for documents and ads based on empirically proven user click models. The ranking considers parameters (i) and (ii) above, optimal and has the same time complexity as sorting. Furthermore, the CE ranking exploits the commonality of click models, hence is applicable for both documents and ads. We examine the reduced forms of CE ranking based upon different underlying assumptions, enumerating a hierarchy of ranking functions. Interestingly, some of the rankings in the hierarchy are currently used ad and document ranking functions; while others suggest new rankings. Thus, this hierarchy illustrates the relationships between different rankings, and clarifies the underlying assumptions. While optimality of ranking is sufficient for document ranking, applying CE ranking to ad auctions requires an appropriate pricing mechanism. We incorporate a second price based mechanism with the proposed ranking. Our analysis proves several desirable properties including revenue dominance over Vickrey Clarke Groves (VCG) for the same bid vector and existence of a Nash equilibrium in pure strategies. The equilibrium is socially optimal, and revenue equivalent to the truthful VCG equilibrium. As a result of its generality, the auction mechanism and the equilibrium reduces to the current mechanisms including Generalized Second Price Auction (GSP) and corresponding equilibria. Furthermore, we relax the independence assumption in CE ranking and analyze the diversity ranking problem. We show that optimal diversity ranking is NP-Hard in general, and a constant time approximation algorithm is not likely. Finally our simulations to quantify the amount of increase in different utility functions conform to the results, and suggest potentially significant increase in utilities. [ABSTRACT FROM AUTHOR]

High-quality development paths in important cities are blurry and lacking. In order to explore the important engine for Chengdu high-quality development, driving forces and obstacles recognition has emerged as a pivotal technological solution. Using the Chengdu in Sichuan province of China as a research area and quantitative data from 2010 to 2019, this study has used content mining to recognize urban high-quality development (UHQD) variables, and calculated variables' weights by entropy weight method, and explored driving forces and obstacles of UHQD by the technique for order preference by similarity to ideal solution (TOPSIS) method. The main findings are: (1) there are 36 UHQD variables; (2) Chengdu high-quality development overall level soars from 2017 to 2019, only with two negative growth rates in 2011, 2015; (3) There are 3 key driving force paths: (1) improving green development by volume of industrial wastewater discharged, comprehensively utilised ratio of industrial solid wastes, harmless treatment rate of domestic garbage; (2) stressing open development by total import and export/GDP, actual use of foreign capital, number of foreign tourists/total tourists; and (3) intensifying shared development by funds for urban residents under basic provision protection. (4) 3 clearing obstacles paths can also realize Chengdu high-quality development: (1) improving innovative development level by R&D internal outlay, patent authorisations, state high-level tech enterprises; (2) optimizing coordinated development level by the proportion of tertiary industry; (3) promoting shared development level by urban basic pension insurance. According to these findings, suggestions are put forward to promote Chengdu high-quality development from the perspective of policy implementation. [ABSTRACT FROM AUTHOR]

Mining method selection (MMS) is one of the core contents in mining design. As the influencing factors of MMS are in general of ambiguity or uncertainty, MMS can therefore be deemed as a complex fuzzy multi-criteria decision making problem. To resolve this problem, an extended multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) approach is studied in this paper. Three major contributions of this study are included. At first, comparative importance is crisp number in most stepwise weight assessment ratio analysis (SWARA) method, while linguistic terms are utilized to determine the criteria weights in the improved SWARA. Then, several Heronian mean (HM) operators related to linguistic neutrosophic numbers (LNNs) are defined to cope with evaluating indicators' interrelations. Special cases are discussed, and primary properties are proved as well. At last, in combination with improved SWARA and HM operators, a novel decision making method, extended MULTIMOORA approach based on LNNs, is proposed. Applying this new approach to selecting the optimal mining method through an example, the feasibility is indicated. In addition, analyses of the strengths of the presented method are performed in comparison with other existing approaches. [ABSTRACT FROM AUTHOR]

In the field of image processing, data mining technique is being implemented in various concepts. Generally, the management of video content with data mining technique became an essential part since there is an increase in the advancement of multimedia and networking technology. Previously, there are certain algorithm such as Apriori and frequency pattern growth algorithm for video management. In this paper, a novel fast frequency pattern algorithm is designed to find the high priority pattern with minimum time. In this concept the data mining process is carried out in vertical format in order to find the pattern with high priority. The simulated results are compared with the existing data mining algorithms and it is found that the proposed algorithm is efficient in aspect of time and memory size. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to propose an intelligent system to automatically monitor the firms' engagement in e-commerce by analyzing online data retrieved from their corporate websites. The design of the proposed system combines web content mining and scraping techniques with learning methods for Big Data. Corporate websites are scraped to extract more than 150 features related to the e-commerce adoption, such as the presence of some keywords or a private area. Then, these features are taken as input by a classification model that includes dimensionality reduction techniques. The system is evaluated with a data set consisting of 426 corporate websites of firms based in France and Spain. The system successfully classified most of the firms into those that adopted e-commerce and those that did not, reaching a classification accuracy of 90.6%. This demonstrates the feasibility of monitoring e-commerce adoption from online data. Moreover, the proposed system represents a cost-effective alternative to surveys as method for collecting e-commerce information from companies, and is capable of providing more frequent information than surveys and avoids the non-response errors. This is the first research work to design and evaluate an intelligent system to automatically detect e-commerce engagement from online data. This proposal opens up the opportunity to monitor e-commerce adoption at a large scale, with highly granular information that otherwise would require every firm to complete a survey. In addition, it makes it possible to track the evolution of this activity in real time, so that governments and institutions could make informed decisions earlier. [ABSTRACT FROM AUTHOR]

Schools of Public Health have a commitment to engage in practice-based research and be involved in collaborative partnerships. In 2016 the faculty, staff, and students from the University of Nebraska Medical Center College of Public Health and the Nebraska Department of Health and Human Services, Division of Behavioral Health collaborated to develop and administer a comprehensive assessment of the mental health and substance use disorder services provided by the Division of Behavioral Health. The purpose of this paper is to describe the process used to develop the trusting and mutually beneficial partnership and the data tools that were created and used to assess and determine the behavioral health needs. It is unrealistic to think that practitioners could undertake a project of this magnitude on their own. It is essential to have identified processes and systems in place for others to follow. [ABSTRACT FROM AUTHOR]

To improve data quality and save cost, clinical trials are nowadays performed using electronic data capture systems (EDCS) providing electronic case report forms (eCRF) instead of paper-based CRFs. However, such EDCS are insufficiently integrated into the medical workflow and lack in interfacing with other study-related systems. In addition, most EDCS are unable to handle image and biosignal data, although electrocardiography (EGC, as example for one-dimensional (1D) data), ultrasound (2D data), or magnetic resonance imaging (3D data) have been established as surrogate endpoints in clinical trials. In this paper, an integrated workflow based on OpenClinica, one of the world's largest EDCS, is presented. Our approach consists of three components for (i) sharing of study metadata, (ii) integration of large volume data into eCRFs, and (iii) automatic image and biosignal analysis. In all components, metadata is transferred between systems using web services and JavaScript, and binary large objects (BLOBs) are sent via the secure file transfer protocol and hypertext transfer protocol. We applied the close-looped workflow in a multicenter study, where long term (7 days/24 h) Holter ECG monitoring is acquired on subjects with diabetes. Study metadata is automatically transferred into OpenClinica, the 4 GB BLOBs are seamlessly integrated into the eCRF, automatically processed, and the results of signal analysis are written back into the eCRF immediately. [ABSTRACT FROM AUTHOR]

A new capable content discovery platform based on multimedia data enrichment is presented in this paper. The platform, known as the IMCOP system, refers to the concept of intelligent discovery and delivery of multimedia content. Relevant state-of-the-art solutions are described in detail in the background section. The overall architecture and the main components of the IMCOP system are presented next. An original concept of Complex Multimedia Objects which extend the MPEG-7 standard to hold the processed data and bind it into content related collections is introduced. Selected results of tests illustrating how the IMCOP system performs in terms of responsiveness and stability under a particular workload are reported. Finally, IMCOP’s advantages in reference to other content discovery platforms are discussed and summed up. [ABSTRACT FROM AUTHOR]

This paper aims to describe delivery and birth outcomes of Aboriginal infants and their mothers in an urban setting on the east coast of Australia. The paper uses a causal pathway approach to consider the role of risk and protective factors for low birthweight. All mothers who delivered at Campbelltown Hospital between October 2005 and May 2007 were eligible. The study included 1,869 non-Aboriginal infants and 178 Aboriginal infants and their mothers. Information on delivery and birthweight was extracted from electronic medical records. Risk factors for poor outcomes were explored using regression and causal pathway analysis. Mothers of Aboriginal infants were younger than mothers of non-Aboriginal infants, and were more likely to be single, less educated, unemployed prior to pregnancy, and live in a disadvantaged neighbourhood. Health and service use was similar. They were significantly more likely to have a vaginal delivery than mothers of non-Aboriginal infants (77% cf 62.5%; χ = 14.6, P < 0.001) and less likely to receive intervention during delivery. Aboriginal infants (3,281.1 g) weighed 137.5 g (95%CI: 54-221 g; P = 0.001) less then non-Aboriginal infants (3,418.7 g). Gestational age, and single mother with incomplete education, prior unemployment, smoking, and living in a disadvantaged neighbourhood were associated with lower birthweight. Maternal vulnerability had a cumulative impact on birthweight. A causal pathway analysis demonstrated the associations between risk factors. [ABSTRACT FROM AUTHOR]

The design of approaches for supporting the user in the navigation of a variety of contents is an interesting area of research with many potential applications. In particular, interactive television (iTV) offers users the opportunity of accessing a huge amount of contents, ranging from general to specialized ones. As a consequence, the exploration of such contents must be supported in some way. In this paper, we present an innovative approach that integrates some recent methodologies and technologies developed in different areas of Artificial Intelligence and the Web: user-model-based adaptation, social networking, semantic annotation, and content sharing. We show how the integration of these technologies can provide interesting opportunities for a new approach to content navigation and discovery based on the possibility of exploring personalized networks of contents, users and concepts. Also, we focus on the specific goal of designing a platform for accessing iTV contents with the aim of providing the user with many alternative ways of exploring and discovering potentially interesting videos. After discussing the application, integrated in a project by Telecom Italia for a new paradigm of iTV, we preset the architecture we designed for integrating the methodologies discussed above. [ABSTRACT FROM AUTHOR]

The key to Deep Web Crawling is to submit valid input values to a query form and retrieve Deep Web content efficiently. In the literature, related work focus only on generic text boxes or entire query forms, causing the problem of 'data islands' or inferior validity of query submission. This paper proposes the concept of Minimum Executable Pattern (MEP), a minimal combination of elements in a query form that can conduct a successful query, and then presents a MEPGeneration method and a MEP-based Deep Web adaptive crawling method. The query form is parsed and partitioned into MEP set, and then local-optimal queries are generated by choosing a MEP in the MEP set and a keyword vector of the MEP. Furthermore, the crawler can make a decision on its termination to balance the trade-off between high coverage of the content and resource consumption. The adoption of MEP is expected to improve the validity of query submission, and adaptive selection of multiple MEPs shows good effect for overcoming the problem of 'data islands'. We present a set of experiments to validate the effectiveness of the proposed method. Experimental results show that our method outperforms the state of art methods in terms of query capability and applicability, and on average, it achieves good coverage by issuing only a few hundred queries. [ABSTRACT FROM AUTHOR]

critical issue of Neural Network based large-scale data mining algorithms is how to speed up their learning algorithm. This problem is particularly challenging for Error Back-Propagation (EBP) algorithm in Multi-Layered Perceptron (MLP) Neural Networks due to their significant applications in many scientific and engineering problems. In this paper, we propose an Adaptive Variable Learning Rate EBP algorithm to attack the challenging problem of reducing the convergence time in an EBP algorithm, aiming to have a high-speed convergence in comparison with standard EBP algorithm. The idea is inspired from adaptive filtering, which leaded us into two semi-similar methods of calculating the learning rate. Mathematical analysis of AVLR-EBP algorithm confirms its convergence property. The AVLR-EBP algorithm is utilized for data classification applications. Simulation results on many well-known data sets shall demonstrate that this algorithm reaches to a considerable reduction in convergence time in comparison to the standard EBP algorithm. The proposed algorithm, in classifying the IRIS, Wine, Breast Cancer, Semeion and SPECT Heart datasets shows a reduction of the learning epochs relative to the standard EBP algorithm. [ABSTRACT FROM AUTHOR]

Abstract  Many organizations provide dialog-based support through contact centers to sell their products, handle customer issues, and address product-and service-related issues. This is usually provided through voice calls—of late, web-chat based support is gaining prominence. In this paper, we consider any conversational text derived from web-chat systems, voice recognition systems etc., and propose a method to identify procedures that are embedded in the text. We discuss here how to use the identified procedures in knowledge authoring and agent prompting. In our experiments, we evaluate the utility of the proposed method for agent prompting. We first cluster the call transcripts to find groups of conversations that deal with a single topic. Then, we find possible procedure-steps within each topic-cluster by clustering the sentences within each of the calls in the topic-cluster. We propose a measure for differentiating between clusters that are procedure-steps and those that are topical sentence collections. Once we identify procedure-steps, we represent the calls as sequences of procedure-steps and perform mining to extract distinct and long frequent sequences which represent the procedures that are typically followed in calls. We show that the extracted procedures are comprehensive enough. We outline an approach for retrieving relevant procedures for a partially completed call and illustrate the utility of distinct collections of sequences in the real-world scenario of agent prompting using the retrieval mechanism. [ABSTRACT FROM AUTHOR]

One of the aims of process mining is to retrieve a process model from an event log. The discovered models can be used as objective starting points during the deployment of process-aware information systems (Dumas et al., eds., Process-Aware Information Systems: Bridging People and Software Through Process Technology. Wiley, New York, 2005) and/or as a feedback mechanism to check prescribed models against enacted ones. However, current techniques have problems when mining processes that contain non-trivial constructs and/or when dealing with the presence of noise in the logs. Most of the problems happen because many current techniques are based on local information in the event log. To overcome these problems, we try to use genetic algorithms to mine process models. The main motivation is to benefit from the global search performed by this kind of algorithms. The non-trivial constructs are tackled by choosing an internal representation that supports them. The problem of noise is naturally tackled by the genetic algorithm because, per definition, these algorithms are robust to noise. The main challenge in a genetic approach is the definition of a good fitness measure because it guides the global search performed by the genetic algorithm. This paper explains how the genetic algorithm works. Experiments with synthetic and real-life logs show that the fitness measure indeed leads to the mining of process models that are complete (can reproduce all the behavior in the log) and precise (do not allow for extra behavior that cannot be derived from the event log). The genetic algorithm is implemented as a plug-in in the ProM framework. [ABSTRACT FROM AUTHOR]

We address the problem of outlying aspects mining: given a query object and a reference multidimensional data set, how can we discover what aspects (i.e., subsets of features or subspaces) make the query object most outlying? Outlying aspects mining can be used to explain any data point of interest, which itself might be an inlier or outlier. In this paper, we investigate several open challenges faced by existing outlying aspects mining techniques and propose novel solutions, including (a) how to design effective scoring functions that are unbiased with respect to dimensionality and yet being computationally efficient, and (b) how to efficiently search through the exponentially large search space of all possible subspaces. We formalize the concept of dimensionality unbiasedness, a desirable property of outlyingness measures. We then characterize existing scoring measures as well as our novel proposed ones in terms of efficiency, dimensionality unbiasedness and interpretability. Finally, we evaluate the effectiveness of different methods for outlying aspects discovery and demonstrate the utility of our proposed approach on both large real and synthetic data sets. [ABSTRACT FROM AUTHOR]

Regulators increasingly require clinical outcome assessment (COA) data for approval. COAs can be collected via questionnaires or digital health technologies (DHTs), yet no single resource provides a side-by-side comparison of tools that collect complementary or related COA measures. We propose how to align ontologies for actively collected and passively monitored COAs into a single framework to allow for rapid, evidence-based, and fit-for-purpose measure selection. [ABSTRACT FROM AUTHOR]

Frequent itemset mining (FIM) is a fundamental research topic, which consists of discovering useful and meaningful relationships between items in transaction databases. However, FIM suffers from two important limitations. First, it assumes that all items have the same importance. Second, it ignores the fact that data collected in a real-life environment is often inaccurate, imprecise, or incomplete. To address these issues and mine more useful and meaningful knowledge, the problems of weighted and uncertain itemset mining have been respectively proposed, where a user may respectively assign weights to items to specify their relative importance, and specify existential probabilities to represent uncertainty in transactions. However, no work has addressed both of these issues at the same time. In this paper, we address this important research problem by designing a new type of patterns named high expected weighted itemset (HEWI) and the HEWI-Uapriori algorithm to efficiently discover HEWIs. The HEWI-Uapriori finds HEWIs using an Apriori-like two-phase approach. The algorithm introduces a property named high upper-bound expected weighted downward closure (HUBEWDC) to early prune the search space and unpromising itemsets. Substantial experiments on real-life and synthetic datasets are conducted to evaluate the performance of the proposed algorithm in terms of runtime, memory consumption, and number of patterns found. Results show that the proposed algorithm has excellent performance and scalability compared with traditional methods for weighted-itemset mining and uncertain itemset mining. [ABSTRACT FROM AUTHOR]

For a number of compelling scientific, operational, and regulatory reasons, the use of electronic data capture is becoming the preferred means of collecting clinical outcome assessment (eg, patient-reported outcome [PRO]) data in clinical trials. Electronic PRO (ePRO) data collection leverages screen-based technologies (eg, handheld devices, tablet computers, and web-based systems) and telephone-based (eg, interactive voice response) systems. Data collection is routinely either site based (ie, clinical study site) or field based (eg, subject’s home, school, or workplace). While tablet computers are often used for site-based PRO data collection, handheld devices have become the mainstay for ePRO data capture in field-based settings. The data collection devices are usually provisioned to the sites or subjects by an ePRO system provider contracted by the clinical trial sponsor. With site-based data collection, study staff are responsible for ensuring subject compliance with the protocol-driven data collection procedures, whereas with field-based data collection, the subject is responsible for compliance with the data entry requirements and sites are accountable for remotely monitoring the data for compliance. In addition to site and subject compliance issues, technology-related factors must be anticipated in order to adhere to the electronic PRO data collection plan. The objective of this paper is to describe study site-, subject-, and technology-related factors that may lead to deviations from the planned electronic collection of PRO data (eg, defaulting to paper-based data collection) and to provide recommendations aimed at preventing potential problems or quickly resolving problems once they occur. [ABSTRACT FROM AUTHOR]

Biomedical data, in particular electronic medical records data, include a large number of variables sampled in irregular fashion, often including both time point and time intervals, thus providing several challenges for analysis and data mining. Classification of multivariate time series data is a challenging task, but is often necessary for medical care or research. Increasingly, temporal abstraction, in which a series of raw-data time points is abstracted into a set of symbolic time intervals, is being used for classification of multivariate time series. In this paper, we introduce a novel supervised discretization method, geared towards enhancement of classification accuracy, which determines the cutoffs that will best discriminate among classes through the distribution of their states. We present a framework for classification of multivariate time series analysis, which implements three phases: (1) application of a temporal-abstraction process that transforms a series of raw time-stamped data points into a series of symbolic time intervals (based on either unsupervised or supervised temporal abstraction); (2) mining these time intervals to discover frequent temporal-interval relation patterns (TIRPs), using versions of Allen's 13 temporal relations; (3) using the patterns as features to induce a classifier. We evaluated the framework, focusing on the comparison of three versions of the new, supervised, temporal discretization for classification (TD4C) method, each relying on a different symbolic-state distribution-distance measure among outcome classes, to several commonly used unsupervised methods, on real datasets in the domains of diabetes, intensive care, and infectious hepatitis. Using only three abstract temporal relations resulted in a better classification performance than using Allen's seven relations, especially when using three symbolic states per variable. Similarly when using the horizontal support and mean duration as the TIRPs feature representation, rather than a binary (existence) representation. The classification performance when using the three versions of TD4C was superior to the performance when using the unsupervised (EWD, SAX, and KB) discretization methods. [ABSTRACT FROM AUTHOR]

Examination of resting state brain activity using electrophysiological measures like complexity as well as functional connectivity is of growing interest in the study of autism spectrum disorders (ASD). The present paper jointly examined complexity and connectivity to obtain a more detailed characterization of resting state brain activity in ASD. Multi-scale entropy was computed to quantify the signal complexity, and synchronization likelihood was used to evaluate functional connectivity (FC), with node strength values providing a sensor-level measure of connectivity to facilitate comparisons with complexity. Sensor level analysis of complexity and connectivity was performed at different frequency bands computed from resting state MEG from 26 children with ASD and 22 typically developing controls (TD). Analyses revealed band-specific group differences in each measure that agreed with other functional studies in fMRI and EEG: higher complexity in TD than ASD, in frontal regions in the delta band and occipital-parietal regions in the alpha band, and lower complexity in TD than in ASD in delta (parietal regions), theta (central and temporal regions) and gamma (frontal-central boundary regions); increased short-range connectivity in ASD in the frontal lobe in the delta band and long-range connectivity in the temporal, parietal and occipital lobes in the alpha band. Finally, and perhaps most strikingly, group differences between ASD and TD in complexity and FC appear spatially complementary, such that where FC was elevated in ASD, complexity was reduced (and vice versa). The correlation of regional average complexity and connectivity node strength with symptom severity scores of ASD subjects supported the overall complementarity (with opposing sign) of connectivity and complexity measures, pointing to either diminished connectivity leading to elevated entropy due to poor inhibitory regulation or chaotic signals prohibiting effective measure of connectivity. [ABSTRACT FROM AUTHOR]

The EEG reflects the activation of large populations of neurons that act in synchrony and propagate to the scalp surface. This activity reflects both the brain's background electrical activity and when the brain is being challenged by a task. Despite strong theoretical and methodological arguments for the use of EEG in understanding the neural correlates of autism, the practice of collecting, processing and evaluating EEG data is complex. Scientists should take into consideration both the nature of development in autism given the life-long, pervasive course of the disorder and the disability of altered or atypical social, communicative, and motor behaviors, all of which require accommodations to traditional EEG environments and paradigms. This paper presents guidelines for the recording, analyzing, and interpreting of EEG data with participants with autism. The goal is to articulate a set of scientific standards as well as methodological considerations that will increase the general field's understanding of EEG methods, provide support for collaborative projects, and contribute to the evaluation of results and conclusions. [ABSTRACT FROM AUTHOR]

Maximum permissible concentrations for chemical elements in soils (MPCs) of Russia, Germany, Canada, the Netherlands and the USA are compared. In Russia, general sanitary, water-transferred and translocation MPCs are created; in Germany, general sanitary and translocation, and in the Netherlands, the USA and Canada, general sanitary. It is found that the Russian MPCs for total contents of chemical elements are adequate for Mn and V, American, for Co, Cu, and Pb; Dutch, for Pb and Zn; Canadian, for Ba, Co, Ni, Pb, V, and Zn, and German, for Cr, Ni, and Pb by taking into account the typical levels of elements in the background soils across the world. The methodologically closest Russian and Dutch standard rates of total contents of chemical elements are compared in this paper with the typical levels for background Retisols gleic, Phaeozems, and Chernozems in the center of Western Siberia. It is established that use of the Dutch MPCs for the soils under study gives fewer cases of false Pb pollution, but in all the soils under consideration the Dutch MPCs for Ba is exceeded. False pollution by total Zn is observed in the top-soil; by Cr, Cu and V, in subsoil horizons of Retisols gleic as well as by Cr, Ni and V, in most samples of Phaeozems and Chernozems. It is concluded that for the soils under consideration the utilization of Russian MPCs gives fewer cases of false pollution by V. But in the background Retisols gleic, Russian MPCs for Mn and Pb accumulated in the biogeochemical barrier in the top-soil horizons are exceeded. Russian MPCs for metal mobile fraction are the most correct for Co, Cr, Cu, Ni and Pb. The MPCs for mobile fraction of biogenic Mn and Zn are underestimated. Background levels (mg/kg in subindex) of total concentration (mobile fraction extracted by acetate-ammonium buffer) are proposed for Ba404–515, Co12–19(0.001–0.23), Cr143–188(0.001–0.72), Cu32–41(0.01–0.72), Mn423–874(0.09–64), Ni30–62(0.003–1.3), Pb18–27(0.003–0.87), V112–170 иZn55–104(0.004–0.42) in Retisols gleic on Tobol Upland, Phaeozems on Ishym Upland, and Chernozems in the Trans-Ural region. [ABSTRACT FROM AUTHOR]

With the increase in the availability of network connectivity, e-commerce has grown rapidly. User products and reviews have grown multi-folds for these online markets, leading to the web's data explosion. A recommender system overcomes this problem and provides personalised content to the user by mining a plethora of choices available on the web. In the group recommendations scenario, group members will have different tastes and achieving consensus is a nontrivial task. The common set of items taken from users' preferences is vital to provide recommendations. In practice, recommendations are generated by considering only common interest items, with the remaining items not playing a role in the recommendation. The items that are not of interest to a user based on her implicit or explicit feedback are called uninterested items. We hypothesise that these uninterested items may influence the overall recommendations, which can help us better interpret users' preferences. In this work, we also include the importance of uninterested items of the group members while generating the recommendation list. We have performed experiments over automatically identified groups and random groups to check the efficacy of the proposed models. We conducted experiments on publicly available real-world datasets. We found that incorporating uninterested item in group recommendations play a positive role in group recommendations while considering order and flexibility in user preferences. We observed that the overall group satisfaction scores are better in an automatically identified group. [ABSTRACT FROM AUTHOR]

Discovering the most interesting patterns is the key problem in the field of pattern mining. While ranking or selecting patterns is well-studied for itemsets it is surprisingly under-researched for other, more complex, pattern types. In this paper we propose a new quality measure for episodes. An episode is essentially a set of events with possible restrictions on the order of events. We say that an episode is significant if its occurrence is abnormally compact, that is, only few gap events occur between the actual episode events, when compared to the expected length according to the independence model. We can apply this measure as a post-pruning step by first discovering frequent episodes and then rank them according to this measure. In order to compute the score we will need to compute the mean and the variance according to the independence model. As a main technical contribution we introduce a technique that allows us to compute these values. Such a task is surprisingly complex and in order to solve it we develop intricate finite state machines that allow us to compute the needed statistics. We also show that asymptotically our score can be interpreted as a $$P$$ value. In our experiments we demonstrate that despite its intricacy our ranking is fast: we can rank tens of thousands episodes in seconds. Our experiments with text data demonstrate that our measure ranks interpretable episodes high. [ABSTRACT FROM AUTHOR]

A number of studies, theoretical, empirical, or both, have been conducted to provide insight into the properties and behavior of interestingness measures for association rule mining. While each has value in its own right, most are either limited in scope or, more importantly, ignore the purpose for which interestingness measures are intended, namely the ultimate ranking of discovered association rules. This paper, therefore, focuses on an analysis of the rule-ranking behavior of 61 well-known interestingness measures tested on the rules generated from 110 different datasets. By clustering based on ranking behavior, we highlight, and formally prove, previously unreported equivalences among interestingness measures. We also show that there appear to be distinct clusters of interestingness measures, but that there remain differences among clusters, confirming that domain knowledge is essential to the selection of an appropriate interestingness measure for a particular task and business objective. [ABSTRACT FROM AUTHOR]

New sources of operational data are leading to novel healthcare delivery system design and opportunities to support operational planning and decision-making. Technologies such as real time locating systems (RTLS) provide a unique view and understanding of how healthcare delivery settings behave and respond to operational design changes. In this paper RTLS data from an outpatient clinical setting is leveraged to identify the appropriate number of scheduled providers in order to improve the utilization of the clinical space while balancing the negative effects of clinic congestion. The approaches presented pair historical utilization rates for the clinical space with scheduled provider and patient volumes to support scheduling decisions in an operationally flexible clinic design. These historical data are augmented with clinic staff observation logs to identify target utilization rates as well as high congestion levels. Results are presented for two approaches: one where utilization of clinical space is a key performance metric and another where the decision-maker may be risk averse toward the use of provider time and use a probabilistic approach to determine provider staffing levels. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low accuracy of classification learning algorithm caused by serious imbalance of sample set in medical diagnostic application, this paper proposes a distribution-sensitive oversampling algorithm for imbalanced data. The algorithm accurately divides the minority samples into noise samples, unstable samples, boundary samples and stable samples according to the location of the minority samples. Different samples are processed differently to select the most suitable sample for the synthesis of new samples. In the case of sample synthesis, a distribution-sensitive sample synthesis method is adopted. Different sample synthesis methods are selected according to their different distance from the surrounding minority samples, so as to ensure that the newly synthesized samples have the same characteristics with the original minority samples. The real medical diagnostic data test shows that this algorithm improves the accuracy rate of classification learning algorithm compared with the existing sampling algorithms, especially for the accuracy rate and recall rate of minority classes. [ABSTRACT FROM AUTHOR]

eviation Completion is a novel technique to improve the efficiency of code-writing by supporting code completion of multiple keywords based on non-predefined abbreviated input-a different approach from conventional code completion that finds one keyword at a time based on an exact character match. Abbreviated input consisting of abbreviated keywords and non-alphanumeric characters between each abbreviated keyword (e.g. pb st nm) is expanded into a full expression (e.g. public String name) by a Hidden Markov Model learned from a corpus of existing code and abbreviation examples. The technique does not require the user to memorize abbreviations and provides incremental feedback of the most likely completions. In addition to code completion by disabbreviation of multiple keywords, abbreviation completion supports prediction of the next keywords and non-alphanumeric characters of a code completion candidate, a technique called code completion by extrapolation. The system finds the most likely next keywords and non-alphanumeric characters using an n-gram model of programming language. This enables a code completion scenario in which a user first types a short abbreviated expression to complete the beginning part of a desired full expression and then uses the extrapolation feature to complete the remaining part without further typing. This paper presents the algorithm for abbreviation completion, integrated with a new user interface for multiple-keyword code completion. We tested the system by sampling 4919 code lines from open source projects and found that more than 99% of the code lines could be resolved from acronym-like abbreviations. The system could also extrapolate code completion candidates to complete the next one or two keywords with the accuracy of 96% and 82%, respectively. A user study of code completion by disabbreviation found 30% reduction in time usage and 41% reduction of keystrokes over conventional code completion. [ABSTRACT FROM AUTHOR]

In this paper, we present a general framework to mine patterns with antimonotone constraints. This framework uses a technique that structures the pattern space in a way that facilitates the integration of constraints within the mining process. Furthermore, we also introduce a powerful strategy that uses background information on the data to speed-up the mining process. We illustrate our approach on a popular structured data mining problem, the frequent subgraph mining problem, and show, through experiments on synthetic and real-life data, that this general approach has advantages over state-of-the-art pattern mining algorithms. [ABSTRACT FROM AUTHOR]

The critical task of predicting clicks on search advertisements is typically addressed by learning from historical click data. When enough history is observed for a given query-ad pair, future clicks can be accurately modeled. However, based on the empirical distribution of queries, sufficient historical information is unavailable for many query-ad pairs. The sparsity of data for new and rare queries makes it difficult to accurately estimate clicks for a significant portion of typical search engine traffic. In this paper we provide analysis to motivate modeling approaches that can reduce the sparsity of the large space of user search queries. We then propose methods to improve click and relevance models for sponsored search by mining click behavior for partial user queries. We aggregate click history for individual query words, as well as for phrases extracted with a CRF model. The new models show significant improvement in clicks and revenue compared to state-of-the-art baselines trained on several months of query logs. Results are reported on live traffic of a commercial search engine, in addition to results from offline evaluation. [ABSTRACT FROM AUTHOR]

major challenge in subspace clustering is that subspace clustering may generate an explosive number of clusters with high computational complexity, which severely restricts the usage of subspace clustering. The problem gets even worse with the increase of the data's dimensionality. In this paper, we propose to summarize the set of subspace clusters into k representative clusters to alleviate the problem. Typically, subspace clusters can be clustered further into k groups, and the set of representative clusters can be selected from each group. In such a way, only the most representative subspace clusters will be returned to user. Unfortunately, when the size of the set of representative clusters is specified, the problem of finding the optimal set is NP-hard. To solve this problem efficiently, we present two approximate methods: PCoC and HCoC. The greatest advantage of our methods is that we only need a subset of subspace clusters as the input instead of the complete set of subspace clusters. Precisely, only the clusters in low-dimensional subspaces are computed and assembled into representative clusters in high-dimensional subspaces. The approximate results can be found in polynomial time. Our performance study shows both the effectiveness and efficiency of these methods. [ABSTRACT FROM AUTHOR]

im: The aim of this paper is to analyze the epidemiological pattern, the occupational background and the public health problems of acute pesticide poisoning in Ecuador. This is the first report of its kind. Subjects and methods: This was an observational and retrospective study performed with data from 2001 to 2007 from the National Register of Hospital Admissions/Discharges from the Instituto Nacional de Estadísticas y Censos, and also with data from the Ministry of Public Health. Ecuador does not have an official public medical poisoning registry or disease-specific registries. Results: The complete register reported 44,931 cases of poisoning with an average of 6,418 cases per year and a global progressive increase of 2,123 cases in the entire period. It reported 14,145 cases of pesticide poisoning. Of these, 10,100 cases were due to the effects of the insecticides organophospate and carbamate. The number of cases by gender was 7,102 (50.21%) males and 7,043 (49.79%) females. The major age group affected was adolescents and young adults. Conclusion: In Ecuador, pesticide poisoning occurs in individuals of both sexes who are between 15 to 25 years old and work in adverse conditions as agricultural farmers. The poisoning especially occurs in flower and banana plantation workers throughout the country. Seventy-one percent of the cases are due to organophosphate and carbamate poisonings, which cause death in 4% of the cases; 57% die in the first 48 h, possibly because of the acute action of AChE inhibitors. The long-term effects of pesticides are still unclear and need further research. [ABSTRACT FROM AUTHOR]

Overservice of alcohol, defined as commercial provision of alcohol to an individual who is obviously intoxicated, is illegal in most states and contributes to motor vehicle crashes and violence. Law enforcement agencies use various strategies that aim to reduce overservice at licensed alcohol establishments (e.g., bars, restaurants). Place of Last Drink (POLD) data collection is an emerging overservice enforcement strategy. POLD identifies patterns of overservice, which can provide support for targeted interventions to prevent overservice at offending establishments. We describe the prevalence of POLD and other overservice enforcement strategies and associations with agency characteristics, which has important implications for public health and safety. We conducted a national survey of 1024 municipal (e.g., town, city) and county law enforcement agencies in 2019 (response rate = 73%). We assessed the use of overservice enforcement strategies conducted by the agencies over the past year. We examined associations of each type of overservice enforcement strategy with agency and jurisdiction characteristics using regression models. 27% of responding agencies reported conducting overservice enforcement and 7% conducted POLD data collection specifically. Municipal (vs. county) agencies and agencies with an officer assigned primarily to alcohol enforcement activities were significantly more likely to conduct overservice enforcement generally but not POLD data collection specifically. Overservice enforcement in general, and POLD data collection specifically, are not widely conducted. Prevention of overservice has the potential to reduce harms related to excessive alcohol consumption. Increased evaluation of overservice enforcement strategies should be prioritized. [ABSTRACT FROM AUTHOR]

Textual content appearing in videos represents an interesting index for semantic retrieval of videos (from archives), generation of alerts (live streams), as well as high level applications like opinion mining and content summarization. The key components of such systems require detection and recognition of textual content which also make the subject of our study. This paper presents a comprehensive framework for detection and recognition of textual content in video frames. More specifically, we target cursive scripts taking Urdu text as a case study. Detection of textual regions in video frames is carried out by fine-tuning deep neural networks based object detectors for the specific case of text detection. Script of the detected textual content is identified using convoluational neural networks (CNNs), while for recognition, we propose a UrduNet, a combination of CNNs and long short- term memory (LSTM) networks. A benchmark dataset containing cursive text with more than 13,000 video frame is also developed. A comprehensive series of experiments is carried out reporting an F-measure of 88.3% for detection while a recognition rate of 87%. [ABSTRACT FROM AUTHOR]

For researchers to realize the benefits of real-world data in healthcare requires broader access to patient data than is currently possible given siloed data systems. To facilitate evidence generation, infrastructure must support integrated data collection and sharing enabled by patient consent. Critical to the success of data sharing is to design secured data sharing platforms around patient preferences and expectations. The objective of this review was to characterize patient and public preferences for secured data sharing platforms and incentives to share real-world data for health research. We conducted a scoping review of the data sharing and health informatics literature capturing patient and public values for data sharing platforms and incentivization. We searched Embase and Medline (OVID) databases for primary data studies. Two reviewers participated in study selection and data abstraction. Findings were summarized according to preference frequency within each major theme. The final search produced 253 articles. After screening, 12 articles were included for data extraction. Two studies discussed preferences for data sharing platforms, 7 discussed incentives preferences, and 3 addressed both. We identified considerable variation of patient and public preferences according to preferred consent mechanisms and level of control, willingness to trade off risks and benefits, and the type of incentivization appropriate to offer for participation. This preference variation informs the conditions under which individuals may be willing to engage with secured data sharing platforms to support research. Our findings indicate that platforms will need to be flexible to meet the diverse preferences of users and facilitate uptake. [ABSTRACT FROM AUTHOR]