nils-herrmann/clean_scirepeval huggingface dataset:
Influentials, Imitables or Susceptibles? Virality and Word of Mouth Conversations in Online Social Networks
Venue: J. Manag. Inf. Syst.

Motivated by the rise of social media platforms that achieve a fusion of content and community, we consider the role of word of mouth communications (WOM) structured through a network. Using a dataset from YouTube, we examine how cascades of WOM interactions enhance the popularity of videos. We first estimate the impact of channel influence and other network parameters in initiating WOM communications. The probit estimation considers the selection effect in videos that are likely to be associated with a greater propensity to trigger WOM. We find that factors related to a channel's ability to be a connector and a translator is most likely to result in the incidence of WOM. We then examine how cascades of WOM conversations have persistent impacts on subsequent video popularity. Empirically, the main issue here is heterogeneity in the epidemic potential of a video. Since the threshold might vary across videos, we use a finite mixture model. We also conduct a simultaneous estimation using latent instrumental variables to address endogeneity from unobservables. Our research provides implications for researchers and practitioners by highlighting how WOM travels through networks of influence and susceptibility in disseminating awareness, and holds out insights to the design of social recommendation systems and in identifying trending topics in social media.
Influentials, Imitables, or Susceptibles? Virality and Word of Mouth Conversations in Online Social Networks

Motivated by the rise of social media platforms that achieve a fusion of content and community, we consider the role of word of mouth communications (WOM) structured through a network. Using a data set from YouTube, we examine how cascades of WOM interactions enhance the popularity of videos. We first estimate the impact of channel influence and other network parameters in initiating WOM communications. The probit estimation considers the selection effect in videos that are likely to be associated with a greater propensity to trigger WOM. We find that factors related to a channel's ability to be a connector and a translator is most likely to result in the incidence of WOM. We then examine how cascades of WOM conversations have persistent impacts on subsequent video popularity. Empirically, the main issue here is heterogeneity in the epidemic potential of a video. Since the threshold might vary across videos, we use a finite mixture model. We also conduct a simultaneous estimation using latent instrumental variables to address endogeneity from unobservables. Our research has implications for researchers and practitioners by highlighting how WOM travels through networks of influence and susceptibility in disseminating awareness, and holds insights in regard to designing social recommendation systems and identifying trending topics in social media.
An Analysis of Altmetrics in Emergency Medicine.
Venue: Academic emergency medicine official journal of the Society for Academic Emergency Medicine

Alternative level metrics (Altmetrics) are a new method to assess the sharing and spread of scientific knowledge. The primary objective of this study was to describe the traditional metrics and Altmetric scores of the 50 most frequently cited articles published in emergency medicine (EM) journals. Since many articles related to EM are published in other journals, the secondary aim of this study was to describe the Altmetric scores of the most frequently cited articles relevant to EM in other biomedical journals.  A structured search of the Institute for Scientific Information Web of Science version of the Science Citation Index Expanded was conducted. The 200 most frequently cited articles in the top 10 EM journals (2011  were identified. The 200 most frequently cited articles from the rest of the medical literature, matching a predefined list of keywords relevant to the specialty of EM, were identified. Two authors reviewed the lists of citations for relevance to EM and a consensus approach was used to arrive at the final lists of the top 50 cited articles. The Altmetric scores for the top 50 cited articles in EM and other journals were determined. Descriptive statistics and Spearman correlation were performed.  The highest Altmetric score for EM articles was 25.0; the mean SD) was 1.9 5.0) The EM journal with the highest mean article Altmetric score was Resuscitation. The main clinical areas shared for articles from EM articles were trauma (mean SD 11.0 15.6, median 11.0) and cardiac arrest (mean SD 2.7 5.8, median 0) The highest Altmetric score for other journals was 176.0 (mean SD 23.3 40.8) The other journal with the highest mean article Altmetric score was the New England Journal of Medicine. The main clinical areas shared for articles were critical care (mean SD score 36.5 47.4, median 36.5) sepsis (mean SD 24.6 48.8, median 12.0) cardiology (mean SD 19.2 35.6, median 7.0) and infectious diseases (mean SD 17.0 12.7, median 17.0) Spearman correlation demonstrated weakly positive correlation between citation counts and Altmetric scores for EM articles and other journals.  This study is the first analysis of Altmetric scores for the top cited articles in EM. We demonstrated that there is a mild correlation between citation counts and Altmetric scores for the top papers in EM and other biomedical journals. We also demonstrated that there is a gap between the sharing of the top articles in EM journals and those related to EM in other biomedical journals. Future research to explore this relationship and its temporal trends will benefit the understanding of the reach and dissemination of EM research within the scientific community and society in general.
The Roberta IRONSIDE project: A dialog capable humanoid personal assistant in a wheelchair for dependent persons
Venue: 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

With an aging population and the financial difficulties of having a full time caregiver for every dependent person living at home, assistant robots appear to be a solution for advanced countries. However, most of what can be done with a robot can be done without it. So it is difficult to quantify what real value an assistant robot can add. Such a robot should be a real assistant capable of helping a person, whether indoors or outdoors. Additionally, the robot should be a companion for dialoging, as well as a system capable of detecting health problems. The Roberta Ironside project is a robotic evolution, embodying the expertise learned during the development of pure vocal personal assistants for dependent persons during the vAssist project (Sansen et al. 2014) The project proposes a relatively affordable and simplified design of a human sized humanoid robot that fits the requirements of this analysis. After an overall description of the robot, the justification of the novel choice of a handicapped robot in an electric wheel chair, this paper emphasizes the technology that is used for the head and the face and the subsequent verbal and non verbal communication capabilities of the robot, in turn highlighting the characteristics of Embodied Conversational Agents.
The Roberta IRONSIDE project a cognitive and physical robot coach for dependent persons

Many elderly and dependent people living at home suffer from a lack of social contact. With their strength and physical condition decreasing, they are also reluctant to walk outside. Even in sheltered accommodation or hospitals, they may have phases of loneliness when they are left alone or when the personnel cannot continuously take care of them. With an aging population and the financial difficulties of having a full time caregiver for every dependent person living at home, the proliferation of advanced assistant robots seems to be a viable future solution. However, as most of what can be done with a robot is also possible without it, it is sometimes difficult to quantify the real value this technology can add to the current situation. Hence we believe that such a robot should be a reliable assistant, capable of helping a person indoors as well as outdoors. Furthermore, it should be a companion for dialoging, as well as a system capable of detecting health problems. The Roberta Ironside project proposes to build such an advanced robot. That is, we want to build a cognitive coach that on the one hand fulfills the lack of social contact, and on the other hand may transform into a physical coach when going outside. In this paper we propose an affordable and simplified design for this human sized humanoid. It starts with an overall description of the robot, followed by a justification for the choice of putting it into an electric wheel chair. Next, it emphasizes the technology that is used for the head and the face, and then ends with a discussion of the verbal and non verbal communication capabilities of the robot, highlighting its distinct characteristics as an Embodied Conversational Agent.
Stable isotope (d13C, d15N, d34S) analysis of sediment cores suggests sockeye salmon (Oncorhynchus nerka) did not historically spawn in Lake Roberta, Washington (USA)
Venue: Journal of Paleolimnology

Historically, the Sanpoil River, Washington (USA) produced spawning runs of chinook salmon (Oncorhynchus tshawytscha) and steelhead trout (O. mykiss) Lake Roberta is connected to the Sanpoil River and local oral history suggests it may have supported anadromous sockeye salmon (O. nerka) until the completion of Grand Coulee Dam in the 1940s. Post spawning mortality of anadromous salmon provides large pulses of marine derived nutrients to aquatic and terrestrial ecosystems in the vicinity of spawning sites. Unique isotopic ratios of these marine derived nutrients are often transferred to freshwater algae and archived in lake sediments. However, marine derived isotope signatures may be overpowered by large inputs of other nutrient sources such as agricultural fertilizers, reactive nitrogen deposition, nitrogen fixation, or poor trophic transfer to freshwater algae. We compared nitrogen and sulfur isotope compositions for pre 1940 and post 1940 sediments to those collected from a control lake with no history of anadromy to investigate the possible historic presence of anadromous salmon in Lake Roberta. We also analyzed carbon isotopes, carbon:nitrogen ratios, and sediment accumulation rates to determine if changes in the lake sediments resulted from eutrophication rather than salmon exclusion. If sockeye did spawn in Lake Roberta historically, and if excessive nitrogen inputs did not overpower the marine derived signal, we would expect pre 1940 sediment organic matter isotope compositions indicative of the large pulses of marine nutrients from decomposing salmon carcasses. Isotope results and land use in the Lake Roberta watershed present no conclusive evidence to support anecdotal accounts of anadromy. There is some evidence to suggest that marine derived nutrients transferred to riparian communities within the lake's watershed may have moved downstream to the lake. However, most of the evidence suggests eutrophication and a switch to increased autochthonous productivity are the main causes of changes in the lake sediment isotope composition.
LifeLine Dialogues with Roberta
Venue: FETLT

This paper describes work on dialogue data collection and dialogue system design for personal assistant humanoid robots undertaken at eNTERFACE 2016. The emphasis has been on the system's speech capabilities and dialogue modeling of what we call LifeLine Dialogues, i.e. dialogues that help people tell stories about their lives. The main goal behind this type of application is to help elderly people exercise their speech and memory capabilities. The system further aims at acquiring a good level of knowledge about the person's interests and thus is expected to feature open domain conversations, presenting useful and interesting information to the user. The novel contributions of this work are: (1) a flexible spoken dialogue system that extends the Ravenclaw type agent based dialogue management model with topic management and multi modal capabilities, especially with face recognition technologies, (2) a collection of WOZ data related to initial encounters and presentation of information to the user, and (3) the establishment of a closer conversational relationship with the user by utilizing additional data (e.g. context, dialogue history, emotions, user goals, etc.
Analysis and design of the google congestion control for web real time communication (WebRTC)
Venue: MMSys

Video conferencing applications require low latency and high bandwidth. Standard TCP is not suitable for video conferencing since its reliability and in order delivery mechanisms induce large latency. Recently the idea of using the delay gradient to infer congestion is appearing again and is gaining momentum. In this paper we present an algorithm that is based on estimating through a Kalman filter the end to end one way delay variation which is experienced by packets traveling from a sender to a destination. This estimate is compared to an adaptive threshold to dynamically throttle the sending rate. The control algorithm has been implemented over the RTP/RTCP protocol and is currently used in Google Hangouts and in the Chrome WebRTC stack. Experiments have been carried out to evaluate the algorithm performance in the case of variable link capacity, presence of heterogeneous or homogeneous concurrent traffic, and backward path traffic.
Intensive insulin therapy implementation by means of planned versus emergent change approach.
Venue: Nursing in critical care

Nurses' participation in decisions about new care procedures and protocols is potentially of benefit for patient outcomes. Whether nurses' participation in decisions is allowed in the implementation of innovations depends on the implementation approach used for the introduction. A planned change implementation approach does not allow it, an emergent change implementation approach does.  To compare a planned change and an emergent change implementation approach to introduce an intensive insulin therapy to an intensive care unit (ICU) DESIGN A prospective comparative study in an ICU in the Netherlands of two teams of nurses using either implementation approach.  Pre introduction of the comparability of the two teams was assessed. The nurse compliance to the protocol was assessed as being nurses' behaviour according to the protocol and leading to acceptable glucose values. The effectiveness of the implementation was assessed by measuring the percentage of patients' glucose values within the target range, the occurrence of hypoglycaemic events and the time to glucose value normalization. Data were collected from December 2007 till January 2009.  In the emergent change approach team there was better nurse compliance measurements than in the planned change approach team (83.5% vs 66,8% conform protocol) a better percentage of glucose values in the target range (53,5% vs 52.8% and a shorter time to glucose value normalization.  The implementation approach allowing nurse participation was associated with better nurse compliance and patient outcome measurements. The implementation approach did not conflict with introducing an evidence based innovation. It was also associated with more effective adaptation of the protocol to changing circumstances. RELEVANCE FOR CLINICAL PRACTICE When a new treatment requires adaptability to changing circumstances to be most effective, nurses' participation in decisions about the implementation of the treatment should be considered.
The Importance of Context: Risk based De identification of Biomedical Data.
Venue: Methods of information in medicine

Data sharing is a central aspect of modern biomedical research. It is accompanied by significant privacy concerns and often data needs to be protected from re identification. With methods of de identification datasets can be transformed in such a way that it becomes extremely difficult to link their records to identified individuals. The most important challenge in this process is to find an adequate balance between an increase in privacy and a decrease in data quality.  Accurately measuring the risk of re identification in a specific data sharing scenario is an important aspect of data de identification. Overestimation of risks will significantly deteriorate data quality, while underestimation will leave data prone to attacks on privacy. Several models have been proposed for measuring risks, but there is a lack of generic methods for risk based data de identification. The aim of the work described in this article was to bridge this gap and to show how the quality of de identified datasets can be improved by using risk models to tailor the process of de identification to a concrete context.  We implemented a generic de identification process and several models for measuring re identification risks into the ARX de identification tool for biomedical data. By integrating the methods into an existing framework, we were able to automatically transform datasets in such a way that information loss is minimized while it is ensured that re identification risks meet a user defined threshold. We performed an extensive experimental evaluation to analyze the impact of using different risk models and assumptions about the goals and the background knowledge of an attacker on the quality of de identified data.  The results of our experiments show that data quality can be improved significantly by using risk models for data de identification. On a scale where 100 represents the original input dataset and 0 represents a dataset from which all information has been removed, the loss of information content could be reduced by up to 10 when protecting datasets against strong adversaries and by up to 24 when protecting datasets against weaker adversaries.  The methods studied in this article are well suited for protecting sensitive biomedical data and our implementation is available as open source software. Our results can be used by data custodians to increase the information content of de identified data by tailoring the process to a specific data sharing scenario. Improving data quality is important for fostering the adoption of de identification methods in biomedical research.
Efficient and effective pruning strategies for health data de identification
Venue: BMC Medical Informatics and Decision Making

BackgroundPrivacy must be protected when sensitive biomedical data is shared, e.g. for research purposes. Data de identification is an important safeguard, where datasets are transformed to meet two conflicting objectives: minimizing re identification risks while maximizing data quality. Typically, de identification methods search a solution space of possible data transformations to find a good solution to a given de identification problem. In this process, parts of the search space must be excluded to maintain scalability. set of transformations which are solution candidates is typically narrowed down by storing the results obtained during the search process and then using them to predict properties of the output of other transformations in terms of privacy (first objective) and data quality (second objective) However, due to the exponential growth of the size of the search space, previous implementations of this method are not well suited when datasets contain many attributes which need to be protected. As this is often the case with biomedical research data, e.g. as a result of longitudinal collection, we have developed a novel method.MethodsOur approach combines the mathematical concept of antichains with a data structure inspired by prefix trees to represent properties of a large number of data transformations while requiring only a minimal amount of information to be stored. To analyze the improvements which can be achieved by adopting our method, we have integrated it into an existing algorithm and we have also implemented a simple best first branch and bound search (BFS) algorithm as a first step towards methods which fully exploit our approach. We have evaluated these implementations with several real world datasets and the k anonymity privacy model.ResultsWhen integrated into existing de identification algorithms for low dimensional data, our approach reduced memory requirements by up to one order of magnitude and execution times by up to 25 This allowed us to increase the size of solution spaces which could be processed by almost a factor of 10. When using the simple BFS method, we were able to further increase the size of the solution space by a factor of three. When used as a heuristic strategy for high dimensional data, the BFS approach outperformed a state of the art algorithm by up to 12 in terms of the quality of output data.ConclusionsThis work shows that implementing methods of data de identification for real world applications is a challenging task. Our approach solves a problem often faced by data custodians: a lack of scalability of de identification software when used with datasets having realistic schemas and volumes. The method described in this article has been implemented into ARX, an open source de identification software for biomedical data.
Review of Diagnosis Related Group Based Financing of Hospital Care
Venue: Health services research and managerial epidemiology

Since the 1990s, diagnosis related group (DRG) based payment systems were gradually introduced in many countries. The main design characteristics of a DRG based payment system are an exhaustive patient case classification system (ie, the system of diagnosis related groupings) and the payment formula, which is based on the base rate multiplied by a relative cost weight specific for each DRG. Cases within the same DRG code group are expected to undergo similar clinical evolution. Consecutively, they should incur the costs of diagnostics and treatment within a predefined scale. Such predictability was proven in a number of cost of illness studies conducted on major prosperity diseases alongside clinical trials on efficiency. This was the case with risky pregnancies, chronic obstructive pulmonary disease, diabetes, depression, alcohol addiction, hepatitis, and cancer. This article presents experience of introduced DRG based payments in countries of western and eastern Europe, Scandinavia, United States, Canada, and Australia. This article presents the results of few selected reviews and systematic reviews of the following evidence: published reports on health system reforms by World Health Organization, World Bank, Organization for Economic Co operation and Development, Canadian Institute for Health Information, Canadian Health Services Research Foundation, and Centre for Health Economics University of York. Diverse payment systems have different strengths and weaknesses in relation to the various objectives. The advantages of the DRG payment system are reflected in the increased efficiency and transparency and reduced average length of stay. The disadvantage of DRG is creating financial incentives toward earlier hospital discharges. Occasionally, such polices are not in full accordance with the clinical benefit priorities.