Handling Concept Drift for Predictions in Business Process Mining

Predictive services nowadays play an important role across all business sectors. However, deployed machine learning models are challenged by changing data streams over time which is described as concept drift. Prediction quality of models can be largely influenced by this phenomenon. Therefore, concept drift is usually handled by retraining of the model. However, current research lacks a recommendation which data should be selected for the retraining of the machine learning model. Therefore, we systematically analyze different data selection strategies in this work. Subsequently, we instantiate our findings on a use case in process mining which is strongly affected by concept drift. We can show that we can improve accuracy from 0.5400 to 0.7010 with concept drift handling. Furthermore, we depict the effects of the different data selection strategies

Nurses as role models in health promotion: a concept analysis

There are national and international expectations that nurses are healthy role models but there is a lack of clarity about what this concept means. This study used concept analysis methodology to provide theoretical clarity for the concept of role models in health promoting behaviour for registered nurses and students. The framework included analysis of literature and qualitative data from six focus groups and one interview. Participants (n=39) included pre-registration students (Adult field), nurse lecturers and registered nurses (RNs), working in NHS Trusts across London and South East London. From the findings, being a role model in health promoting behaviour involves being an exemplar, portraying a healthy image (being fit and healthy), and championing health and wellness. Personal attributes of a role model in health promoting behaviour include being: caring, non-judgemental, trustworthy, inspiring and motivating, self-caring, knowledgeable and self-confident, innovative, professional and having a deep sense of self

A cookbook for temporal conceptual data modelling with description logic

We design temporal description logics suitable for reasoning about temporal conceptual data models and investigate their computational complexity. Our formalisms are based on DL-Lite logics with three types of concept inclusions (ranging from atomic concept inclusions and disjointness to the full Booleans), as well as cardinality constraints and role inclusions. In the temporal dimension, they capture future and past temporal operators on concepts, flexible and rigid roles, the operators `always' and `some time' on roles, data assertions for particular moments of time and global concept inclusions. The logics are interpreted over the Cartesian products of object domains and the flow of time (Z,<), satisfying the constant domain assumption. We prove that the most expressive of our temporal description logics (which can capture lifespan cardinalities and either qualitative or quantitative evolution constraints) turn out to be undecidable. However, by omitting some of the temporal operators on concepts/roles or by restricting the form of concept inclusions we obtain logics whose complexity ranges between PSpace and NLogSpace. These positive results were obtained by reduction to various clausal fragments of propositional temporal logic, which opens a way to employ propositional or first-order temporal provers for reasoning about temporal data models

Gendered Differences in Adolescent Body Image: Youth Agency, Protective andRisk Factors

This research examined youth agency and the micro-meso system environments (protective and risks) as they shaped adolescents’ body image. National data from 11,531 students (Grades 5-10) in the Health Behavior in School Aged Children survey (2009-2010) and commentaries from six education/health professionals were used. As predicted by the Iowa and Chicago Schools of Self Concept, parental figure protected youth against negative body image by shielding them against school bullying. But, the protection and risks associated with youth agency and the micro-meso systems were gendered and operated differently for male and female youth. Female negative body image models were more complex in the salience of protective and risk factors than male models. These findings added to the literature on adolescent health and endorsed the need for wrap-around role modeling and protection for adolescents

Investigation of the role of endosomal Toll-like receptors in murine collagen-induced arthritis reveals a potential role for TLR7 in disease maintenance

INTRODUCTION Endosomal toll-like receptors (TLRs) have recently emerged as potential contributors to the inflammation observed in human and rodent models of rheumatoid arthritis (RA). This study aims to evaluate the role of endosomal TLRs and in particular TLR7 in the murine collagen induced arthritis (CIA) model. METHODS CIA was induced by injection of collagen in complete Freund's adjuvant. To investigate the effect of endosomal TLRs in the CIA model, mianserin was administered daily from the day of disease onset. The specific role of TLR7 was examined by inducing CIA in TLR7-deficient mice. Disease progression was assessed by measuring clinical score, paw swelling, serum anti-collagen antibodies histological parameters, cytokine production and the percentage of T regulatory (Treg) cells. RESULTS Therapeutic administration of mianserin to arthritic animals demonstrated a highly protective effect on paw swelling and joint destruction. TLR7-/- mice developed a mild arthritis, where the clinical score and paw swelling were significantly compromised in comparison to the control group. The amelioration of arthritis by mianserin and TLR7 deficiency both corresponded with a reduction in IL-17 responses, histological and clinical scores, and paw swelling. CONCLUSIONS These data highlight the potential role for endosomal TLRs in the maintenance of inflammation in RA and support the concept of a role for TLR7 in experimental arthritis models. This study also illustrates the potential benefit that may be afforded by therapeutically inhibiting the endosomal TLRs in RA

Handling Concept Drift for Predictions in Business Process Mining

Predictive services nowadays play an important role across all business sectors. However, deployed machine learning models are challenged by changing data streams over time which is described as concept drift. Prediction quality of models can be largely influenced by this phenomenon. Therefore, concept drift is usually handled by retraining of the model. However, current research lacks a recommendation which data should be selected for the retraining of the machine learning model. Therefore, we systematically analyze different data selection strategies in this work. Subsequently, we instantiate our findings on a use case in process mining which is strongly affected by concept drift. We can show that we can improve accuracy from 0.5400 to 0.7010 with concept drift handling. Furthermore, we depict the effects of the different data selection strategies

Evolutionary Fitness in Variable Environments

One essential ingredient of evolutionary theory is the concept of fitness as a measure for a species' success in its living conditions. Here, we quantify the effect of environmental fluctuations onto fitness by analytical calculations on a general evolutionary model and by studying corresponding individual-based microscopic models. We demonstrate that not only larger growth rates and viabilities, but also reduced sensitivity to environmental variability substantially increases the fitness. Even for neutral evolution, variability in the growth rates plays the crucial role of strongly reducing the expected fixation times. Thereby, environmental fluctuations constitute a mechanism to account for the effective population sizes inferred from genetic data that often are much smaller than the census population size.Comment: main: 5 pages, 4 figures; supplement: 7 pages, 7 figue

Estimating potential output using business survey data in a svar framework

Potential output and the related concept of output gap play a central role in the macroeconomic policy interventions and evaluations. In particular, the output gap, defined as the difference between actual and potential output, conveys useful information on the cyclical position of a given economy. This paper proposes estimates of the Italian potential output based on a structural VAR model using data coming from business surveys. This kind of data, given their cyclical profile, are particularly useful for detrending purposes, as they allow to include information concerning the business cycle activity. The ability of the cyclical GDP component obtained with the SVAR decomposition to forecast inflation and to detect business cycle turning points over the expansion and recession phases is then performed.output gap, business survey data, structural VAR models, forecast ability

Soft Seeded SSL Graphs for Unsupervised Semantic Similarity-based Retrieval

Semantic similarity based retrieval is playing an increasingly important role in many IR systems such as modern web search, question-answering, similar document retrieval etc. Improvements in retrieval of semantically similar content are very significant to applications like Quora, Stack Overflow, Siri etc. We propose a novel unsupervised model for semantic similarity based content retrieval, where we construct semantic flow graphs for each query, and introduce the concept of "soft seeding" in graph based semi-supervised learning (SSL) to convert this into an unsupervised model. We demonstrate the effectiveness of our model on an equivalent question retrieval problem on the Stack Exchange QA dataset, where our unsupervised approach significantly outperforms the state-of-the-art unsupervised models, and produces comparable results to the best supervised models. Our research provides a method to tackle semantic similarity based retrieval without any training data, and allows seamless extension to different domain QA communities, as well as to other semantic equivalence tasks.Comment: Published in Proceedings of the 2017 ACM Conference on Information and Knowledge Management (CIKM '17