Two-Sample Tests that are Safe under Optional Stopping, with an Application to Contingency Tables

We develop E variables for testing whether two data streams come from the same source or not, and more generally, whether the difference between the sources is larger than some minimal effect size. These E variables lead to tests that remain safe, i.e. keep their Type-I error guarantees, under flexible sampling scenarios such as optional stopping and continuation. In special cases our E variables also have an optimal `growth' property under the alternative. We illustrate the generic construction through the special case of 2x2 contingency tables, where we also allow for the incorporation of different restrictions on a composite alternative. Comparison to p-value analysis in simulations and a real-world example show that E variables, through their flexibility, often allow for early stopping of data collection, thereby retaining similar power as classical methods

Integration and verification of semantic constraints in adaptive process management systems

Adaptivity in process management systems is key to their successful applicability in practice. Approaches have been already developed to ensure system correctness after arbitrary process changes at the syntactical level (e.g., avoiding inconsistencies such as deadlocks or missing input parameters after a process change). However, errors may be still caused at the semantical level (e.g., violation of business rules). Therefore, the integration and verification of domain knowledge will flag a milestone in the development of adaptive process management technology. In this paper, we introduce a framework for defining semantic constraints over processes in such a way that they can express real-world domain knowledge on the one hand and are still manageable concerning the effort for maintenance and semantic process verification on the other hand. This can be used to detect semantic conflicts (e.g., drug incompatibilities) when modeling process templates, applying ad hoc changes at process instance level, and propagating process template modifications to already running process instances, even if they have been already individually modified themselves; i.e., we present techniques to ensure semantic correctness for single and concurrent changes which are, in addition, minimal regarding the set of semantic constraints to be checked. Together with further optimizations of the semantic checks based on certain process meta model properties this allows for efficiently verifying processes. Altogether, the framework presented in this paper provides the basis for process management systems which are adaptive and semantic-aware at the same time

Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworks

Deep Q-Networks algorithm (DQN) was the first reinforcement learning algorithm using deep neural network to successfully surpass human level performance in a number of Atari learning environments. However, divergent and unstable behaviour have been long standing issues in DQNs. The unstable behaviour is often characterised by overestimation in the $Q$-values, commonly referred to as the overestimation bias. To address the overestimation bias and the divergent behaviour, a number of heuristic extensions have been proposed. Notably, multi-step updates have been shown to drastically reduce unstable behaviour while improving agent's training performance. However, agents are often highly sensitive to the selection of the multi-step update horizon ($n$), and our empirical experiments show that a poorly chosen static value for $n$ can in many cases lead to worse performance than single-step DQN. Inspired by the success of $n$-step DQN and the effects that multi-step updates have on overestimation bias, this paper proposes a new algorithm that we call `Elastic Step DQN' (ES-DQN). It dynamically varies the step size horizon in multi-step updates based on the similarity of states visited. Our empirical evaluation shows that ES-DQN out-performs $n$-step with fixed $n$ updates, Double DQN and Average DQN in several OpenAI Gym environments while at the same time alleviating the overestimation bias

On Enabling Integrated Process Compliance with Semantic Constraints in Process Management Systems

Key to broad use of process management systems (PrMS) in practice is their ability to foster and ease the implementation, execution, monitoring, and adaptation of business processes while still being able to ensure robust and error-free process enactment. To meet these demands a variety of mechanisms has been developed to prevent errors at the structural level (e.g., deadlocks). In many application domains, however, processes often have to comply with business level rules and policies (i.e., semantic constraints) as well. Hence, to ensure error-free executions at the semantic level, PrMS need certain control mechanisms for validating and ensuring the compliance with semantic constraints. In this paper, we discuss fundamental requirements for a comprehensive support of semantic constraints in PrMS. Moreover, we provide a survey on existing approaches and discuss to what extent they are able to meet the requirements and which challenges still have to be tackled. In order to tackle the particular challenge of providing integrated compliance support over the process lifecycle, we introduce the SeaFlows framework. The framework introduces a behavioural level view on processes which serves a conceptual process representation for constraint specification approaches. Further, it provides general compliance criteria for static compliance validation but also for dealing with process changes. Altogether, the SeaFlows framework can serve as formal basis for realizing integrated support of semantic constraints in PrMS

Monitoring Business Process Compliance Using Compliance Rule Graphs

Driven by recent trends, effective compliance control has become a crucial success factor for companies nowadays. In this context, compliance monitoring is considered an important building block to support business process compliance. Key to the practical application of a monitoring framework will be its ability to reveal and pinpoint violations of imposed compliance rules that occur during process execution. In this context, we propose a compliance monitoring framework that tackles three major challenges. As a compliance rule can become activated multiple times within a process execution, monitoring only its overall enforcement can be insufficient to assess and deal with compliance violations. Therefore, our approach enables to monitor each activation of a compliance rule individually. In case of violations, we are able to derive the particular root cause, which is helpful to apply specific remedy strategies. Even if a rule activation is not yet violated, the framework can provide assistance in proactively enforcing compliance by deriving measures to render the rule activation satisfied

Compliance of Semantic Constraints - A Requirements Analysis for Process Management Systems

Key to the use of process management systems (PrMS) in practice is their ability to facilitate the implementation, execution, and adaptation of business processes while still being able to ensure error-free process executions. Mechanisms have been developed to prevent errors at the syntactic level such as deadlocks. In many application domains, processes often have to comply with business level rules and policies (i.e., semantic constraints). Hence, in order to ensure error-free executions at the semantic level, PrMS need certain control mechanisms for validating and ensuring the compliance with semantic constraints throughout the process lifecycle. In this paper, we discuss fundamental requirements for a comprehensive support of semantic constraints in PrMS. Moreover, we provide a survey on existing approaches and discuss to what extent they meet the requirements and which challenges still have to be tackled. Finally, we show how the challenge of life time compliance can be dealt with by integrating design time and runtime process validation

On Enabling Data-Aware Compliance Checking of Business Process Models

In the light of an increasing demand on business process compliance, the verication of process models against compliance rules has become essential in enterprise computing. To be broadly applicable compliance checking has to support data-aware compliance rules as well as to consider data conditions within a process model. Independently of the actual technique applied to accomplish compliance checking, dataawareness means that in addition to the control ow dimension, the data dimension has to be explored during compliance checking. However, naive exploration of the data dimension can lead to state explosion. We address this issue by introducing an abstraction approach in this paper. We show how state explosion can be avoided by conducting compliance checking for an abstract process model and abstract compliance rules. Our abstraction approach can serve as preprocessing step to the actual compliance checking and provides the basis for more ecient application of existing compliance checking algorithms

Population need for primary eye care in Rwanda: A national survey.

BACKGROUND: Universal access to Primary Eye Care (PEC) is a key global initiative to reduce and prevent avoidable causes of visual impairment (VI). PEC can address minor eye conditions, simple forms of uncorrected refractive error (URE) and create a referral pathway for specialist eye care, thus offering a potential solution to a lack of eye health specialists in low-income countries. However, there is little information on the population need for PEC, including prevalence of URE in all ages in Sub-Saharan Africa. METHODS: A national survey was conducted of people aged 7 and over in Rwanda in September-December 2016. Participants were selected through two-stage probability proportional to size sampling and compact segment sampling. VI (visual acuity<6/12) was assessed using Portable Eye Examination Kit (PEEK); URE was detected using a pinhole and presbyopia using local near vision test. We also used validated questionnaires to collect socio-demographic and minor eye symptoms information. Prevalence estimates for VI, URE and need for PEC (URE, presbyopia with good distance vision, need for referrals and minor eye conditions) were age and sex standardized to the Rwandan population. Associations between age, sex, socio-economic status and the key outcomes were examined using logistic regression. RESULTS: 4618 participants were examined and interviewed out of 5361 enumerated (86% response rate). The adjusted population prevalence of VI was 3.7% (95%CI = 3.0-4.5%), URE was 2.2% (95%CI = 1.7-2.8%) and overall need for PEC was 34.0% (95%CI = 31.8-36.4%). Women and older people were more likely to need PEC and require a referral. CONCLUSIONS: Nearly a third of the population in Rwanda has the potential to benefit from PEC, with greater need identified in older people and women. Universal access to PEC can address unmet eye health needs and public health planning needs to ensure equitable access to older people and women