Involvement Of Domain Experts Research Articles

App development in a sports science setting: A systematic review and lessons learned from an exemplary setting to generate recommendations for the app development process.

The digital health sector is rapidly growing. With only 4% of publishers out of academic settings, it is under-represented in app development. The objective of this study is to assess the current state of app development with a systematic review and a survey within an exemplary academic setting along the following research questions: (Q1) Are software engineering principles sufficiently known in the sports science app development context? (Q2) Is the role of sports scientists in the context of app development sufficiently understood? The systematic review was conducted by two independent reviewers within databases Pubmed, Scopus, Web of Science, and IEEE Xplore. The PICO schema was used to identify the search term. We subtracted information about five main topics: development process, functional requirements and features, security, technology, and dissemination. The survey was developed by a multidisciplinary team and focused on five main topics. Out of 701 matches, 21 were included in the review. The development process was only described in seven studies. Functional requirements and features were considered in 11 studies, security in 3, technology in 13, and dissemination in 12 with varying details. Twelve respondents [mean age 33(7) years, 58% women] replied to the survey. The survey revealed limited knowledge in realization of security measures, underlying technology and source code management, and dissemination. Respondents were able to provide input on development processes as well as functional requirements and features. The involvement of domain experts is given in seven review studies and described in two more. In 50% of survey respondents, the role in app development is defined as a research assistant. We conclude that there is a varying degree of software engineering knowledge in the sports science app development context (Q1). Furthermore, we found that the role of sports scientists within app development is not sufficiently defined (Q2). We present recommendations for improving the success probability and sustainability of app development and give orientation on the potential roles of sports scientists as domain experts. Future research should focus on the generalizability of these findings and the reporting of the app development process.

Artificial Intelligence for Human Life: A Critical Opinion from Medical Bioethics Perspective – Part II

Healthcare and medical fields are among the areas of AI applications that require serious attention when it comes to ethical and trustworthy AI. In the medical field, artificial intelligence is applied in laboratory research, regulation-policy making, and clinical intervention. Several factors need to be seriously accounted for in the AI-empowered healthcare system, including algorithm transparency, bias mitigation, domain-expert involvement, privacy and data protection, and informed consent. In this article, we summarized the applications of AI in the field of medical and healthcare sectors. We also presented the possible ethical issues in AI applications in mentioned sectors. These issues include transparency and safety, informed consent and right to information, algorithmic fairness and biases, and data privacy and sharing regulation. We then present the basic principles of AI ethics and the existing AI guidelines, especially for the fields of medicine and healthcare. Based on the existing issues and guidelines, we believe that there are still many factors that need to be perfected, such as how we can generalize these guidelines worldwide and how these guidelines can be legally-binding. Detailed clauses in the guideline and law enforcement are another issue that should be addressed such that no loophole can be addressed. Ethical consideration is critical in AI applications, especially in the medical and healthcare fields. Until these issues can be totally addressed, we believe that AI applications in healthcare should be very strictly monitored.

Evaluation of a Clinical Decision Support System in the Domain of Chronic Wound Management.

The PosiThera project focuses on the management of chronic wounds, which is multi-professional and multi-disciplinary. For this context, a software prototype was developed in the project, which is intended to support medical and nursing staff with the assistance of artificial intelligence. In accordance with the user-centred design, national workshops were held at the beginning of the project with the involvement of domain experts in wound care in order to identify requirements and use cases of IT systems in wound care, with a focus on AI. In this study, the focus was on involving nursing and nursing science staff in testing the software prototype to gain insights into its functionality and usability. The overarching goal of the iterative testing and adaptation process is to further develop the prototype in a way that is close to care.

Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis.

BackgroundSocial media is a rich source where we can learn about people’s reactions to social issues. As COVID-19 has impacted people’s lives, it is essential to capture how people react to public health interventions and understand their concerns.ObjectiveWe aim to investigate people’s reactions and concerns about COVID-19 in North America, especially in Canada.MethodsWe analyzed COVID-19–related tweets using topic modeling and aspect-based sentiment analysis (ABSA), and interpreted the results with public health experts. To generate insights on the effectiveness of specific public health interventions for COVID-19, we compared timelines of topics discussed with the timing of implementation of interventions, synergistically including information on people’s sentiment about COVID-19–related aspects in our analysis. In addition, to further investigate anti-Asian racism, we compared timelines of sentiments for Asians and Canadians.ResultsTopic modeling identified 20 topics, and public health experts provided interpretations of the topics based on top-ranked words and representative tweets for each topic. The interpretation and timeline analysis showed that the discovered topics and their trend are highly related to public health promotions and interventions such as physical distancing, border restrictions, handwashing, staying home, and face coverings. After training the data using ABSA with human-in-the-loop, we obtained 545 aspect terms (eg, “vaccines,” “economy,” and “masks”) and 60 opinion terms such as “infectious” (negative) and “professional” (positive), which were used for inference of sentiments of 20 key aspects selected by public health experts. The results showed negative sentiments related to the overall outbreak, misinformation and Asians, and positive sentiments related to physical distancing.ConclusionsAnalyses using natural language processing techniques with domain expert involvement can produce useful information for public health. This study is the first to analyze COVID-19–related tweets in Canada in comparison with tweets in the United States by using topic modeling and human-in-the-loop domain-specific ABSA. This kind of information could help public health agencies to understand public concerns as well as what public health messages are resonating in our populations who use Twitter, which can be helpful for public health agencies when designing a policy for new interventions.

Achieving parametric transparency in model-based factory planning

The planning of new factories, as well as the re-planning of existing factories, has become more frequent due to increasingly changing business requirements, as for example shorter product life cycles and Industry 4.0. A higher number of involved planners and the resulting high amount of planning information strongly require coordination. In this context, the importance of Building Information Modeling (BIM) in factory planning rises as it provides a method of integrated building planning and planning validation by means of 3D software and object-oriented modelling. However, despite the use of BIM, there are still major interface problems in factory planning that cannot be solved by the still manual plausibility checks of non-geometrical planning information. To enable automatic checking of planning results, thereby improving the BIM-based factory planning process, machine-readable explication of the parametric dependencies are required between different planning fields such as production planning and building planning. The goal of this paper is to show parametric and thus non-geometric dependencies that exist between the sub-models of BIM-based factory planning in such a way that software agents can automatically evaluate this design information. Within the planning interface between production planning and building planning, the paper focusses on the particular exchange between the planning of the manufacturing system and the planning of a cutting fluid pump. With the involvement of domain experts from factory planning, systems engineering and production engineering, we as the authors have managed to develop a coherent system of block diagrams, constraint diagrams and parametric diagrams that explicate the focused interface in a machine-readable manner. We believe our accomplishments are an essential element for completely automated planning validation in BIM-based factory planning and general object-oriented modelling in the future.

Knowledge Graph for the Visualisation of CRM Objects in a Social Network of Business Objects (SoNBO): Development of the SoNBO Visualiser

Abstract This paper investigates possibilities to visually support the collaborative design process of Enterprise Knowledge Graphs for Business Application Systems. Starting with a description of the concept of the Social Network of Business Objects (SoNBO), we show how the SoNBO Visualiser supports the visualisation of a company-specific ontology and the resulting knowledge graph. Our previous research showed that existing visualisation tools from the Semantic Web are unsuited for the human involvement that is required in the design of an ontology for a SoNBO. In order to address the lack of a tool, the SoNBO Visualiser was developed using a Design Science Research approach. This paper aims to provide a methodological as well as a technical contribution: The method provides support for the visualisation of the ontology (on two levels) and facilitates the involvement of domain experts (employees) without technical knowledge in the design process. The complementary technical solution connects heterogeneous source systems in a simple configuration process and allows to visualise the ontology and the resulting knowledge graph.

Development and Validation of a Controlled Vocabulary: An OWL Representation of Organizational Structures of Trauma Centers and Trauma Systems.

In trauma care and trauma care research there exists an implementation gap regarding a consistent controlled vocabulary to describe organizational aspects of trauma centers and trauma systems. This paper describes the development and evaluation of a controlled vocabulary for trauma care organizations. We give a detailed description of the involvement of domain experts in the domain analysis workflow and the authoring of definitions and additional term descriptions. Finally, the paper details the evaluation methodology to assess the initial version of the controlled vocabulary. The results of the evaluation show that our development process yields terms most of which find approval from domain experts not involved in the development. In addition, our evaluation tools resulted in valuable domain expert input to optimize the controlled vocabulary.

Detecting Anomalies in Computer Networks Recurrent Neural Networks

Due to the widespread use of computer networks as well as the numerous attacks that such networks face, a fast and accurate means of detecting attacks represents an ever growing need. In this paper, a system that uses a recurrent neural network (RNN) is explored as a potential method for detecting intrusions. The investigated system is applied to an unlabeled cyber-security data set in order to determine its effectiveness. The goal is to train the system on every individual user included in the data set so as to learn their behavior and then to allow for the identification of any deviations in their behavior. It should be stressed here that deviations in behavior (or anomalies) cannot be labeled as “intrusions” without the involvement of domain experts. Nevertheless, they can be used to identify potential attacks, and they can also be presented to cyber-security experts for further evaluation. Several architectures for the system are explored with the aim of identifying the optimal one. A system is developed to be both fast and good at identifying anomalous behavior, while at the same time it must be able to adapt to any changes the attackers might implement to avoid detection, thus the results shown in this work using an unlabeled data set to train the network does not allow for the accuracy of the system for identifying the attackers. This means that identifying the optimal architecture is a difficult task. Yet, the system used in this paper is able to identify likely anomalies, which leads to the conclusion that RNNs do represent an effective means of flagging anomalous behavior.

Collaborative Modeling and Group Decision Making Using Chatbots in Social Networks

Modeling is used in the early phases of software and system development to discuss and explore problems, understand domains, and evaluate alternatives and comprehend their implications. In this setting, modeling is inherently collaborative because it involves stakeholders with different backgrounds and expertise, who cooperate to build solutions based on consensus. However, modeling tools typically provide unwieldy diagrammatic editors that might hamper the active involvement of domain experts and lack mechanisms to ease decision making. To tackle these issues, the proposed approach embeds modeling in social networks, so that the modeling interface is natural language that a chatbot interprets to derive an appropriate domain model. Social networks have intuitive built-in discussion mechanisms, while the use of natural language lowers the entry barrier to modeling for domain experts. Moreover, this approach facilitates choosing among modeling alternatives, using soft-consensus decision making. This approach is supported by the SOCIO tool, which works on social networks such as Telegram. This article is part of a theme issue on collaborative modeling.

Building a business domain meta-ontology for information pre-processing

Business analysts, along with other business domain software application users, have created a vast amount of business documents, which often do not have any business domain ontologies in the background. This situation leads to misinterpretation of such documents, when being processed by machines, that results in inhibiting the productiveness of computer-assisted analytical work and effectiveness of business solutions due to lack of effective semantics; therefore, business analysts (especially, if rotating) can use well-designed business domain ontologies as a backbone for their official applications. The process of extracting and capturing domain ontologies from these voluminous documents requires extensive involvement of domain experts and application of methods of ontology learning that is substantially labor intensive; therefore, some intermediate solutions which would assist in capturing business domain ontologies must be developed. The present paper proposes a solution in this direction which involves building a meta-ontology as a rapid approach in conceptualizing a business domain from huge amounts of source documents. This meta-ontology can be populated by ontological concepts, attributes and relations from business documents, and then refined in order to form better business domain ontology either through automatic ontology learning methods or some other traditional ontology building approaches.

Type inference in flexible model-driven engineering using classification algorithms

Flexible or bottom-up model-driven engineering (MDE) is an emerging approach to domain and systems modelling. Domain experts, who have detailed domain knowledge, typically lack the technical expertise to transfer this knowledge using traditional MDE tools. Flexible MDE approaches tackle this challenge by promoting the use of simple drawing tools to increase the involvement of domain experts in the language definition process. In such approaches, no metamodel is created upfront, but instead the process starts with the definition of example models that will be used to infer the metamodel. Pre-defined metamodels created by MDE experts may miss important concepts of the domain and thus restrict their expressiveness. However, the lack of a metamodel, that encodes the semantics of conforming models has some drawbacks, among others that of having models with elements that are unintentionally left untyped. In this paper, we propose the use of classification algorithms to help with the inference of such untyped elements. We evaluate the proposed approach in a number of random generated example models from various domains. The correct type prediction varies from 23 to 100% depending on the domain, the proportion of elements that were left untyped and the prediction algorithm used.

Agile validation of model transformations using compound F-Alloy specifications

• We introduce Hybrid Analysis as an efficient way to validate F-Alloy specifications. • We propose an F-Alloy extension to specify compound model transformations. • We describe VBV, a domain specific validation process for model transformations. • VBV is based on compound model transformations. Model transformations play a key role in model driven software engineering approaches. Validation of model transformations is crucial for the quality assurance of software systems to be constructed. The relational logic based specification language Alloy and its accompanying tool the Alloy Analyzer have been used in the past to validate properties of model transformations. However Alloy based analysis of transformations suffers from several limitations. On one hand, it is time consuming and does not scale well. On the other hand, the reliance on Alloy, being a formal method, prevents the effective involvement of domain experts in the validation process which is crucial for pinpointing domain pertinent errors. Those limitations are even more severe when it comes to transformations whose input and/or output are themselves transformations (called compound transformations) because they are inherently more complex. To tackle the performance and scalability limitations, in previous work, we proposed an Alloy-based Domain Specific Language (DSL), called F-Alloy, that is tailored for model transformation specifications. Instead of pure analysis based validation, F-Alloy speeds up the validation of model transformations by applying a hybrid strategy that combines analysis with interpretation. In this paper, we formalize the notion of “hybrid analysis” and further extended it to also support efficient validation of compound transformations. To enable the effective involvement of domain experts in the validation process, we propose in this paper a new approach to model transformation validation, called Visualization-Based Validation (briefly VBV). Following VBV, representative instances of a to-be-validated model transformation are automatically generated by hybrid analysis and shown to domain experts for feedback in a visual notation that they are familiar with. We prescribe a process to guide the application of VBV to model transformations and illustrate it with a benchmark model transformation.

Constraint programming for type inference in flexible model-driven engineering

Domain experts typically have detailed knowledge of the concepts that are used in their domain; however they often lack the technical skills needed to translate that knowledge into model-driven engineering (MDE) idioms and technologies. Flexible or bottom-up modelling has been introduced to assist with the involvement of domain experts by promoting the use of simple drawing tools. In traditional MDE the engineering process starts with the definition of a metamodel which is used for the instantiation of models. In bottom-up MDE example models are defined at the beginning, letting the domain experts and language engineers focus on expressing the concepts rather than spending time on technical details of the metamodelling infrastructure. The metamodel is then created manually or inferred automatically. The flexibility that bottom-up MDE offers comes with the cost of having nodes in the example models left untyped. As a result, concepts that might be important for the definition of the domain will be ignored while the example models cannot be adequately re-used in future iterations of the language definition process. In this paper, we propose a novel approach that assists in the inference of the types of untyped model elements using Constraint Programming. We evaluate the proposed approach in a number of example models to identify the performance of the prediction mechanism and the benefits it offers. The reduction in the effort needed to complete the missing types reaches up to 91.45% compared to the scenario where the language engineers had to identify and complete the types without guidance.

WOUND UP MALAYSIAN COMPANIES' PATTERN EXPLORATION USING DATA MINING METHODS

A company is wound up when the company is unable to pay financial debts or is experiencing serious financial distress. From the year 1998 until 2003, an average of 1166 companies were wound up yearly. This research focuses on the knowledge exploration of wound up companies in Malaysia using association rules mining techniques (quantitative) and the involvement of domain expert in knowledge evaluation (qualitative). Association Rules Mining technique is used to mine the implicit patterns of wound up companies by analyzing relationships between attributes such as total asset, total liability and profit and loss. The human expert functions to verify the significant relation between attributes and the mined patterns. This research succeeded to mine 2 quantitative criteria and 9 qualitative criteria related to wound up companies. The criteria combination can be utilized for evaluating the risk of wound up Malaysian companies in the future.

Availability Communication: Requirements for an Awareness System to Support Nurses' Handling of Nurse Calls.

The recent development of mobile technologies allows nurses to receive different types of requests anywhere. However, the interruptions generated by these devices often presents a challenge for nurses in their daily work in a hospital department. In previous inquires we have investigated nurses' strategies to managing technology-mediated interruptions in the form of nurse calls. This study reports on an effort to co-design a system that supports an important strategy employed by nurses. Through the involvement of domain experts, the study elicits requirements for an awareness system to support nurses' collaborative effort in handling nurse calls.

A generalizable NLP framework for fast development of pattern-based biomedical relation extraction systems.

BackgroundText mining is increasingly used in the biomedical domain because of its ability to automatically gather information from large amount of scientific articles. One important task in biomedical text mining is relation extraction, which aims to identify designated relations among biological entities reported in literature. A relation extraction system achieving high performance is expensive to develop because of the substantial time and effort required for its design and implementation. Here, we report a novel framework to facilitate the development of a pattern-based biomedical relation extraction system. It has several unique design features: (1) leveraging syntactic variations possible in a language and automatically generating extraction patterns in a systematic manner, (2) applying sentence simplification to improve the coverage of extraction patterns, and (3) identifying referential relations between a syntactic argument of a predicate and the actual target expected in the relation extraction task.ResultsA relation extraction system derived using the proposed framework achieved overall F-scores of 72.66% for the Simple events and 55.57% for the Binding events on the BioNLP-ST 2011 GE test set, comparing favorably with the top performing systems that participated in the BioNLP-ST 2011 GE task. We obtained similar results on the BioNLP-ST 2013 GE test set (80.07% and 60.58%, respectively). We conducted additional experiments on the training and development sets to provide a more detailed analysis of the system and its individual modules. This analysis indicates that without increasing the number of patterns, simplification and referential relation linking play a key role in the effective extraction of biomedical relations.ConclusionsIn this paper, we present a novel framework for fast development of relation extraction systems. The framework requires only a list of triggers as input, and does not need information from an annotated corpus. Thus, we reduce the involvement of domain experts, who would otherwise have to provide manual annotations and help with the design of hand crafted patterns. We demonstrate how our framework is used to develop a system which achieves state-of-the-art performance on a public benchmark corpus.

Ontology Based Data Analysing Approach for Actionable Knowledge Discovery

In Data Mining, the effectiveness of association rules is limited by the huge quantity of delivered rules. In this manuscript, we propose a new approach to prune and filter discovered rules. An interactive and iterative framework is designed to assist the user along the analyzing task. The manuscript focus on medical data test set for detailed analysis and formation of the ontologies. In the proposed approach, the data set of medical records is entered in the back - end database. The development of associations and the ontology is fully dynamic. In case, any symptom is searched, the proposed approach search from the back end database and creates the ontology that is dynamic in execution. It refers that the unsupervised approach of ontology generation is implemented so that the unbiased results can be achieved. The implementation of proposed work shows the dynamic results in terms of rules found and their effectiveness in the real world scenario. 3 M) which is intended to make data mining workable in supporting decision-making actions in the real world. Current data mining tools and techniques drive a paradigm shift from traditional data-centred hidden pattern mining to domain-driven actionable knowledge discovery (AKD). AKD must cater for domain knowledge and environmental factors, balance technical significance and business expectations from both objective and subjective perspectives and support automatically converting patterns into deliverables in business friendly and operable forms such as actions or rules. In domain-driven framework, data mining analysts and domain-specific business analysts complement each other with regard to in-depth granularity and constrained environment through interactive system support. The involvement of domain experts and their knowledge can assist in developing highly effective domain specific data mining techniques, and reduce the complexity of knowledge discovery. The involvement of domain-related social intelligence into KDD process not only strengthens technical development and performance, but highlights business expectations and actionable capability of the identified results. In Data Mining, the effectiveness of association rules is strappingly limited by the huge quantity of delivered rules. In this manuscript, we propose a new approach to prune and filter discovered rules. An association rule is described as the implication X → Y where X and Y are sets of items and

A non-parametric non-filtering approach to bearing fault detection in the presence of multiple interference

Reliable fault detection of bearing faults is crucial to avoid costly machine failures. To this end, many methods have been proposed over the years. However, most of them are dependent on properly selected parameters, such as the center frequency and the bandwidth of the bandpass filter for the pass band in the high-frequency resonance method. Such parameters may also have to be updated in a variable operating environment which may not always be possible without the involvement of domain experts. As such, we propose a new non-parametric and non-filtering method which can perform reasonably well in the presence of vibration interference and noise. This method is based on the derivation of amplitude demodulation of a signal to suppress unwanted periodic interference components in the acquired signal. It has been shown that the proposed method can boost the signal-to-interference ratio by up to 2–12 times compared with the energy operator approach and can handle signals tinted by both noise and multiple interference. It compares favorably with other non-parameter methods, such as the plain envelope method and energy operator method. Its effectiveness has further been demonstrated using both simulated and experimental data.

A Methodology and a Web Platform for the Collaborative Development of Context-Aware Systems

Information and services personalization is essential for an optimal user experience. Systems have to be able to acquire data about the user's context, process them in order to identify the user's situation and finally, adapt the functionality of the system to that situation, but the development of context-aware systems is complex. Data coming from distributed and heterogeneous sources have to be acquired, processed and managed. Several programming frameworks have been proposed in order to simplify the development of context-aware systems. These frameworks offer high-level application programming interfaces for programmers that complicate the involvement of domain experts in the development life-cycle. The participation of users that do not have programming skills but are experts in the application domain can speed up and improve the development process of these kinds of systems. Apart from that, there is a lack of methodologies to guide the development process. This article presents as main contributions, the implementation and evaluation of a web platform and a methodology to collaboratively develop context-aware systems by programmers and domain experts.

BUDUJĄC MOSTY MIĘDZY TEORIĄ I PRAKTYKĄ: ROZWÓJ TERMINOLOGII W RAMACH PRAWA I ADMINISTRACJI

The purpose of this paper is to illustrate the workflow for the elaboration of multilingual terminology in the legal and administrative domain. Next to giving a short overview over each single step in the workflow, we focus on two important aspects that make multilingual terminology work in the legal domain so challenging and partly different from multilingual terminology work in other domains: the micro-comparative approach and the strong involvement of domain experts. Finally, we discuss a series of practical aspects that distinguish work in the legal domain from terminology work in other domains and partly even clash against some of the requirements set by general terminology theory and practice.