A Theoretical Framework of Creativity Software, Idea Creativity, and Group Satisfaction

Idea generation software can be useful in electronic brainstorming and creativity tasks. Based on the theory of task/technology fit, we discuss two software features: graphic/outline mode and communication support, and propose that these features can improve group’s creative performance in an electronic brainstorming task. We assess group’s creative performance by idea creativity, which in turn can affect group members’ satisfaction with the outcome of the electronic brainstorming session, and satisfaction with the electronic brainstorming process. We develop a theoretical framework to explain these relationships and state propositions associated with the research model. Practitioners can use the model to improve an electronic brainstorming session and researchers can extend our framework by exploring in depth the software interactive mode and communication support of idea generation software, and the interaction of both features

Development and evaluation of a lesson authoring tool for AutoTutor

This paper describes the process of developing an Electronic Performance Support System (EPSS) for AutoTutor 3D. The new architecture of AutoTutor 3D has four models: Domain model, Student model, Tutor model and Interface model. To date, the complexity of authoring the scripts used by AutoTutor has presented a significant challenge. Creation of a tool to simplify this process gives us the ability to disseminate AutoTutor across many different domains. The tool was created using a rapid prototyping approach and incorporates real world case based scenarios based on actual teacher experience with the tool, and a point-and-query help system. This tool and the model for its design may inform the development of similar EPSSs in the future

Electronic Disclosure and Financial Knowledge Management

In this paper we report the benefits of using eXtended Markup Language (XML) to support financial knowledge management, which include indexing, organizing, association generation, cross-referencing, and retrieval of financial information to support the generation of knowledge. The current searching engines cannot provide sufficient performance, such as, recall, precision, extensibility, etc, to support users of financial information. XML is able to partially solve such problem by providing tags to create structures. XML provides a vendor-neutral approach to structure and organize contents. XML authors are allowed to create arbitrary tags to describe the format or structure of data, rather than restricted to a specific number of tags given in the specification of HTML. A prototype of XML-based ELectronic Financial Filing System (ELFFS-XML) has been developed to illustrate how to apply XML to model and add value to traditional HTML-based financial information by cross-linking related information from different data sources, which is an important step in moving from traditional information management to knowledge management. We compared the functionality of XML-based ELFFS with the original HTML-based ELFFS and SEDAR, an electronic filing system used in Canada, and recommended some directions for future development of similar electronic filing systems

Machine learning for additive manufacturing of electronics

Quality of electronic products fabricated with additive manufacturing (AM) techniques such as 3D inkjet printing can be assured by adopting pro-active predictive models for process condition monitoring instead of using conventional post-manufacture assessment techniques. This paper details a model-based approach, and associated machine learning algorithms, which can be used to achieve and maintain optimal product quality during production runs and to realise model predictive process control (MPC). The investigated data-driven prognostics based on state-space modelling of the dynamic behaviour of 3D inkjet printing for electronics manufacturing is new and makes it an original contribution. 3D printing of conductive lines for electronic circuits is a main targeted application, and is used to demonstrate and validate the prognostics capability of machine learning models developed from measured process data. The results show that, for moderately non-linear dynamics of the 3D-Printing process, state-space models can inform on the expected process trends (states) and related product quality characteristics even over large prediction horizons. The models can also support the realisation of model predictive process control for optimal target performance

Supporting Treatment Adherence Readiness through Training (START) for patients with HIV on antiretroviral therapy: study protocol for a randomized controlled trial.

BackgroundFew HIV antiretroviral adherence interventions target patients before they start treatment, assess adherence readiness to determine the timing of treatment initiation, or tailor the amount of adherence support. The Supporting Treatment Adherence Readiness through Training (START) intervention, based on the information-motivation-behavioral skills model of behavior change, is designed to address these gaps with the inclusion of (1) brief pill-taking practice trials for enhancing pretreatment adherence counseling and providing a behavioral criterion for determining adherence readiness and the timing of treatment initiation and (2) a performance-driven dose regulation mechanism to tailor the amount of counseling to the individual needs of the patient and conserve resources. The primary aim of this randomized controlled trial is to examine the effects of START on antiretroviral adherence and HIV virologic suppression.Methods/designA sample of 240 patients will be randomized to receive START or usual care at one of two HIV clinics. Primary outcomes will be optimal dose-taking adherence (>85 % prescribed doses taken), as measured with electronic monitoring caps, and undetectable HIV viral load. Secondary outcomes will include dose-timing adherence (>85 % prescribed doses taken on time) and CD4 count. Primary endpoints will be month 6 (short-term effect) and month 24 (to test durability of effect), though electronic monitoring will be continuous and a fully battery of assessments will be administered every 6 months for 24 months.DiscussionIf efficacious and cost-effective, START will provide clinicians with a model for assessing patient adherence readiness and helping patients to achieve and sustain readiness and optimal treatment benefits.Trial registrationClinicalTrials.gov identifier NCT02329782 . Registered on 22 December 2014

Evolutionary Optimization Of Support Vector Machines

Support vector machines are a relatively new approach for creating classifiers that have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the classifier, as well as a stronger mathematical background based on optimization and statistical learning theory. This thesis deals with the problem of model selection with support vector machines, that is, the problem of finding the optimal parameters that will improve the performance of the algorithm. It is shown that genetic algorithms provide an effective way to find the optimal parameters for support vector machines. The proposed algorithm is compared with a backpropagation Neural Network in a dataset that represents individual models for electronic commerce

Automatic Detection of Hypoglycemic Events From the Electronic Health Record Notes of Diabetes Patients: Empirical Study

BACKGROUND: Hypoglycemic events are common and potentially dangerous conditions among patients being treated for diabetes. Automatic detection of such events could improve patient care and is valuable in population studies. Electronic health records (EHRs) are valuable resources for the detection of such events. OBJECTIVE: In this study, we aim to develop a deep-learning-based natural language processing (NLP) system to automatically detect hypoglycemic events from EHR notes. Our model is called the High-Performing System for Automatically Detecting Hypoglycemic Events (HYPE). METHODS: Domain experts reviewed 500 EHR notes of diabetes patients to determine whether each sentence contained a hypoglycemic event or not. We used this annotated corpus to train and evaluate HYPE, the high-performance NLP system for hypoglycemia detection. We built and evaluated both a classical machine learning model (ie, support vector machines [SVMs]) and state-of-the-art neural network models. RESULTS: We found that neural network models outperformed the SVM model. The convolutional neural network (CNN) model yielded the highest performance in a 10-fold cross-validation setting: mean precision=0.96 (SD 0.03), mean recall=0.86 (SD 0.03), and mean F1=0.91 (SD 0.03). CONCLUSIONS: Despite the challenges posed by small and highly imbalanced data, our CNN-based HYPE system still achieved a high performance for hypoglycemia detection. HYPE can be used for EHR-based hypoglycemia surveillance and population studies in diabetes patients