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Vertical information systems (VIS) standards are technical specifications designed to promote coordination among the organizations within (or across) vertical industry sectors. Examples include the bar code, electronic data interchange (EDI) standards, and RosettaNet business process standards in the electronics industry. This contribution examines VIS standardization through the lens of collective action theory, applied in the literature to information technology product standardization, but not yet to VIS standardization, which is led by heterogeneous groups of user organizations rather than by IT vendors. Through an intensive case analysis of VIS standardization in the U.S. residential mortgage industry, VIS standardization success is shown to be as problematic as IT product standardization success, but for different reasons. VIS standardization involves two linked collective action dilemmas-standards development and standards diffusion-with different characteristics, such that a solution to the first may fail to resolve the second. Whereas prior theoretical and empirical research shows that IT product standardization efforts tend to splinter into rival factions that compete through standards wars in the marketplace, successful VIS standards consortia must encompass heterogeneous groups of user organizations and IT vendors without fragmenting. Some tactics successfully used to solve the collective action dilemma of VIS standardization (e.g., governance mechanisms and policies about intellectual property protection) are also used by IT product standardization efforts, but some are different, and successful VIS standardization requires a package of solutions tailored to fit and jointly resolve the specific dilemmas of particular VIS standards initiatives.

Background Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients’ disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning models and high dimensional data sources such as electronic health records, magnetic resonance imaging scans, cardiotocograms, etc. These foundations make interpretation and explanation of the CAD advice very challenging. This challenge is recognised throughout the machine learning research community. eXplainable Artificial Intelligence (XAI) is emerging as one of the most important research areas of recent years because it addresses the interpretability and trust concerns of critical decision makers, including those in clinical and medical practice. Methods In this work, we focus on AdaBoost, a black box model that has been widely adopted in the CAD literature. We address the challenge – to explain AdaBoost classification – with a novel algorithm that extracts simple, logical rules from AdaBoost models. Our algorithm, Adaptive-Weighted High Importance Path Snippets (Ada-WHIPS), makes use of AdaBoost’s adaptive classifier weights. Using a novel formulation, Ada-WHIPS uniquely redistributes the weights among individual decision nodes of the internal decision trees of the AdaBoost model. Then, a simple heuristic search of the weighted nodes finds a single rule that dominated the model’s decision. We compare the explanations generated by our novel approach with the state of the art in an experimental study. We evaluate the derived explanations with simple statistical tests of well-known quality measures, precision and coverage, and a novel measure stability that is better suited to the XAI setting. Results Experiments on 9 CAD-related data sets showed that Ada-WHIPS explanations consistently generalise better (mean coverage 15%-68%) than the state of the art while remaining competitive for specificity (mean precision 80%-99%). A very small trade-off in specificity is shown to guard against over-fitting which is a known problem in the state of the art methods. Conclusions The experimental results demonstrate the benefits of using our novel algorithm for explaining CAD AdaBoost classifiers widely found in the literature. Our tightly coupled, AdaBoost-specific approach outperforms model-agnostic explanation methods and should be considered by practitioners looking for an XAI solution for this class of models.

Background The hypochromic microcytic anemia (HMA) commonly found in Thailand are iron deficiency anemia (IDA) and thalassemia trait (TT). Accurate discrimination between IDA and TT is an important issue and better methods are urgently needed. Although considerable RBC formulas and indices with various optimal cut-off values have been developed, distinguishing between IDA and TT is still a challenging problem due to the diversity of various anemic populations. To address this problem, it is desirable to develop an improved and automated prediction model for discriminating IDA from TT. Methods We retrospectively collected laboratory data of HMA found in Thai adults. Five machine learnings, including k -nearest neighbor ( k -NN), decision tree, random forest (RF), artificial neural network (ANN) and support vector machine (SVM), were applied to construct a discriminant model. Performance was assessed and compared with thirteen existing discriminant formulas and indices. Results The data of 186 patients (146 patients with TT and 40 with IDA) were enrolled. The interpretable rules derived from the RF model were proposed to demonstrate the combination of RBC indices for discriminating IDA from TT. A web-based tool ‘ThalPred’ was implemented using an SVM model based on seven RBC parameters. ThalPred achieved prediction results with an external accuracy, MCC and AUC of 95.59, 0.87 and 0.98, respectively. Conclusion ThalPred and an interpretable rule were provided for distinguishing IDA from TT. For the convenience of health care team experimental scientists, a web-based tool has been established at http://codes.bio/thalpred/ by which users can easily get their desired screening test result without the need to go through the underlying mathematical and computational details.

The high volatility of an asset in financial markets is commonly seen as a negative factor. However short-term trades may entail high profits if traders open and close the correct positions. The high volatility of cryptocurrencies, and in particular of Bitcoin, is what made cryptocurrency trading so profitable in these last years. The main goal of this work is to compare several frameworks each other to predict the daily closing Bitcoin price, investigating those that provide the best performance, after a rigorous model selection by the so-called k-fold cross validation method. We evaluated the performance of one stage frameworks, based only on one machine learning technique, such as the Bayesian Neural Network, the Feed Forward and the Long Short Term Memory Neural Networks, and that of two stages frameworks formed by the neural networks just mentioned in cascade to Support Vector Regression. Results highlight higher performance of the two stages frameworks with respect to the correspondent one stage frameworks, but for the Bayesian Neural Network. The one stage framework based on Bayesian Neural Network has the highest performance and the order of magnitude of the mean absolute percentage error computed on the predicted price by this framework is in agreement with those reported in recent literature works.

This paper introduces a token-based digital currency (TBDC) model for standardizing service delivery in an aviation technical support as a service (ATSaaS) platform. The model addresses the challenges of service standardization and valuation by integrating cost, time, and quality parameters into a unified framework. Unlike traditional cryptocurrencies, this specialized digital currency incorporates intrinsic service valuation mechanisms that dynamically reflect the worth of aviation technical support services. The research presents a mathematical formulation for token value calculation, including a Service Passport framework for comprehensive documentation and a systematic approach for service integration. The model is validated through a numerical case study focusing on maintenance, repair, and overhaul services, demonstrating its effectiveness in generating fair token values across diverse service types. The study introduces optimization techniques using machine learning to enhance token calculations, successfully standardizing heterogeneous services while maintaining flexibility and transparency. Implementation challenges and future developments are identified. The TBDC model provides a foundation for transforming aviation technical support services, particularly benefiting small airlines through improved efficiency, standardization, and accessibility.

Education in multiple forms and diverse geographical contexts delivers quality in all aspects of learning in which stakeholders such as students, instructors, and educational institutions play an important role. Quality assurance in higher education ensures the smooth functioning of the teaching and learning process by supporting the attainment of the desired quality levels of learning outcomes. This further leads to educational sustainability, as education has been acknowledged as a strategic constituent of sustainability-focused strategies in many educational contexts. Hence, it has become very important for educational institutions to maintain quality standards through the implementation of appropriate strategies, as quality is the lifeline of both Traditional Learning and E-Learning, and a lack of a suitable assessment standard affects the quality of learning. This research study attempts to address the existing gaps observed following a review of the related literature. This study collected qualitative data using an observation method through the observations and review of online software used at the Saudi Electronic University, namely Blackboard Learning Management System (LMS), Tawkeed Quality Management E-System, and Blue Survey software. In addition to this, the expertise of the research team members was also utilized for this research study in designing E-Learning quality dimensions. The purpose of this study was to propose an E-Learning Quality Assessment Standard that will help third-level educational institutions to assess their current teaching and learning practices of E-Learning and support them in enhancing the overall students’ experiences toward E-Learning within their institutions. As a research outcome, a conceptual quality assessment standard titled “SPECIFIERS” was proposed to offer a helping hand during the E-Learning quality assessment process to ensure sustainable education development of global educational institutions.

Natural disaster response and assessment are key elements of natural hazard monitoring and risk management. Currently, the existing systems are not able to meet the specific needs of many regional stakeholders worldwide; traditional approaches with field surveys are labor-intensive, time-consuming, and expensive, especially for severe disasters that affect a large geographic area. Recent studies have demonstrated that Earth observation (EO) data and technologies provide powerful support for the natural disaster emergency response. However, challenges still exist in support of the entire disaster lifecycle—preparedness, response, and recovery—which build the gaps between the disaster Spatial Data Infrastructure (SDI) already-in-place requirements and the EO capabilities. In order to tackle some of the above challenges, this paper demonstrates how to facilitate typhoon-triggered flood disaster-ready information delivery using an SDI services approach, and proposes a web-based remote sensing disaster decision support system to facilitate natural disaster response and impact assessment, which implements on-demand disaster resource acquisition, on-the-fly analysis, automatic thematic mapping, and decision report release. The system has been implemented with open specifications to facilitate interoperability. The typhoons and floods in Hainan Province, China, are used as typical scenarios to verify the system’s applicability and effectiveness. The system improves the automation level of the natural disaster emergency response service, and provides technical support for regional remote-sensing-based disaster mitigation in China.

Brazilian Federal Institutes of Education, Science and Technology (FIs) have expanded through multicampi structure, which has led to profound changes in their administrative and academic organization. As consequence of this expansion, the demand for services, systems, and information technology (IT) solutions has increased; and the support service’s provision has become much more relevant. However, a diagnosis of the quality of the IT support services, performed with the FI’s CIOs, has shown that the service’s provision in these institutions is below required. There are several problems encountered, such as political and cultural issues, lack of stakeholder involvement, insufficient staff, resistance to change, lack of priorities, excessive demands, lack of knowledge of best practices, and the use of inappropriate tools. So, there is a clear need to develop a proposal to help the FIs to improve their IT support services. The Service Desk is an alternative, since it implements a unique interface between users and the IT sector, however, with a broader role than just the technical support, as it embraces processes, people, and technologies geared to IT management. This work aimed to develop a framework, with a practical approach on “how to do,” which guides the implementation and/or the improvement of Service Desks of the Federal Institutes. The proposal sought incorporates several practices related to Service Desk, identified in ITIL, ISO 20000, CMMI-SVC, and MR-MPS-SV models, creating a deployment and/or improvement approach through a life cycle framework, based on the IDEAL model, and a process toolbox, structured according to the seven dimensions of the EPMF. The research is relevant due to the lack of guidelines for the implementation and/or improvement of Service Desks from a practical point of view, since the main models found focus on “what has to be done,” and little on “how to do.” The IT support service’s current situation diagnosis in the IFs showed that the service provisioning was less than expected. The need to develop a proposal to help the FIs to improve the IT support service became evident, and the QoS-IT framework emerged. After the framework’s development, it was evaluated by IFs specialists and, posteriorly, it was used and evaluated in the context of a specific Federal Institute. Finally, a gap analysis was done between user satisfaction surveys, comparing the results obtained before and after the Service Desk deployment at the Federal Institute, which presented evidences of a positive impact on the service provision after using the framework.

In order to expand the concept and form of teacher training, a remote human-computer interaction and STEM teacher online training platform based on the embedded Internet of Things is proposed. Based on the content analysis method, this article sorts out the development status of the current online teacher training platform and points out the shortcomings of the platform in information aggregation, resource construction, content setting, presentation, learning support services, and other aspects. Based on the combination of new concepts and new technologies, an online training platform for STEM teachers is proposed for the construction of the platform from four perspectives: innovative design concepts, enriching platform promotion ideas, developing educational makers, and improving support services, and the results of the use are investigated. The results show that the subjects believe that the STEM teacher online training system has clarified the STEM curriculum design steps, standardized STEM design methods, improved their cooperative learning ability, and cultivated design thinking. Among them, 13 people think that the training system has made STEM design steps clearer, “the design process is clearer,” “it makes me clearer about the whole design process,” and “the course design and development process is clearer.” Conclusion. This platform can provide a reference for online training and professional development of Chinese teachers.