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Technology trend analysis uses data relevant to historical performance and extrapolates it to estimate and assess the future potential of technology. Such analysis is used to analyze emerging technologies or predict the growing markets that influence the resulting social or economic development to assist in effective decision-making. Traditional trend analysis methods are time-consuming and require considerable labor. Moreover, the implemented processes may largely rely on the specific knowledge of the domain experts. With the advancement in the areas of science and technology, emerging cross-domain trends have received growing attention for its considerable influence on society and the economy. Consequently, emerging cross-domain predictions that combine or complement various technologies or integrate with diverse disciplines may be more critical than other tools and applications in the same domain. This study uses a design science research methodology, a text mining technique, and social network analysis (SNA) to analyze the development trends concerning the presentation of the product or service information on a company's website. This study applies regulatory technology (RegTech) as a case to analyze and justify the emerging cross-disciplinary trend. Furthermore, an experimental study is conducted using the Google search engine to verify and validate the proposed research mechanism at the end of this study. The study results reveal that, compared with Google Trends and Google Correlate, the research mechanism proposed in this study is more illustrative, feasible, and promising because it reduces noise and avoids the additional time and effort required to perform a further in-depth exploration to obtain the information.

In this article, the Mingei Online Platform is presented as an authoring platform for the representation of social and historic context encompassing a focal topic of interest. The proposed representation is employed in the contextualised presentation of a given topic, through documented narratives that support its presentation to diverse audiences. Using the obtained representation, the documentation and digital preservation of social and historical dimensions of Cultural Heritage are demonstrated. The implementation follows the Human-Centred Design approach and has been conducted under an iterative design and evaluation approach involving both usability and domain experts.

As big data becomes ubiquitous across domains, and more and more stakeholders aspire to make the most of their data, demand for machine learning tools has spurred researchers to explore the possibilities of automated machine learning (AutoML). AutoML tools aim to make machine learning accessible for non-machine learning experts (domain experts), to improve the efficiency of machine learning, and to accelerate machine learning research. But although automation and efficiency are among AutoML’s main selling points, the process still requires human involvement at a number of vital steps, including understanding the attributes of domain-specific data, defining prediction problems, creating a suitable training dataset, and selecting a promising machine learning technique. These steps often require a prolonged back-and-forth that makes this process inefficient for domain experts and data scientists alike and keeps so-called AutoML systems from being truly automatic. In this review article, we introduce a new classification system for AutoML systems, using a seven-tiered schematic to distinguish these systems based on their level of autonomy. We begin by describing what an end-to-end machine learning pipeline actually looks like, and which subtasks of the machine learning pipeline have been automated so far. We highlight those subtasks that are still done manually—generally by a data scientist—and explain how this limits domain experts’ access to machine learning. Next, we introduce our novel level-based taxonomy for AutoML systems and define each level according to the scope of automation support provided. Finally, we lay out a roadmap for the future, pinpointing the research required to further automate the end-to-end machine learning pipeline and discussing important challenges that stand in the way of this ambitious goal.

Health coaching applications can include (embodied) conversational agents as coaches. The development of these agents requires an interdisciplinary cooperation between eHealth application developers, interaction designers and domain experts. Therefore, proper dialogue authoring tools and tools to integrate these dialogues in a conversational agent system are essential in the process of creating successful agent-based applications. However, we found no existing open source, easy-to-use authoring tools that support multidisciplinary agent development. To that end, we developed the WOOL Dialogue Platform. The WOOL Dialogue Platform provides the eHealth and conversational agent communities with an open source platform, consisting of a set of easy to use tools that facilitate virtual agent development. The platform consists of a dialogue definition language, an editor, application development libraries and a web service. To illustrate the platform’s possibilities and use in practice, we describe two use cases from EU Horizon 2020 research projects. The WOOL Dialogue Platform is an ‘easy to use, and powerful if needed’ platform for the development of conversational agent applications that is seeing a slow but steady increase in uptake in the eHealth community. Developed to support dialogue authoring for embodied conversational agents in the health coaching domain, this platform’s strong points are its ease of use and ability to let domain experts and agents technology experts work together by providing all parties with tools that support their work effectively.

Abstract The Clock Drawing Test (CDT) is an inexpensive tool to screen for dementia. In this study, we examined if a semi-supervised deep learning (DL) system using Variational Autoencoder (VAE) can extract atypical clock features from a large dataset of unannotated CDTs (n=13,580) and use them to classify dementia (n=18) from non-dementia (n=20) peers. The classification model built with VAE latent space features adequately classified dementia from non-dementia (0.78 Area Under Receiver Operating Characteristics (AUROC)). The VAE-identified atypical clock features were then reviewed by domain experts and compared with existing literature on clock drawing errors. This study shows that a semi-supervised deep learning (DL) analysis of the CDT can extract important clock drawing anomalies that are predictive of dementia.

The customer feedbacks provide alternative and important sources to discover knowledge supporting the marketers and customers to make better decisions. However, the manual process to extract useful information depends on domain experts. This paper focuses on improving the performance of the automatic sentiment information extraction from customer feedbacks. The article proposes a new extraction method that consider multiple dimensions of feedback information, aspect, word, contrast, sentence or phrase, and document levels. The aspect-based sentiment extraction uses a named entity recognition technique to extract the desired aspects of a target product. The aspect-based sentiment combines with sentiment information from multiple levels of feedback contexts resulting in the fused sentiment information improves the extraction performance. We validate the effectiveness by measuring the accuracy of the sentiment and aspect recognition methods comparing with SentiStrength and Word-Count. This information gives some insights on customer satisfaction and can be applied in an alarming tool.

AbstractRobot-based automation is still not widespread in small and medium-sized enterprises, since programming industrial robots is usually costly and only feasible by experts. This disadvantages can be resolved by using intuitive robot programming approaches like playback programming. At the same time, there are currently not automatized automatized, like fiber spraying. We present a novel approach in programming a robot system for fiber spraying processes, which extends a playback programming framework inspired by video editing concepts. The resulting framework allows the programming of also the periphery devices needed for the fiber spraying process. We evaluated the resulting programming framework to measure the intuitiveness in the use and show that the framework is not only able to program fiber spraying tasks but is also rather intuitive to use for domain experts.

Ayurveda medicines uses herbs for curing many ailments without side effects. The biggest concern related to Ayurveda medicine is extinction of many important medicinal herbs, which may be due to insufficient knowledge, weather conditions, and urbanization. Another reason consists of lack of online facts on Indian herbs because it is dependent on books and experts. This concern has motivated in utilizing the machine learning techniques to identify and reveal few details of Indian medicinal herbs because, until now, it is identified manually, which is cumbersome and may lead to errors. Many researchers have shown decent results in identifying and classifying plants with good accuracy and robustness. But no complete framework and strong evidence is projected on Indian medicinal herbs. Accordingly, the chapter aims to provide an outline on how machine learning techniques can be adopted to enrich the knowledge of Indian herbs, which advantages both common man and the domain experts with wide information on traditional herbs.

The adoption of Information & Communication Technology (ICT) is becoming increasingly ubiquitous and it is prominent in Supply Chain too. The objective of this paper is to perform an empirical investigation to assess the impact of ICT in Supply Chain Management (SCM) using Structural Equation Modeling (SEM). A Survey questionnaire was administered and collected from 200+ SCM professionals in sectors such as manufacturing, services, MSMEs, international companies as well as SCM/ERP professionals working as domain experts in IT and service companies. Confirmatory Factor Analysis was used to validate the suggested empirical model & hypothesis based on the impact of ICT deployment on SCOR Level I metrics/indicators as constructs. This research appends to the literature on supply chain performance measures and addresses a recognized gap in terms of rubrics, constructs, assessment frameworks and metrics of these ICT benefits and capabilities in SCM. This framework can be used by any enterprise irrespective of the geography or country, vertical or domain, manufacturing or services.

High Frequency Trading (HFT), mainly based on high speed infrastructure, is a significant element of the trading industry. However, trading machines generate enormous quantities of trading messages that are difficult to explore for financial researchers and traders. Visualization tools of financial data usually focus on portfolio management and the analysis of the relationships between risk and return. Beside risk-return relationship, there are other aspects that attract financial researchers like liquidity and moments of flash crashes in the market. HFT researchers can extract these aspects from HFT data since it shows every detail of the market movement. In this paper, we present HFTViz, a visualization tool designed to help financial researchers explore the HFT dataset provided by NASDAQ exchange. HFTViz provides a comprehensive dashboard aimed at facilitate HFT data exploration. HFTViz contains two sections. It first proposes an overview of the market on a specific date. After selecting desired stocks from overview visualization to investigate in detail, HFTViz also provides a detailed view of the trading messages, the trading volumes and the liquidity measures. In a case study gathering five domain experts, we illustrate the usefulness of HFTViz.