A Conceptual Model for Assistant Platforms

Assistant platforms are becoming a key element for the business model of many companies. They have evolved from assistance systems that provide support when using information (or other) systems to platforms in their own. Alexa, Cortana or Siri may be used with literally thousands of services. From this background, this paper develops the notion of assistant platforms and elaborates a conceptual model that supports businesses in developing appropriate strategies. The model consists of three main building blocks, an architecture that depicts the components as well as the possible layers of an assistant platform, the mechanism that determines the value creation on assistant platforms, and the ecosystem with its network effects, which emerge from the multi-sided nature of assistant platforms. The model has been derived from a litera-ture review and is illustrated with examples of existing assistant platforms. Its main purpose is to advance the understanding of assistant platforms and to trigger future research

Design and analysis of a broadband SIS-mixer for the Heterodyne Instrument for the Far Infrared (HIFI) on the Herschel Space Observatory

In this thesis the design and analysis of SIS-mixers (SIS: superconductor-isolator-superconductor) for the use as low-noise and broad-band detectors in the submm-region for astronomical observations is described. The SIS-mixers have been developed for the frequency band 2 (636-802 GHz) of the Heterodyne Instrument for the Far Infrared (HIFI) on ESA’s space-observatory Herschel. The required performance baseline has been defined by the HIFI-consortium in terms of the estimated noise temperature contribution of the mixer to be 110 K at 636 GHz and 150 K at 802 GHz. In the frequency region between 80 GHz and 900 GHz Heterodyne SIS mixers are established as the best devices for low noise mixing with a high spectral resolution (mixing of the signal radiation with local oscillator, LO). For the HIFI band 2 mixers, the technology of Nb/Al-Al2O3/Nb-junctions has been used with junction areas of 0.5-1.0 um2, a target value for the current density jc of 15 kA/cm2 and a gap-voltage of 2.75-2.77 mV. For the compensation of the SIS-junction’s intrinsic capacitance and the optimized power coupling to the tunnel junction, two types of matching circuits have been theoretically modelled and experimentally studied: (1) three-step transformer single junction devices and (2) double-junction devices. This has been done for two material combinations: for an all-superconductive micro strip NbTiN/SiO2/Nb, and for the superconductor/normal-conductor combination NbTiN/SiO2/Al...thesi

I Like Your Apron And Your Bonnet And Your Little Quaker Gown

https://digitalcommons.library.umaine.edu/mmb-vp/1568/thumbnail.jp

Introduction to the Minitrack on Artificial Intelligence-based Assistants

Artificial Intelligence (AI) has received much attention due to the recent progress in several technological areas such as image detection, translation, and decision support. Established businesses and many start-up businesses are eagerly discussing how they can gain a competitive advantage from complementing their products, services and processes with AI. In fact, based on the research in the AI domain since several decades, a broad variety of promising application fields were suggested where AI might add business value. Meanwhile, applications are not limited to simple structured problems, but even applications higher complexities are feasible, which require higher levels of “intelligence”. To avoid discussions on the ambivalent notion of “intelligence”, it shall refer to tasks involving perception, processing, action and learning. Many applications are possible along these activities, in particular a user’s interaction via natural language

Measuring Ecosystem Complexity - Decision-Making Based on Complementarity Graphs

Platforms feature increasingly complex architectures with regard to interconnecting with other digital platforms as well as with a variety of devices and services. This development also impacts the structure of digital platform ecosystems and forces providers of these services, devices, and services to incorporate this complexity in their decision-making. To contribute to the existing body of knowledge on measuring ecosystem complexity, the present research proposes two key artefacts based on ecosystem intelligence: On the one hand, complementarity graphs represent ecosystems with an ecosystem's functional modules as vertices and complementarities as edges. The nodes carry information about the category membership of the module. On the other hand, a process is suggested that can collect important information for ecosystem intelligence using proxies and web scraping. Our approach allows replacing data, which today is largely unavailable due to competitive reasons. We demonstrated the use of the artefacts in category-oriented complementarity maps that aggregate the information from complementarity graphs and support decision-making. They show which combination of module categories creates strong and weak complementarities. The paper evaluates complementarity maps and the data collection process by creating category-oriented complementarity graphs on the Alexa skill ecosystem and concludes with a call to pursue more research based on functional ecosystem intelligence

Ecosystem Intelligence for AI-based Assistant Platforms

Digital assistants like Alexa, Google Assistant, or Siri have seen a large adoption over the past years. Using artificial intelligence (AI) technologies, they provide a vocal interface to physical devices as well as to digital services and have spurred an entire new eco-system. This comprises the big tech companies themselves, but also a strongly growing community of developers that make these functionalities available via digital platforms. At present, only few research is available to understand the structure and the value creation logic of these AI-based assistant platforms and their ecosystem. This research adopts ecosystem intelligence to shed light on their structure and dynamics. It combines existing data collection methods with an automated approach that proves useful in deriving a network-based conceptual model of Amazon's Alexa assistant platform and ecosystem. It shows that skills are a key unit of modularity in this ecosystem, which is linked to other elements such as service, data, and money flows. It also suggests that the topology of the Alexa ecosystem may be described using the criteria reflexivity, symmetry, variance, strength, and centrality of the skill coactivations. Finally, it identifies three ways to create and capture value on AI-based assistant platforms. Surprisingly only a few skills use a transactional business model by selling services and goods but many skills are complementary and provide information, configuration, and control services for other skill provider products and services. These findings provide new insights into the highly relevant ecosystems of AI-based assistant platforms, which might serve enterprises in developing their strategies in these ecosystems. They might also pave the way to a faster, data-driven approach for ecosystem intelligence

Developmental Considerations in University-School Collaborative Research

Some common complications that arise in collaborative research between school and university researchers, as well as some conditions for successful collaboration are described in this report. Difficulties possibly attributable to developmental levels of the researchers are discussed utilizing Kegans (1982) theory of constructive developmentalism. A collaborative, qualitative study of needs for independence and inclusion in two fifth grade classrooms is described to illustrate the importance of attending to issues of differing perspectives and experiences that may be related to development. The authors suggest that researchers carefully consider issues of role, status, and contextual differences, as well as the developmental maturity of those with whom they engage in collaborative research

What should be recycled: An integrated model for product recycling desirability

This research was focused on developing a new scientific approach for prioritising recycling of end-of-life products in a circular economy. To date, product complexity based on the mixture of materials has been used as a predictor of what gets recycled. While the separation of materials that make up a product has been modelled as a measure of product complexity, this does not taken into account the benefits and considerations in recycling products. In this paper, a new agenda and approach to prioritise the recycling of products was developed based on a recycling desirability index. The material mixing complexity measure was inverted into a simplicity index and then extended by modelling the security index for the mix of materials and the technological readiness level of recycling technologies. The extended model is proposed as an integrated measure of the desirability of recycling end-of-life products. From this analysis, an apparent recycling desirability boundary, enabling products to be prioritised for recycling, was developed. This model and analysis can be used as an information source in developing policies and product recycling priorities

Bagged textural and color features for melanoma skin cancer detection in dermoscopic and standard images

Early detection of malignant melanoma skin cancer is crucial for treating the disease and saving lives. Many computerized techniques have been reported in the literature to diagnose and classify the disease with satisfactory skin cancer detection performance. However, reducing the false detection rate is still challenging and preoccupying because false positives trigger the alarm and require intervention by an expert pathologist for further examination and screening. In this paper, an automatic skin cancer diagnosis system that combines different textural and color features is proposed. New textural and color features are used in a bag-of-features approach for efficient and accurate detection. We particularly claim that the Histogram of Gradients (HG) and the Histogram of Lines (HL) are more suitable for the analysis and classification of dermoscopic and standard skin images than the conventional Histogram of Oriented Gradient (HOG) and the Histogram of Oriented Lines (HOL), respectively. The HG and HL are bagged separately using a codebook for each and then combined with other bagged color vector angles and Zernike moments to exploit the color information. The overall system has been assessed through intensive experiments using different classifiers on a dermoscopic image dataset and another standard dataset. Experimental results have shown the superiority of the proposed system over state-of-the-art techniques