Text Analytics for Android Project

Most advanced text analytics and text mining tasks include text classification, text clustering, building ontology, concept/entity extraction, summarization, deriving patterns within the structured data, production of granular taxonomies, sentiment and emotion analysis, document summarization, entity relation modelling, interpretation of the output. Already existing text analytics and text mining cannot develop text material alternatives (perform a multivariant design), perform multiple criteria analysis, automatically select the most effective variant according to different aspects (citation index of papers (Scopus, ScienceDirect, Google Scholar) and authors (Scopus, ScienceDirect, Google Scholar), Top 25 papers, impact factor of journals, supporting phrases, document name and contents, density of keywords), calculate utility degree and market value. However, the Text Analytics for Android Project can perform the aforementioned functions. To the best of the knowledge herein, these functions have not been previously implemented; thus this is the first attempt to do so. The Text Analytics for Android Project is briefly described in this article

A Proposal for Supply Chain Management Research That Matters: Sixteen High Priority Research Projects for the Future

On May 4th, 2016 in Milton, Ontario, the World Class Supply Chain 2016 Summit was held in partnership between CN Rail and Wilfrid Laurier University’s Lazaridis School of Business & Economics to realize an ambitious goal: raise knowledge of contemporary supply chain management (SCM) issues through genuine peer-­‐to-­‐peer dialogue among practitioners and scholars. A principal element of that knowledge is an answer to the question: to gain valid and reliable insights for attaining SCM excellence, what issues must be researched further? This White Paper—which is the second of the summit’s two White Papers—addresses the question by proposing a research agenda comprising 16 research projects. This research agenda covers the following: The current state of research knowledge on issues that are of the highest priority to today’s SCM professionals Important gaps in current research knowledge and, consequently, the major questions that should be answered in sixteen future research projects aimed at addressing those gaps Ways in which the research projects can be incorporated into student training and be supported by Canada’s major research funding agencies That content comes from using the summit’s deliberations to guide systematic reviews of both the SCM research literature and Canadian institutional mechanisms that are geared towards building knowledge through research. The major conclusions from those reviews can be summarized as follows: While the research literature to date has yielded useful insights to inform the pursuit of SCM excellence, several research questions of immense practical importance remain unanswered or, at best, inadequately answered The body of research required to answer those questions will have to focus on what the summit’s first White Paper presented as four highly impactful levers that SCM executives must expertly handle to attain excellence: collaboration; information; technology; and talent The proposed research agenda can be pursued in ways that achieve the two inter-­‐related goals of creating new actionable knowledge and building the capacity of today’s students to become tomorrow’s practitioners and contributors to ongoing knowledge growth in the SCM field This White Paper’s details underlying these conclusions build on the information presented in the summit’s first White Paper. That is, while the first White Paper (White Paper 1) identified general SCM themes for which the research needs are most urgent, this White Paper goes further along the path of industry-academia knowledge co-creation. It does so by examining and articulating those needs against the backdrop of available research findings, translating the needs into specific research projects that should be pursued, and providing guidelines for how those projects can be carried out

Big data and the SP theory of intelligence

This article is about how the "SP theory of intelligence" and its realisation in the "SP machine" may, with advantage, be applied to the management and analysis of big data. The SP system -- introduced in the article and fully described elsewhere -- may help to overcome the problem of variety in big data: it has potential as "a universal framework for the representation and processing of diverse kinds of knowledge" (UFK), helping to reduce the diversity of formalisms and formats for knowledge and the different ways in which they are processed. It has strengths in the unsupervised learning or discovery of structure in data, in pattern recognition, in the parsing and production of natural language, in several kinds of reasoning, and more. It lends itself to the analysis of streaming data, helping to overcome the problem of velocity in big data. Central in the workings of the system is lossless compression of information: making big data smaller and reducing problems of storage and management. There is potential for substantial economies in the transmission of data, for big cuts in the use of energy in computing, for faster processing, and for smaller and lighter computers. The system provides a handle on the problem of veracity in big data, with potential to assist in the management of errors and uncertainties in data. It lends itself to the visualisation of knowledge structures and inferential processes. A high-parallel, open-source version of the SP machine would provide a means for researchers everywhere to explore what can be done with the system and to create new versions of it.Comment: Accepted for publication in IEEE Acces

The Digitalisation of African Agriculture Report 2018-2019

An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains

Using visual analytics to develop situation awareness in astrophysics

We present a novel collaborative visual analytics application for cognitively overloaded users in the astrophysics domain. The system was developed for scientists who need to analyze heterogeneous, complex data under time pressure, and make predictions and time-critical decisions rapidly and correctly under a constant influx of changing data. The Sunfall Data Taking system utilizes several novel visualization and analysis techniques to enable a team of geographically distributed domain specialists to effectively and remotely maneuver a custom-built instrument under challenging operational conditions. Sunfall Data Taking has been in production use for 2 years by a major international astrophysics collaboration (the largest data volume supernova search currently in operation), and has substantially improved the operational efficiency of its users. We describe the system design process by an interdisciplinary team, the system architecture and the results of an informal usability evaluation of the production system by domain experts in the context of Endsley's three levels of situation awareness

Problems and Promises of Using LMS Learner Analytics for Assessment: Case Study of a First-Year English Program

Learning management systems (LMS) are widely used in education. They offer the potential for assessing student learning, but the reality of using them for this is problematic. This case study chronicles efforts by librarians at Marquette University to use LMS data to assess students’ information literacy knowledge in Marquette’s first-year English program