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Librarianship is both an art and a science. Librarians study the science of information and how to work with clients to help them find solutions to their information needs. They also learn quickly that there is an art to working with people, to finding the answers to tough questions using the resources available and knowing which information resources to use to find the information being sought in short order. But, what technical skills do librarians need to be successful in the future? How can library managers best develop their staffs for success? Developing Librarian Competencies for the Digital Age explores questions such as: What is the composition of a modern library collection? Will that collection look different in the future? What are the information sources and how do we manage those? What are the technical skills needed for a 21st century librarian? How will reference services change and adapt to embrace new ways to interact with library patrons or clients? What kinds of library skills are needed for the librarian of today to grow and thrive, now and into the future? How will service models change to existing clients and how will the model change going into the future of librarianship?What kinds of budgeting challenges are there for libraries and the administrators who oversee these libraries?What do the library professional organizations see as the core skills needed for new graduates and those practicing in the profession going into the future? In answering those questions, the book identifies specific digital skills needed for success, ways of developing those skills, and ways of assessing them.

Librarians are experts at selecting, evaluating, and disseminating the best content in the most appropriate format to meet the needs of library users. And, new technologies can be invaluable tools in these efforts. Taking advantage of these technologies does not, however, change the fundamental mission of libraries everywhere. In this thought-provoking work, one of the library world's leading thinkers discusses the transformative effect communications technology has had on information delivery from past to present to future. By tracing the transformations, Gorman writes a roadmap for achieving balance between the tradition of library service and ever-changing technology.

Tap into the insights of one of the world’s leading library visionaries. Stephen Abram has the knack for seeing and expressing the obvious long before most people become aware of the issue. Although he’s one of the most prolific writers and speakers in the industry, this is the first time his writings are being collected in one volume.

Libraries in the USA and globally are undergoing quiet revolution. Libraries are moving away from a philosophy that is collection-centered to one focused on service. Technology is key to that change. The Patron Driven Library explores the way technology has moved the focus from library collections to services, placing the reader at the center of library activities. The book reveals the way library users are changing, and how social networking, web delivery of information, and the uncertain landscape of e-print has energized librarians to adopt technology to meet a different model of the library while preserving core values. Following an introduction, the first part begins with the historical milieu, and moves on to current challenges for financing and acquiring materials, and an exploration of why the millennial generation is transformational. The second part examines how changes in library practice can create a culture for imagining library services in an age of information overflow. The final chapter asks: Whither the library? Provides a synthesis of current research on the impact of technology on behaviour, and connecting it with library servicesOffers examples and practical advice for incorporating technology to meet user expectations and assess servicesSuggests management techniques to overcome barriers to change and technology innovation

Deep learning can be used to forecast emerging technologies based on patent data. However, it requires a large amount of labeled patent data as a training set, which is difficult to obtain due to various constraints. This study proposes a novel approach that integrates data augmentation and deep learning methods, which overcome the problem of lacking training samples when applying deep learning to forecast emerging technologies. First, a sample data set was constructed using Gartner’s hype cycle and multiple patent features. Second, a generative adversarial network was used to generate many synthetic samples (data augmentation) to expand the scale of the sample data set. Finally, a deep neural network classifier was trained with the augmented data set to forecast emerging technologies, and it could predict up to 77% of the emerging technologies in a given year with high precision. This approach was used to forecast emerging technologies in Gartner’s hype cycles for 2017 based on patent data from 2000 to 2016. Four out of six of the emerging technologies were forecasted correctly, showing the accuracy and precision of the proposed approach. This approach enables deep learning to forecast emerging technologies with limited training samples.

Academic and public libraries are much different today than they were even 15 years ago. And with even bigger changes on the horizon, what lies in store? In this systematic attempt to speak to academic and public librarians about the future of library services, Hernon and Matthews invite a raft of contributors to step back and envision the type of future library that will generate excitement and enthusiasm among users and stakeholders .

Global warming skeptics often fall back on the argument that the scientific case for global warming is all model predictions, nothing but simulation; they warn us that we need to wait for real data, \"sound science.\" In A Vast Machine Paul Edwards has news for these doubters: without models, there are no data. Today, no collection of signals or observations--even from satellites, which can \"see\" the whole planet with a single instrument--becomes global in time and space without passing through a series of data models. Everything we know about the world's climate we know through models. Edwards offers an engaging and innovative history of how scientists learned to understand the atmosphere--to measure it, trace its past, and model its future. Edwards argues that all our knowledge about climate change comes from three kinds of computer models: simulation models of weather and climate; reanalysis models, which recreate climate history from historical weather data; and data models, used to combine and adjust measurements from many different sources. Meteorology creates knowledge through an infrastructure (weather stations and other data platforms) that covers the whole world, making global data. This infrastructure generates information so vast in quantity and so diverse in quality and form that it can be understood only by computer analysis--making data global. Edwards describes the science behind the scientific consensus on climate change, arguing that over the years data and models have converged to create a stable, reliable, and trustworthy basis for the reality of global warming.

This paper studies in some detail a class of high-frequency-based volatility (HEAVY) models. These models are direct models of daily asset return volatility based on realised measures constructed from high-frequency data. Our analysis identifies that the models have momentum and mean reversion effects, and that they adjust quickly to structural breaks in the level of the volatility process. We study how to estimate the models and how they perform through the credit crunch, comparing their fit to more traditional GARCH models. We analyse a model-based bootstrap which allows us to estimate the entire predictive distribution of returns. We also provide an analysis of missing data in the context of these models. Copyright © 2010 John Wiley & Sons, Ltd.

In this article, the association between the strength of information technology controls over management information systems and the subsequent forecasting ability of the information produced by those systems is investigated. The Sarbanes-Oxley Act of 2002 highlights the importance of information system controls by requiring management and auditors to report on the effectiveness of internal controls over the financial reporting component of the firm ' s management information systems. We hypothesize and find evidence that management forecasts are less accurate for firms with information technology material weaknesses in their financial reporting system than the forecasts for firms that do not have information technology material weaknesses. In addition, we examine three dimensions of information technology material weaknesses: data processing integrity, system access and security, and system structure and usage. We find that the association with forecast accuracy appears to be strongest for IT control weaknesses most directly related to data processing integrity. Our results support the contention that information technology controls, as apart of the management information system, affect the quality of the information produced by the system. We discuss the complementary nature of our findings to the information and systems quality literature.

The study aims to forecast the research output of four selected countries (USA, China, India and Pakistan) using two models of Grey System Theory—Even Model GM (1, 1) and Nonhomogeneous Discrete Grey Model (NDGM). The study also conducts publication growth analysis using relative growth rate (RGR) and the doubling time (Dt). The linear and exponential regression analyses were also performed for comparison. The study also proposes and successfully tests two novel synthetic models for RGR and Dt that facilities the comparison of the countries’ performance when actual data and forecasted data produce different sequences of performance in the given period of time. The data of documents published by the four countries from 2005 to 2016 was collected from SJR/Scopus website. Performance criterion was Mean Absolute Percentage Error. The study confirms that NDGM is a better model for forecasting research output as its accuracy level is higher than that of the Even Model GM (1, 1) and statistical regression models. The results revealed that USA is likely to continue leading in research output at least till 2025 however the research output difference between USA and China is likely to reduce. The study reveals that the less developed countries tend to possess higher relative growth rate in publications whereas the more developed countries tend to possess lower relative growth rate. Further, the more developed countries need more time for publications to double in numbers for a given relative growth rate and less developed countries need less time to do so. The study is original in term of its analysis of the problem using the models involved in the study. The study suggests that the strategies of USA and China to enhance the research output of their respective countries seem productive for the time being however in long run less developed countries have greater competitive advantage over the more developed countries because of their publication growth rate and time required to double the number of publications. The study reported nearly linear trend of growth in research output among the countries. The study is primarily important for the academic policy makers and encourages them to take corrective measures if the growth rate of their academic/publishing sector is not reasonable.