Advances In Information Science Research Articles

Enhancing performance ofelectron holography with mathematical andmachine learning-based denoising techniques.

Electron holography is a useful tool for analyzing functional properties, such as electromagnetic fields and strains of materials and devices. The performance of electron holography is limited by the 'shot noise' inherent in electron micrographs (holograms), which are composed of a finite number of electrons. A promising approach for addressing this issue is to use mathematical and machine learning-based image-processing techniques for hologram denoising. With the advancement of information science, denoising methods have become capable of extracting signals that are completely buried in noise, and they are being applied to electron microscopy, including electron holography. However, these advanced denoising methods are complex and have many parameters to be tuned; therefore, it is necessary to understand their principles in depth and use them carefully. Herein, we present an overview of the principles and usage of sparse coding, the wavelet hidden Markov model and tensor decomposition, which have been applied to electron holography. We also present evaluation results for the denoising performance of these methods obtained through their application to simulated and experimentally recorded holograms. Our analysis, review and comparison of the methods clarify the impact of denoising on electron holography research.

Social work education for the digital age: insight from information science

ABSTRACT This article presents an information science perspective on Social Work (SW) education based on the results of a comparative policy content evaluation (PCE) of accreditation documents in Canada and the U.S. It investigates to what extent information and technology education (ITE) is integral to the accreditation documents that guide the SW curriculum in both countries. Empirical results are presented in the context of current advances in information science, technology, and artificial intelligence affecting the practice of SW, social justice, and social change. With accreditation documents viewed as stage-setters for SW programs, it is proposed that technology- and information-driven change may not be reflected in the SW curriculum design in an adequate or timely fashion but is seen as a bonus, not essential knowledge. The article suggests some ways to revise the current accreditation documents and positions information science as a potential partner that can contribute to SW education.

Delineating the field of medical education: Bibliometric research approach(es).

BackgroundThe field of medical education remains poorly delineated such that there is no broad consensus of articles or journals that comprise ‘the field’. This lack of consensus indicates a missed opportunity for researchers to generate insights about the field that could facilitate conducting bibliometric studies and other research designs (e.g., systematic reviews) and also enable individuals to identify themselves as ‘medical education researchers’. Other fields have utilised bibliometric field delineation, which is the assigning of articles or journals to a certain field in an effort to define that field.ProcessIn this Research Approach, three bibliometric field delineation approaches—information retrieval, core journals, and journal co‐citation—are introduced. For each approach, the authors describe attempts to apply it in medical education and identify related strengths and weaknesses. Based on co‐citation, the authors propose the Medical Education Journal List 24 (MEJ‐24), as a starting point for delineating medical education and invite the community to collaborate on improving and potentially expanding this list.PearlsAs a research approach, field delineation is complicated, and there is no clear best way to delineate the field of medical education. However, recent advances in information science provide potentially fruitful approaches to deal with the field's complexity. When considering these approaches, researchers should consider collaborating with bibliometricians. Bibliometric approaches rely on available metadata for articles and journals, which necessitates that researchers examine the metadata prior to analysis to understand its strengths and weaknesses, and to assess how this might affect data interpretation. While using bibliometric approaches for field delineation is valuable, it is important to remember that these techniques are only as good as the research team's interpretation of the data, which suggests that an expanded approach is needed to better delineate medical education, an approach that includes active discussion within the medical education community.

Reconstructing Nonparametric Productivity Networks.

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra- and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown.

The Evolution of Researchers’ Bibliography: From Systematic Organization to Citation

Purpose/Thesis: This paper analyses the publications of two authors from different countries through a number of citations identified by Google Scholar. Jean Meyriat and Edson Nery da Fonseca are outstanding researchers in France and Brazil, respectively, both contributing to the formation and advancement of information science in their countries. The “traditional” bibliography and the development trajectory of their scientific achievements were considered in this analysis.Approach/Methods: The analysis of the researchers’ publications identified by Google Scholar based on their names helps to understand the role that search engines play in the evaluation of science and its effect on information seeking and in the evaluation of the scientific production. The qualitative research is based on a bibliological analyses focusing on the way in which the written production of the selected authors is reported and highlighted. The carried out study is exploratory in nature, so it proposes to raise questions and to emit hypotheses that could suggest directions for further research.Results and conclusions: The uniformity of controlled metadata used in traditional bibliographies make it possible to follow the progress of an author’s thought. The same is true of other activities he has performed in and out of the field and, it is also true for what has been produced about this author. Searching for information in Google Scholar can show a researcher’s production and publi­cations about her/him in the order of citations but not in the order of the progress of the researcher’s scientific achievements or development of research focused on these achievements. Placed in the context of the analysis of the scientific field as it was defined by Bourdieu (1976), the observation of the visibility of the production Jean Meyriat and Edson Nery da Fonseca, two major actors in information science in France and Brazil , by Google Scholar, shows a tendency to underemphasize the role they played.Practical implications: The functioning of the scientific field has specific effects on the production of research. The search engines such as Google Scholar, made the research output more accessible on Internet. However, the criteria they prioritize, i.e., the number of citations, ignores important aspects of a scientific career. Qualitative analysis that considers the networks of sociability and the development trajectory of researchers’ scientific achievements may reveal relevant contributions to their scientific field.

Online sequential pattern mining and association discovery by advanced artificial intelligence and machine learning techniques

With the advances in information science, vast amounts of financial time series data can been collected and analyzed. In modern time series analysis, sequential pattern mining (SPM) and association discovery (AD) are the most important techniques to predict the future trends. This study aims at developing advanced SPM and AD for financial data by cutting edge techniques from artificial intelligence and machine learning. The nonlinearity and non-stationarity of financial time series dynamics pose a major challenge for SPM and AD. This study employs time–frequency analysis to extract features for SPM. Then, a sparse multi-manifold clustering (SMMC) is used to partition the feature space into several disjointed regions for better AD. Finally, local relevance vector machines (RVMs) are employed for AD and perform the forecasting. Different from traditional methods, the novel forecasting system operates on multiple resolutions and multiple dynamic regimes. SMMC finds both the neighbors and the weights automatically by a sparse solution, which approximately spans a low-dimensional affine subspace at that point. RVM, the Bayesian kernel machines, can produce parsimonious models with excellent generalization properties. Taking multiple time series data from financial markets as an example, the empirical results demonstrate that the proposed model outperforms traditional models and significantly reduces the forecasting errors. The framework is effective and suitable for other time series forecasting.

Information Technology and the Arts: The Evolution of Computer Choreography during the Last Half Century

This article explores the history of the relations between computer science, information technology, and the art of dance. In the early years of computer choreography, scientists envisaged the development of visual displays and software tools to help in choreographic design. They used random number generators to create a variety of spatial displacements and body movements for the dancers—work that suggested that computer programs could be customized to suit the preferences of the individual choreographer. Such projections fell woefully short of what a large number of choreographers, computer scientists, digital artists, and professionals from different fields eventually achieved during the last half-century, not to mention what we might expect in the future. The convergence of dance creation and performance with advances in information science and technology constitutes a privileged ground on which to explore deep philosophical implications of our embodied mind.

Integrating novel data streams to support biosurveillance in commercial livestock production systems in developed countries: challenges and opportunities.

Reducing the burden of emerging and endemic infectious diseases on commercial livestock production systems will require the development of innovative technology platforms that enable information from diverse animal health resources to be collected, analyzed, and communicated in near real-time. In this paper, we review recent initiatives to leverage data routinely observed by farmers, production managers, veterinary practitioners, diagnostic laboratories, regulatory officials, and slaughterhouse inspectors for disease surveillance purposes. The most commonly identified challenges were (1) the lack of standardized systems for recording essential data elements within and between surveillance data streams, (2) the additional time required to collect data elements that are not routinely recorded by participants, (3) the concern over the sharing and use of business sensitive information with regulatory authorities and other data analysts, (4) the difficulty in developing sustainable incentives to maintain long-term program participation, and (5) the limitations in current methods for analyzing and reporting animal health information in a manner that facilitates actionable response. With the significant recent advances in information science, there are many opportunities to develop more sophisticated systems that meet national disease surveillance objectives, while still providing participants with valuable tools and feedback to manage routine animal health concerns.

Lattice anharmonicity and thermal conductivity from compressive sensing of first-principles calculations.

First-principles prediction of lattice thermal conductivity κ(L) of strongly anharmonic crystals is a long-standing challenge in solid-state physics. Making use of recent advances in information science, we propose a systematic and rigorous approach to this problem, compressive sensing lattice dynamics. Compressive sensing is used to select the physically important terms in the lattice dynamics model and determine their values in one shot. Nonintuitively, high accuracy is achieved when the model is trained on first-principles forces in quasirandom atomic configurations. The method is demonstrated for Si, NaCl, and Cu(12)Sb(4)S(13), an earth-abundant thermoelectric with strong phonon-phonon interactions that limit the room-temperature κ(L) to values near the amorphous limit.

Multi-scale clustering by building a robust and self correcting ultrametric topology on data points.

The advent of high-throughput technologies and the concurrent advances in information sciences have led to an explosion in size and complexity of the data sets collected in biological sciences. The biggest challenge today is to assimilate this wealth of information into a conceptual framework that will help us decipher biological functions. A large and complex collection of data, usually called a data cloud, naturally embeds multi-scale characteristics and features, generically termed geometry. Understanding this geometry is the foundation for extracting knowledge from data. We have developed a new methodology, called data cloud geometry-tree (DCG-tree), to resolve this challenge. This new procedure has two main features that are keys to its success. Firstly, it derives from the empirical similarity measurements a hierarchy of clustering configurations that captures the geometric structure of the data. This hierarchy is then transformed into an ultrametric space, which is then represented via an ultrametric tree or a Parisi matrix. Secondly, it has a built-in mechanism for self-correcting clustering membership across different tree levels. We have compared the trees generated with this new algorithm to equivalent trees derived with the standard Hierarchical Clustering method on simulated as well as real data clouds from fMRI brain connectivity studies, cancer genomics, giraffe social networks, and Lewis Carroll's Doublets network. In each of these cases, we have shown that the DCG trees are more robust and less sensitive to measurement errors, and that they provide a better quantification of the multi-scale geometric structures of the data. As such, DCG-tree is an effective tool for analyzing complex biological data sets.

Integration of sucrose accumulation processes across hierarchical scales: towards developing an understanding of the gene-to-crop continuum

Although we have a working knowledge of the general biology of sugarcane and are fairly efficient in crop production, we are only beginning to produce the detailed biochemical and genetic information that will be needed to develop technologies necessary for long-term success of the sugarcane industry. A true understanding of sucrose accumulation will be achieved only through a combination of experimental and computational analyses of comprehensive datasets to gain information on the underlying molecules and processes. Currently, systems-level approaches towards understanding biology are gaining momentum primarily because of the tremendous increases in computing power, advances in information sciences and rapid progress being made in molecular biology to facilitate data management, analyses and modeling from high-throughput genome sequencing and proteomics. Large-scale sequencing projects have not only provided complete sequence information for a number of genomes, but they are also producing integrated pathway-genome databases and models that provide organism-specific connectivity maps of metabolic and, to a lesser extent, other cellular networks. To date, the processes that generate mass, energy, information transfer and cell-fate specification have been analyzed only at the cell or microorganism levels, where they are shown to be seamlessly integrated through a complex network of cellular constituents and reactions. Despite the key role of these networks in sustaining cellular functions, their large-scale structure is essentially unknown. Fifty years of sugarcane research have identified and characterized a suite of physiological processes and enzymes involved in sucrose accumulation. More recently, genes encoding these enzymes have been isolated, cloned and used in experiments to transform sugarcane to increase or decrease expression of the enzyme with the goal of altering sucrose accumulation. However, results of this reductionist approach towards understanding sucrose accumulation have fallen short of expectations, apparently because of the complex interactions among the multitude of simultaneous processes. Recent rapid expansion of sugarcane molecular datasets and the beginning of a systems approach to metabolic modeling of sucrose accumulation point the way for future research efforts to integrate processes from gene to crop performance.

The role of SIG/CON in the advancement of information science

Historique et role joue par le SIG/CON (Coterminous Operation of neo-Nodes) dans l'evolution des sciences de l'information

The role of SIG/CON in the advancement of information science

The role of SIG/CON in the advancement of information science

Computer Literacy for School Pupils: The Psychological and Pedagogical Problems

The incursion of mathematics into spheres of human intellectual and practical activity that do not traditionally fall within its purview—an incursion which has been made possible by the development of computer hardware and software and by rapid advances in information science—is a characteristic trait of the scientific and technological revolution. The modern applications of mathematics (not just as a material cause to use computers but as an important element in software engineering) have given general research methods a foothold in virtually all fields of contemporary social production. Mankind is now more aware than ever before that knowledge does not become precise until it is rendered as a mathematical model. The mathematization process, which is a kind of interdisciplinary interaction, has experienced some substantial change in recent decades. Not only has there been an abrupt widening of the area wherein mathematical methods are being applied; there has also been a radical revision of attitudes toward the role mathematics plays in scientific and technological progress. Mathematics is no longer a mere tool that is used to perform quantitative calculations. It is literally the be-all and end-all of development in the exact sciences. Mathematization could not, however, have attained its contemporary status as a revolutionizing force if mathematical methods had continued to predominate in the stream of data flowing into that bridging discipline of [information?] science.

Medical library and information services

Summary The medical libraries in the Postgraduate Medical Education Centres which are now being built in many regions are generally of a simple pattern based on traditional library practices. In view of the recent advances in information science it is suggested that a more radical approach should be made to the design of medical library and information centres. The basic requirements for medical information in relation to education and medical practice are discussed together with recommendations for a regional and national network for the co-ordination of information-handling in the field of the health sciences.