Challenging Computer Software Frontiers and the Human Resistance to Change

This paper examines the driving and opposing forces that are governing the current paradigm shift from a data-processing information technology environment without software intelligence to an information-centric environment in which data changes are automatically interpreted withinthe context of the application domain. The driving forces are related to the large quantity of dataand the complexity of networked systems that both call for software intelligence. The opposing forces are non-technical and due to the natural human resistance to change. Based on this background the paper describes current information-centric technology, proposes avision of intelligent software system capabilities, and identifies four areas of necessary research.Most urgent among these are the ability to dynamically extend and merge ontologies and semantic search capabilities that can be initiated either by human users or software agents.Longer term research interests that pose a more severe challenge are related to the translation of emerging theoretical hierarchical temporal memory (HTM) concepts into usable software capabilities and the automated interpretation of graphical images such as those recorded bysurveillance video cameras

Identifying critical success factors of ERP systems at the higher education sector

In response to a range of contextual drivers, the worldwide adoption of ERP Systems in Higher Education Institutions (HEIs) has increased substantially over the past decade. Though the difficulties and high failure rate in implementing ERP systems at university environments have been cited in the literature, research on critical success factors (CSFs) for ERP implementations in this context is rare and fragmented. This paper is part of a larger research effort that aims to contribute to understanding the phenomenon of ERP implementations and evaluations in HEIs in the Australasian region; it identifies, previously reported, critical success factors (CSFs) in relation to ERP system implementations and discusses the importance of these factors

Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Spiking neural networks (SNN) are artificial computational models that have been inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the conventional artificial neural networks, though their full computational capabilities are yet to be explored. Recently, computational memory architectures based on non-volatile memory crossbar arrays have shown great promise to implement parallel computations in artificial and spiking neural networks. In this work, we experimentally demonstrate for the first time, the feasibility to realize high-performance event-driven in-situ supervised learning systems using nanoscale and stochastic phase-change synapses. Our SNN is trained to recognize audio signals of alphabets encoded using spikes in the time domain and to generate spike trains at precise time instances to represent the pixel intensities of their corresponding images. Moreover, with a statistical model capturing the experimental behavior of the devices, we investigate architectural and systems-level solutions for improving the training and inference performance of our computational memory-based system. Combining the computational potential of supervised SNNs with the parallel compute power of computational memory, the work paves the way for next-generation of efficient brain-inspired systems

Predicting the effectiveness of hepatitis C virus neutralizing antibodies by bioinformatic analysis of conserved epitope residues using public sequence data

Hepatitis C virus (HCV) is a global health issue. Although direct-acting antivirals are available to target HCV, there is currently no vaccine. The diversity of the virus is a major obstacle to HCV vaccine development. One approach toward a vaccine is to utilize a strategy to elicit broadly neutralizing antibodies (bNAbs) that target highly-conserved epitopes. The conserved epitopes of bNAbs have been mapped almost exclusively to the E2 glycoprotein. In this study, we have used HCV-GLUE, a bioinformatics resource for HCV sequence data, to investigate the major epitopes targeted by well-characterized bNAbs. Here, we analyze the level of conservation of each epitope by genotype and subtype and consider the most promising bNAbs identified to date for further study as potential vaccine leads. For the most conserved epitopes, we also identify the most prevalent sequence variants in the circulating HCV population. We examine the distribution of E2 sequence data from across the globe and highlight regions with no coverage. Genotype 1 is the most prevalent genotype worldwide, but in many regions, it is not the dominant genotype. We find that the sequence conservation data is very encouraging; several bNAbs have a high level of conservation across all genotypes suggesting that it may be unnecessary to tailor vaccines according to the geographical distribution of genotypes

The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge

Reinventing Media Activism: Public Interest Advocacy in the Making of U.S. Communication-Information Policy, 1960-2002

This report is a long-term analysis of citizens' collective action to influence public policy toward communication and information. The work discusses in greater detail what is meant by communication and information policy (CIP) and why we think it is worthwhile to study it as a distinctive domain of public policy and citizen action. The report concentrates on citizen action in the United States and looks backwards, tracing the long-term evolutionary trajectory of communications-information advocacy in the USA since the 1960s. We focus on the concept of citizen collective action and explain its relevance to CIP.Research supported by the Ford Foundation's Knowledge, Creativity and Freedom Program. The views expressed are those of the author and do not necessarily represent the views of the School of Information Studies, Syracuse University, or the Ford Foundation