IUScholarWorks, Statistics, and Altmetrics

This talk will focus on new developments regarding statistics and altmetrics in the IUScholarWorks institutional repository. It will cover the technology and policies behind a recently added statistics module, which displays filtered data regarding views and downloads for all items in the repository. Additionally, we will discuss an experimental new feature, the integration of alternative metrics ("altmetrics"; which track social media mentions of scholarship) into the repository display

Dissertations in IUScholarWorks

IUScholarWorks is a set of services whose stated goal is to make the work of IU scholars freely available. One of our recent efforts towards this goal has been to ingest all IUB student dissertations into the repository and make them freely available. Our talk will focus on the recent efforts of the IUScholarWorks team to do this. There were several challenges that the team faced in making this possible. These included issues of copyright, locating authors to gain permission, the challenge of converting the metadata into an appropriate format, and creating a ingestion workflow that would be as automated as possible. We will discuss the creation of an automated drop box processor that allows the dissertations to be ingested automatically, and a new embargo feature, which allows dissertations to be hidden until permission to display them is granted. We will conclude with a discussion of what is left to be done on this project, and ways in which the service can be improved in the future

Implementing an altmetrics reporting service into DSpace using Altmetric.com

The IUScholarWorks Repository is a DSpace-based institutional repository for the dissemination and preservation of Indiana University's scholarly output. As in many institutional repositories, the statistics tracked for repository content have historically been concerned with a) the number and size of items held in our repository, and b) the page views and downloads for those item records (Wacha & Wisner, 2011). To date, these statistics have mostly been used for internal reference. With the rise of researcher awareness of alternative metrics (commonly called “altmetrics”) that track the usage and sharing of scholarly outputs on the social web (Priem, Taraborelli, Groth, & Neylon, 2010), the IUScholarWorks team has been interested in the idea of implementing altmetrics as a value-added service for depositors. We have partnered with Altmetric.com to test deployment of the service in two phases described below, with eventual roll-out to our production repository

Self-repairing mobile robotic car using astrocyte-neuron networks

A self-repairing robot utilising a spiking astrocyte-neuron network is presented in this paper. It uses the output spike frequency of neurons to control the motor speed and robot activation. A software model of the astrocyte-neuron network previously demonstrated self-detection of faults and its self-repairing capability. In this paper the application demonstrator of mobile robotics is employed to evaluate the fault-tolerant capabilities of the astrocyte-neuron network when implemented in a hardware-based robotic car system. Results demonstrated that when 20% or less synapses associated with a neuron are faulty, the robot car can maintain system performance and complete the task of forward motion correctly. If 80% synapses are faulty, the system performance shows a marginal degradation, however this degradation is much smaller than that of conventional fault-tolerant techniques under the same levels of faults. This is the first time that astrocyte cells merged within spiking neurons demonstrates a self-repairing capabilities in the hardware system for a real application

Assessing Self-Repair on FPGAs with Biologically Realistic Astrocyte-Neuron Networks

This paper presents a hardware based implementation of a biologically-faithful astrocyte-based selfrepairing mechanism for Spiking Neural Networks. Spiking Astrocyte-neuron Networks (SANNs) are a new computing paradigm which capture the key mechanisms of how the human brain performs repairs. Using SANN in hardware affords the potential for realizing computing architecture that can self-repair. This paper demonstrates that Spiking Astrocyte Neural Network (SANN) in hardware have a resilience to significant levels of faults. The key novelty of the paper resides in implementing an SANN on FPGAs using fixed-point representation and demonstrating graceful performance degradation to different levels of injected faults via its self-repair capability. A fixed-point implementation of astrocyte, neurons and tripartite synapses are presented and compared against previous hardware floating-point and Matlab software implementations of SANN. All results are obtained from the SANN FPGA implementation and show how the reduced fixedpoint representation can maintain the biologically-realistic repair capability

Homeostatic Fault Tolerance in Spiking Neural Networks : A Dynamic Hardware Perspective

Fault tolerance is a remarkable feature of biological systems and their self-repair capability influence modern electronic systems. In this paper, we propose a novel plastic neural network model, which establishes homeostasis in a spiking neural network. Combined with this plasticity and the inspiration from inhibitory interneurons, we develop a fault-resilient robotic controller implemented on an FPGA establishing obstacle avoidance task. We demonstrate the proposed methodology on a spiking neural network implemented on Xilinx Artix-7 FPGA. The system is able to maintain stable firing (tolerance ±10%) with a loss of up to 75% of the original synaptic inputs to a neuron. Our repair mechanism has minimal hardware overhead with a tuning circuit (repair unit) which consumes only three slices/neuron for implementing a threshold voltage-based homeostatic fault-tolerant unit. The overall architecture has a minimal impact on power consumption and, therefore, supports scalable implementations. This paper opens a novel way of implementing the behavior of natural fault tolerant system in hardware establishing homeostatic self-repair behavior

Conversational Grammar- Feminine Grammar? A Sociopragmatic Corpus Study

One area in language and gender research that has so far received only little attention is the extent to which the sexes make use of what recent corpus research has termed “conversational grammar.” The author’s initial findings have suggested that the majority of features distinctive of conversational grammar may be used predominantly by female speakers. This article reports on a study designed to test the hypothesis that conversational grammar is “feminine grammar” in the sense that women’s conversational language is more adapted to the conversational situation than men’s. Based on data from the conversational subcorpus of the British National Corpus and following the situational framework for the description of conversational features elaborated in the author’s previous research, features distinctive of conversational grammar are grouped into five functional categories and their normed frequencies compared across the sexes. The functional categories distinguish features that can be seen as adaptations to constraints set by the situational factors of (1) Shared Context, (2) Co-Construction, (3) Real-Time Processing, (4) Discourse Management, and (5) Relation Management. The study’s results, described in detail in relation to the biological category of speaker sex and cultural notions of gender, suggest that the feminine grammar hypothesis is valid