Exploring subscription renewal intention of operational cloud enterprise systems - a stakeholder perspective

Retaining customers is a relevant topic throughout all service industries. However, only limited attention has been directed towards studying the antecedents of subscription renewal in the context of operational cloud enterprise systems. Cloud services have historically been offered as subscription-based services with the (theoretical) possibility of seamless service cancellation, in contrast to classical IT-Outsourcing contracts or license-based software installations of on-premise enterprise systems. In this work, we investigate the central concept of subscription renewal by focusing on different facets of IS success and their relevance for distinct employee cohorts. Analyzing inter-cohort differences has strong practical implications, as it helps IT vendors to focus on specific IT-related factors when trying to retain customers. Therefore an empirical study was undertaken. The hypotheses were developed on an individual level and tested using survey responses of IT decision makers within companies which adopted cloud enterprise systems. Gathered data was then analyzed using PLS. The results show that subscription renewal intention of the strategic cohort is mainly based on perceived system quality, whereas information quality explains most of the variance of subscription renewal in the management cohort. Beneath the cloud enterprise systems specific contributions, the work adds to the theoretical body of research related to IS success and IS continuation, as well as stakeholder perspectives.

A pattern based two-stage text classifier

In a classification problem typically we face two challenging issues, the diverse characteristic of negative documents and sometimes a lot of negative documents that are closed to positive documents. Therefore, it is hard for a single classifier to clearly classify incoming documents into classes. This paper proposes a novel gradual problem solving to create a two-stage classifier. The first stage identifies reliable negatives (negative documents with weak positive characteristics). It concentrates on minimizing the number of false negative documents (recall-oriented). We use Rocchio, an existing recall based classifier, for this stage. The second stage is a precision-oriented “fine tuning”, concentrates on minimizing the number of false positive documents by applying pattern (a statistical phrase) mining techniques. In this stage a pattern-based scoring is followed by threshold setting (thresholding). Experiment shows that our statistical phrase based two-stage classifier is promising.

Metabolite profiling and pathway analysis of acute hepatitis rats by UPLC-ESI MS combined with pattern recognition methods

BACKGROUND & AIMS Metabolomics is comprehensive analysis of low-molecular-weight endogenous metabolites in a biological sample. It could enable mapping of perturbations of early biochemical changes in diseases and hence provide an opportunity to develop predictive biomarkers that could provide valuable insights into the mechanisms of diseases. The aim of this study was to elucidate the changes in endogenous metabolites and to phenotype the metabolic profiling of d-galactosamine (GalN)-inducing acute hepatitis in rats by UPLC-ESI MS. METHODS The systemic biochemical actions of GalN administration (ip, 400 mg/kg) have been investigated in male wistar rats using conventional clinical chemistry, liver histopathology and metabolomic analysis of UPLC- ESI MS of urine. The urine was collected predose (-24 to 0 h) and 0-24, 24-48, 48-72, 72-96 h post-dose. Mass spectrometry of the urine was analysed visually and via conjunction with multivariate data analysis. RESULTS Results demonstrated that there was a time-dependent biochemical effect of GalN dosed on the levels of a range of low-molecular-weight metabolites in urine, which was correlated with developing phase of the GalN-inducing acute hepatitis. Urinary excretion of beta-hydroxybutanoic acid and citric acid was decreased following GalN dosing, whereas that of glycocholic acid, indole-3-acetic acid, sphinganine, n-acetyl-l-phenylalanine, cholic acid and creatinine excretion was increased, which suggests that several key metabolic pathways such as energy metabolism, lipid metabolism and amino acid metabolism were perturbed by GalN. CONCLUSION This metabolomic investigation demonstrates that this robust non-invasive tool offers insight into the metabolic states of diseases.

Accurate pattern prediction of very closely spaced mismatched antennas using measured active element patterns

A method for prediction of the radiation pattern of N strongly coupled antennas with mismatched sources is presented. The method facilitates fast and accurate design of compact arrays. The prediction is based on the measured N-port S parameters of the coupled antennas and the N active element patterns measured in a 50 ω environment. By introducing equivalent power sources, the radiation pattern with excitation by sources with arbitrary impedances and various decoupling and matching networks (DMN) can be accurately predicted without the need for additional measurements. Two experiments were carried out for verification: pattern prediction for parasitic antennas with different loads and for antennas with DMN. The difference between measured and predicted patterns was within 1 to 2 dB.

A novel representation of bioacoustic events for content-based search in field audio data

Bioacoustic data can provide an important base for environmental monitoring. To explore a large amount of field recordings collected, an automated similarity search algorithm is presented in this paper. A region of an audio defined by frequency and time bounds is provided by a user; the content of the region is used to construct a query. In the retrieving process, our algorithm will automatically scan through recordings to search for similar regions. In detail, we present a feature extraction approach based on the visual content of vocalisations – in this case ridges, and develop a generic regional representation of vocalisations for indexing. Our feature extraction method works best for bird vocalisations showing ridge characteristics. The regional representation method allows the content of an arbitrary region of a continuous recording to be described in a compressed format.

Daytime pattern of post-exercise protein intake affects whole-body protein turnover in resistance-trained males

Background The pattern of protein intake following exercise may impact whole-body protein turnover and net protein retention. We determined the effects of different protein feeding strategies on protein metabolism in resistance-trained young men. Methods: Participants were randomly assigned to ingest either 80g of whey protein as 8x10g every 1.5h (PULSE; n=8), 4x20g every 3h (intermediate, INT; n=7), or 2x40g every 6h (BOLUS; n=8) after an acute bout of bilateral knee extension exercise (4x10 repetitions at 80% maximal strength). Whole-body protein turnover (Q), synthesis (S), breakdown (B), and net balance (NB) were measured throughout 12h of recovery by a bolus ingestion of [ 15N]glycine with urinary [15N]ammonia enrichment as the collected end-product. Results PULSE Q rates were greater than BOLUS (?19%, P<0.05) with a trend towards being greater than INT (?9%, P=0.08). Rates of S were 32% and 19% greater and rates of B were 51% and 57% greater for PULSE as compared to INT and BOLUS, respectively (P<0.05), with no difference between INT and BOLUS. There were no statistical differences in NB between groups (P=0.23); however, magnitude-based inferential statistics revealed likely small (mean effect90%CI; 0.590.87) and moderate (0.800.91) increases in NB for PULSE and INT compared to BOLUS and possible small increase (0.421.00) for INT vs. PULSE. Conclusion We conclude that the pattern of ingested protein, and not only the total daily amount, can impact whole-body protein metabolism. Individuals aiming to maximize NB would likely benefit from repeated ingestion of moderate amounts of protein (?20g) at regular intervals (?3h) throughout the day.

Expression pattern of the cannabinoid receptor genes in the frontal cortex of mood disorder patients and mice selectively bred for high and low fear

Although the endocannabinoid system (ECS) has been implicated in brain development and various psychiatric disorders, precise mechanisms of the ECS on mood and anxiety disorders remain unclear. Here, we have investigated developmental and disease-related expression pattern of the cannabinoid receptor 1 (CB1) and the cannabinoid receptor 2 (CB2) genes in the dorsolateral prefrontal cortex (PFC) of humans. Using mice selectively bred for high and low fear, we further investigated potential association between fear memory and the cannabinoid receptor expression in the brain. The CB1, not the CB2, mRNA levels in the PFC gradually decrease during postnatal development ranging in age from birth to 50 years (r 2 > 0.6 & adj. p < 0.05). The CB1 levels in the PFC of major depression patients were higher when compared to the age-matched controls (adj. p < 0.05). In mice, the CB1, not the CB2, levels in the PFC were positively correlated with freezing behavior in classical fear conditioning (p < 0.05). These results suggest that the CB1 in the PFC may play a significant role in regulating mood and anxiety symptoms. Our study demonstrates the advantage of utilizing data from postmortem brain tissue and a mouse model of fear to enhance our understanding of the role of the cannabinoid receptors in mood and anxiety disorders

Pavlovian Fear Conditioning Activates a Common Pattern of Neurons in the Lateral Amygdala of Individual Brains

Understanding the physical encoding of a memory (the engram) is a fundamental question in neuroscience. Although it has been established that the lateral amygdala is a key site for encoding associative fear memory, it is currently unclear whether the spatial distribution of neurons encoding a given memory is random or stable. Here we used spatial principal components analysis to quantify the topography of activated neurons, in a select region of the lateral amygdala, from rat brains encoding a Pavlovian conditioned fear memory. Our results demonstrate a stable, spatially patterned organization of amygdala neurons are activated during the formation of a Pavlovian conditioned fear memory. We suggest that this stable neuronal assembly constitutes a spatial dimension of the engram. © 2011 This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.

Practical guidance for the use of a pattern-matching technique in case-study research : a case presentation

This paper reports on a study that demonstrates how to apply pattern matching as an analytical method in case-study research. Case-study design is appropriate for the investigation of highly-contextualized phenomena that occur within the social world. Case-study design is considered a pragmatic approach that permits employment of multiple methods and data sources in order to attain a rich understanding of the phenomenon under investigation. The findings from such multiple methods can be reconciled in case-study analysis, specifically through a pattern-matching technique. Although this technique is theoretically explained in the literature, there is scant guidance on how to apply the method practically when analyzing data. This paper demonstrates the steps taken during pattern matching in a completed case-study project that investigated the influence of cultural diversity in a multicultural nursing workforce on the quality and safety of patient care. The example highlighted in this paper contributes to the practical understanding of the pattern-matching process, and can also make a substantial contribution to case-study methods.

A novel method for finding similarities between unordered trees using matrix data model

Trees are capable of portraying the semi-structured data which is common in web domain. Finding similarities between trees is mandatory for several applications that deal with semi-structured data. Existing similarity methods examine a pair of trees by comparing through nodes and paths of two trees, and find the similarity between them. However, these methods provide unfavorable results for unordered tree data and result in yielding NP-hard or MAX-SNP hard complexity. In this paper, we present a novel method that encodes a tree with an optimal traversing approach first, and then, utilizes it to model the tree with its equivalent matrix representation for finding similarity between unordered trees efficiently. Empirical analysis shows that the proposed method is able to achieve high accuracy even on the large data sets.

Reconceptualizing Early Mathematics Learning : The Fundamental Role of Pattern and Structure

The Pattern and Structure Mathematics Awareness Program (PASMAP) was developed concurrently with the studies of AMPS and the development of the Pattern and Structure Assessment (PASA) interview. We summarize some early classroom-based teaching studies and describe the PASMAP that resulted. A large-scale two-year longitudinal study, Reconceptualizing Early Mathematics Learning (REML) resulted. We provide an overview of the REML study and discuss the consequences for our view of early mathematics learning. A purposive sample of four large primary schools, two in Sydney and two in Brisbane, representing 316 students from diverse socio-economic and cultural contexts, participated in an evaluation of the PASMAP intervention throughout the 2009 school year and a follow-up assessment in 2010. Two different mathematics programs were implemented: in each school, two Kindergarten teachers implemented the PASMAP and another two implemented their regular program. The study shows that both groups of students made substantial gains on the ‘I Can Do Maths’ standardized assessment and the PASA interview, but highly significant differences were found on the latter with PASMAP students outperforming the regular group on PASA scores. Qualitative analysis of students’ responses for structural development showed increased levels for the PASMAP students. Implications for pedagogy and curriculum are discussed.

Street computing : towards an integrated open data Application Programming Interface (API) for cities

This special issue of the Journal of Urban Technology brings together five articles that are based on presentations given at the Street Computing Workshop held on 24 November 2009 in Melbourne in conjunction with the Australian Computer- Human Interaction conference (OZCHI 2009). Our own article introduces the Street Computing vision and explores the potential, challenges, and foundations of this research trajectory. In order to do so, we first look at the currently available sources of information and discuss their link to existing research efforts. Section 2 then introduces the notion of Street Computing and our research approach in more detail. Section 3 looks beyond the core concept itself and summarizes related work in this field of interest. We conclude by introducing the papers that have been contributed to this special issue.

Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the a mission should be aborted due to mechanical or other failure. On-board cameras provide information that can be used in the determination of potential landing sites, which are continually updated and ranked to prevent injury and minimize damage. Pulse Coupled Neural Networks have been used for the detection of features in images that assist in the classification of vegetation and can be used to minimize damage to the aerial vehicle. However, a significant drawback in the use of PCNNs is that they are computationally expensive and have been more suited to off-line applications on conventional computing architectures. As heterogeneous computing architectures are becoming more common, an OpenCL implementation of a PCNN feature generator is presented and its performance is compared across OpenCL kernels designed for CPU, GPU and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images obtained during unmanned aerial vehicle trials to determine the plausibility for real-time feature detection.

Workflow resource pattern simulation and visualization

This thesis addresses the process simulation and validation in Business Process Management. It proposes that the hybrid Multi Agent System (MAS) / 3D Virtual World approach is a valid method for better simulating the behaviour of human resources in business processes, supporting a wide range of rich visualization applications that can facilitate communication between business analysts and stakeholders. It is expected that the findings of this thesis may be fruitfully extended from BPM to other application domains, such as social simulation in video games and computer-based training animations.

Quality based frame selection for face clustering in news video

Clustering identities in a broadcast video is a useful task to aid in video annotation and retrieval. Quality based frame selection is a crucial task in video face clustering, to both improve the clustering performance and reduce the computational cost. We present a frame work that selects the highest quality frames available in a video to cluster the face. This frame selection technique is based on low level and high level features (face symmetry, sharpness, contrast and brightness) to select the highest quality facial images available in a face sequence for clustering. We also consider the temporal distribution of the faces to ensure that selected faces are taken at times distributed throughout the sequence. Normalized feature scores are fused and frames with high quality scores are used in a Local Gabor Binary Pattern Histogram Sequence based face clustering system. We present a news video database to evaluate the clustering system performance. Experiments on the newly created news database show that the proposed method selects the best quality face images in the video sequence, resulting in improved clustering performance.