A contingency model of perceived effectiveness in accounting information systems: Organizational coordination and control effects

A contingency model is advanced that examines sources of requirements for organizational coordination and control as they affect the extent of integration in an accounting information system. Requirements that are contingent on the degree of organizational formalization, information interdependence among functional areas, and dependence in interorganizational information sharing and electronic data interchange links, are examined. The congruence or fit of system integration with those requirements is a key concept that influences beliefs about system effectiveness. Results of the empirical study indicated that, as hypothesized, the fit between the accounting system design and the contingency factors resulted in a more successful system. Specifically, system fit was a significant factor that explained variations in perceived AIS effectiveness, as measured by decision makers’ perceived satisfaction with the accuracy and monitoring effectiveness of output information. The effect of system fit on a second factor of perceived AIS effectiveness, as measured by decision-makers’ satisfaction with the perceived quality of information content in system outputs, was only marginally significant. The study addresses an important area in accounting systems research that directly relates to the decision facilitation and control objectives of accounting information. © 2000 Elsevier Science Inc. All rights reserved

Concentration of personal and household crimes in England and Wales

Crime is disproportionally concentrated in few areas. Though long-established, there remains uncertainty about the reasons for variation in the concentration of similar crime (repeats) or different crime (multiples). Wholly neglected have been composite crimes when more than one crime types coincide as parts of a single event. The research reported here disentangles area crime concentration into repeats, multiple and composite crimes. The results are based on estimated bivariate zero-inflated Poisson regression models with covariance structure which explicitly account for crime rarity and crime concentration. The implications of the results for criminological theorizing and as a possible basis for more equitable police funding are discussed

An Examination of Familiarity, Risk and Trust in Inter- Organizational Data Exchange Relationships

This study examines the impact of trading partner familiarity on two types of trusting-beliefs (goodwill-trust andcompetence-trust) and two types of perceived risk (relational-risk and performance-risk) in interorganizational data exchangerelationships. A questionnaire and experimental simulation are utilized to provide empirical evidence supporting the study’spropositions. Results show that familiarity has a positive influence on both competence-trust and goodwill-trust and anegative influence on both performance-risk and relational-risk. This study contributes to a further understanding of theprocesses by which familiarity may influence interorganizational relationships and presents findings that have implicationsfor future research in this area

Dynamic probabilistic linear discriminant analysis for video classification

Component Analysis (CA) comprises of statistical techniques that decompose signals into appropriate latent components, relevant to a task-at-hand (e.g., clustering, segmentation, classification). Recently, an explosion of research in CA has been witnessed, with several novel probabilistic models proposed (e.g., Probabilistic Principal CA, Probabilistic Linear Discriminant Analysis (PLDA), Probabilistic Canonical Correlation Analysis). PLDA is a popular generative probabilistic CA method, that incorporates knowledge regarding class-labels and furthermore introduces class-specific and sample-specific latent spaces. While PLDA has been shown to outperform several state-of-the-art methods, it is nevertheless a static model; any feature-level temporal dependencies that arise in the data are ignored. As has been repeatedly shown, appropriate modelling of temporal dynamics is crucial for the analysis of temporal data (e.g., videos). In this light, we propose the first, to the best of our knowledge, probabilistic LDA formulation that models dynamics, the so-called Dynamic-PLDA (DPLDA). DPLDA is a generative model suitable for video classification and is able to jointly model the label information (e.g., face identity, consistent over videos of the same subject), as well as dynamic variations of each individual video. Experiments on video classification tasks such as face and facial expression recognition show the efficacy of the proposed metho

ERP Systems Implementation And Firm Performance

The recent wave of enterprise resource planning systems adoption represents a significant commitment of resources and has a dramatic effect on all business processes.  These systems influence a firm’s performance over a long-term time horizon.  This study examines the effect of adoption of ERP on a firm’s long-term operational performance.  Financial data of companies adopting ERP systems and of a matched control group of firms were compared before and after adoption.  The results from an analysis of performance differences across time periods has shown that firms adopting ERP systems have exhibited a significantly higher differential performance during the two years following the completion of the system than the control group of firms.  These results provide important insights about ERP implementation

Micro-CernVM: Slashing the Cost of Building and Deploying Virtual Machines

The traditional virtual machine building and and deployment process is centered around the virtual machine hard disk image. The packages comprising the VM operating system are carefully selected, hard disk images are built for a variety of different hypervisors, and images have to be distributed and decompressed in order to instantiate a virtual machine. Within the HEP community, the CernVM File System has been established in order to decouple the distribution from the experiment software from the building and distribution of the VM hard disk images. We show how to get rid of such pre-built hard disk images altogether. Due to the high requirements on POSIX compliance imposed by HEP application software, CernVM-FS can also be used to host and boot a Linux operating system. This allows the use of a tiny bootable CD image that comprises only a Linux kernel while the rest of the operating system is provided on demand by CernVM-FS. This approach speeds up the initial instantiation time and reduces virtual machine image sizes by an order of magnitude. Furthermore, security updates can be distributed instantaneously through CernVM-FS. By leveraging the fact that CernVM-FS is a versioning file system, a historic analysis environment can be easily re-spawned by selecting the corresponding CernVM-FS file system snapshot.Comment: Conference paper at the 2013 Computing in High Energy Physics (CHEP) Conference, Amsterda

Facial affect "in the wild": a survey and a new database

Well-established databases and benchmarks have been developed in the past 20 years for automatic facial behaviour analysis. Nevertheless, for some important problems regarding analysis of facial behaviour, such as (a) estimation of affect in a continuous dimensional space (e.g., valence and arousal) in videos displaying spontaneous facial behaviour and (b) detection of the activated facial muscles (i.e., facial action unit detection), to the best of our knowledge, well-established in-the-wild databases and benchmarks do not exist. That is, the majority of the publicly available corpora for the above tasks contain samples that have been captured in controlled recording conditions and/or captured under a very specific milieu. Arguably, in order to make further progress in automatic understanding of facial behaviour, datasets that have been captured in in the-wild and in various milieus have to be developed. In this paper, we survey the progress that has been recently made on understanding facial behaviour in-the-wild, the datasets that have been developed so far and the methodologies that have been developed, paying particular attention to deep learning techniques for the task. Finally, we make a significant step further and propose a new comprehensive benchmark for training methodologies, as well as assessing the performance of facial affect/behaviour analysis/ understanding in-the-wild. To the best of our knowledge, this is the first time that such a benchmark for valence and arousal "in-the-wild" is presente

Recognition of affect in the wild using deep neural networks

In this paper we utilize the first large-scale "in-the-wild" (Aff-Wild) database, which is annotated in terms of the valence-arousal dimensions, to train and test an end-to-end deep neural architecture for the estimation of continuous emotion dimensions based on visual cues. The proposed architecture is based on jointly training convolutional (CNN) and recurrent neural network (RNN) layers, thus exploiting both the invariant properties of convolutional features, while also modelling temporal dynamics that arise in human behaviour via the recurrent layers. Various pre-trained networks are used as starting structures which are subsequently appropriately fine-tuned to the Aff-Wild database. Obtained results show premise for the utilization of deep architectures for the visual analysis of human behaviour in terms of continuous emotion dimensions and analysis of different types of affect