NEVER CHANGE A RUNNING SYSTEM? HOW STATUS QUO-THINKING CAN INHIBIT SOFTWARE AS A SERVICE ADOPTION IN ORGANIZATIONS

Despite the “buzz” about Software as a Service (SaaS), decision makers still often refrain from replacing their existing in-house technologies with innovative IT services. Industry reports indicate that the skeptical attitude of decision makers stems primarily from a high degree of uncertainty that exists, for example, due to insufficient experience with the new technology, a lack of best practice approaches, and missing lighthouse projects. Whereas previous research is predominantly focused on the advantages of SaaS, behavioral economics conclusively demonstrate that reference points like the evaluation of the incumbent technology or a familiar product are oftentimes prevalent when decisions are made under uncertainty. In this context, Status Quo-Thinking may inhibit decisions in favor of potentially advantageous IT service innovations. Drawing on Prospect Theory and Status Quo Bias re-search, we derive and empirically test a research model that explicates the influence of the incumbent technology on the evaluation of SaaS. Based on a large-scale empirical study, we demonstrate that the decision makers’ attitude toward SaaS is highly dependent on their current systems and their level of SaaS. A lack of SaaS experience will increase the impact of the Status Quo, thus inhibiting a potential advantageous adoption of the new technology

Optimizing expected word error rate via sampling for speech recognition

State-level minimum Bayes risk (sMBR) training has become the de facto standard for sequence-level training of speech recognition acoustic models. It has an elegant formulation using the expectation semiring, and gives large improvements in word error rate (WER) over models trained solely using cross-entropy (CE) or connectionist temporal classification (CTC). sMBR training optimizes the expected number of frames at which the reference and hypothesized acoustic states differ. It may be preferable to optimize the expected WER, but WER does not interact well with the expectation semiring, and previous approaches based on computing expected WER exactly involve expanding the lattices used during training. In this paper we show how to perform optimization of the expected WER by sampling paths from the lattices used during conventional sMBR training. The gradient of the expected WER is itself an expectation, and so may be approximated using Monte Carlo sampling. We show experimentally that optimizing WER during acoustic model training gives 5% relative improvement in WER over a well-tuned sMBR baseline on a 2-channel query recognition task (Google Home)

Users' trust in information resources in the Web environment: a status report

This study has three aims; to provide an overview of the ways in which trust is either assessed or asserted in relation to the use and provision of resources in the Web environment for research and learning; to assess what solutions might be worth further investigation and whether establishing ways to assert trust in academic information resources could assist the development of information literacy; to help increase understanding of how perceptions of trust influence the behaviour of information users

Robust audio indexing for Dutch spoken-word collections

Abstract—Whereas the growth of storage capacity is in accordance with widely acknowledged predictions, the possibilities to index and access the archives created is lagging behind. This is especially the case in the oral history domain and much of the rich content in these collections runs the risk to remain inaccessible for lack of robust search technologies. This paper addresses the history and development of robust audio indexing technology for searching Dutch spoken-word collections and compares Dutch audio indexing in the well-studied broadcast news domain with an oral-history case-study. It is concluded that despite significant advances in Dutch audio indexing technology and demonstrated applicability in several domains, further research is indispensable for successful automatic disclosure of spoken-word collections

A reusable iterative optimization software library to solve combinatorial problems with approximate reasoning

Real world combinatorial optimization problems such as scheduling are typically too complex to solve with exact methods. Additionally, the problems often have to observe vaguely specified constraints of different importance, the available data may be uncertain, and compromises between antagonistic criteria may be necessary. We present a combination of approximate reasoning based constraints and iterative optimization based heuristics that help to model and solve such problems in a framework of C++ software libraries called StarFLIP++. While initially developed to schedule continuous caster units in steel plants, we present in this paper results from reusing the library components in a shift scheduling system for the workforce of an industrial production plant.Comment: 33 pages, 9 figures; for a project overview see http://www.dbai.tuwien.ac.at/proj/StarFLIP

Piloting an Empirical Study on Measures for Workflow Similarity

Service discovery of state dependent services has to take workflow aspects into account. To increase the usability of a service discovery, the result list of services should be ordered with regard to the relevance of the services. Means of ordering a list of workflows due to their similarity with regard to a query are missing. This paper presents a pilot of an empirical study on the influence of different measures on workflow similarity. It turns out that, although preliminary, relations between different measures are indicated and that a similarity definition depends on the application scenario in which the service discovery is applied

HiggsBounds: Confronting Arbitrary Higgs Sectors with Exclusion Bounds from LEP and the Tevatron

HiggsBounds is a computer code that tests theoretical predictions of models with arbitrary Higgs sectors against the exclusion bounds obtained from the Higgs searches at LEP and the Tevatron. The included experimental information comprises exclusion bounds at 95% C.L. on topological cross sections. In order to determine which search topology has the highest exclusion power, the program also includes, for each topology, information from the experiments on the expected exclusion bound, which would have been observed in case of a pure background distribution. Using the predictions of the desired model provided by the user as input, HiggsBounds determines the most sensitive channel and tests whether the considered parameter point is excluded at the 95% C.L. HiggsBounds is available as a Fortran 77 and Fortran 90 code. The code can be invoked as a command line version, a subroutine version and an online version. Examples of exclusion bounds obtained with HiggsBounds are discussed for the Standard Model, for a model with a fourth generation of quarks and leptons and for the Minimal Supersymmetric Standard Model with and without CP-violation. The experimental information on the exclusion bounds currently implemented in HiggsBounds will be updated as new results from the Higgs searches become available.Comment: 64 pages, 15 tables, 8 figures; three typos which made it to the published version corrected; the code (currently version 3.0.0beta including LHC Higgs search results) is available via: http://projects.hepforge.org/higgsbounds