Effect of Step Size in Incremental Loading Tests

Using the crack-growth model, the deviation of real fracture stress from the stressing level measured by stepwise incremental loading with three starting modes is calculated. For both static and dynamic fatigue, the relative error depends only on the relative increment size and the crack-growth parameter, n. The usefulness of stepwise incremental loading for strength testing can be confirmed.     

Automated bank cheque verification using image processing and deep learning methods

Multimedia Tools and Applications - Automated bank cheque verification using image processing is an attempt to complement the present cheque truncation system, as well as to provide an alternate...     

Giving too much social support: social overload on social networking sites

As the number of messages and social relationships embedded in social networking sites (SNS) increases, the amount of social information demanding a reaction from individuals increases as well. We observe that, as a consequence, SNS users feel they are giving too much social support to other SNS users. Drawing on social support theory (SST), we call this negative association with SNS usage ‘social overload’ and develop a latent variable to measure it. We then identify the theoretical antecedents and consequences of social overload and evaluate the social overload model empirically using interviews with 12 and a survey of 571 Facebook users. The results show that extent of usage, number of friends, subjective social support norms, and type of relationship (online-only vs offline friends) are factors that directly contribute to social overload while age has only an indirect effect. The psychological and behavioral consequences of social overload include feelings of SNS exhaustion by users, low levels of user satisfaction, and a high intention to reduce or even stop using SNS. The resulting theoretical implications for SST and SNS acceptance research are discussed and practical implications for organizations, SNS providers, and SNS users are drawn.     

Product fit uncertainty and its effects on vendor choice: an experimental study

Recommender systems and other Internet-enabled technologies have changed the surrounding conditions of pre-purchase evaluations on the Internet. In some cases consumers can now sample entire products prior to a purchase – hereby removing all uncertainty about whether a product fits their taste. While previous research has mainly focused on vendor and product quality uncertainty, it is still not clear how declining product fit uncertainty affects consumers. To close this gap, we conducted a laboratory experiment to analyze the effects on consumers’ vendor selection. We find that full elimination of product fit uncertainty is beneficial for vendors, as it increases both the number of purchases and consumer loyalty. Interestingly, if product fit uncertainty is only partially eliminated, consumers do not necessarily show differential behavior for different levels of remaining product fit uncertainty. This has important implications for online vendors that consider the implementation of additional means to reduce product fit uncertainty.     

Approximate variational inference based on a finite sample of Gaussian latent variables

Variational methods are employed in situations where exact Bayesian inference becomes intractable due to the difficulty in performing certain integrals. Typically, variational methods postulate a tractable posterior and formulate a lower bound on the desired integral to be approximated, e.g. marginal likelihood. The lower bound is then optimised with respect to its free parameters, the so-called variational parameters. However, this is not always possible as for certain integrals it is very challenging (or tedious) to come up with a suitable lower bound. Here, we propose a simple scheme that overcomes some of the awkward cases where the usual variational treatment becomes difficult. The scheme relies on a rewriting of the lower bound on the model log-likelihood. We demonstrate the proposed scheme on a number of synthetic and real examples, as well as on a real geophysical model for which the standard variational approaches are inapplicable.     

Discriminating Conduct: Deploying Systems to Support Awareness in Organizations

Alongside the emergence of the use of fieldwork studies for design there has been a discussion on how best these studies can inform system development. Concerns have been expressed as to whether their most appropriate contribution is a list of requirements or design recommendations. This article explores a recurrent issue that has emerged from fieldwork studies in Computer-Supported Cooperative Work, awareness, and with respect to a particular system development project discusses some of the implications for the development and deployment of one particular kind of technology—image recognition systems—in particular, organizational settings. In the setting in question—surveillance centers or operations rooms—staff utilize a range of practices to maintain awareness. Rather than extending field studies so that they can better assist design, it may be considered how workplace studies can contribute to a respecification of key concepts, like awareness, that are critical to an understanding of how technologies are used and deployed in everyday environments.     

Efficiently adapting graphical models for selectivity estimation

Query optimizers rely on statistical models that succinctly describe the underlying data. Models are used to derive cardinality estimates for intermediate relations, which in turn guide the optimizer to choose the best query execution plan. The quality of the resulting plan is highly dependent on the accuracy of the statistical model that represents the data. It is well known that small errors in the model estimates propagate exponentially through joins, and may result in the choice of a highly sub-optimal query execution plan. Most commercial query optimizers make the attribute value independence assumption: all attributes are assumed to be statistically independent. This reduces the statistical model of the data to a collection of one-dimensional synopses (typically in the form of histograms), and it permits the optimizer to estimate the selectivity of a predicate conjunction as the product of the selectivities of the constituent predicates. However, this independence assumption is more often than not wrong, and is considered to be the most common cause of sub-optimal query execution plans chosen by modern query optimizers. We take a step towards a principled and practical approach to performing cardinality estimation without making the independence assumption. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution over all the attributes in the database into small, usually two-dimensional distributions, without a significant loss in estimation accuracy. We show how to efficiently construct such a graphical model from the database using only two-way join queries, and we show how to perform selectivity estimation in a highly efficient manner. We integrate our algorithms into the PostgreSQL DBMS. Experimental results indicate that estimation errors can be greatly reduced, leading to orders of magnitude more efficient query execution plans in many cases. Optimization time is kept in the range of tens of milliseconds, making this a practical approach for industrial-strength query optimizers.     

Adoption and use of Java generics

Support for generic programming was added to the Java language in 2004, representing perhaps the most significant change to one of the most widely used programming languages today. Researchers and language designers anticipated this addition would relieve many long-standing problems plaguing developers, but surprisingly, no one has yet measured how generics have been adopted and used in practice. In this paper, we report on the first empirical investigation into how Java generics have been integrated into open source software by automatically mining the history of 40 popular open source Java programs, traversing more than 650 million lines of code in the process. We evaluate five hypotheses and research questions about how Java developers use generics. For example, our results suggest that generics sometimes reduce the number of type casts and that generics are usually adopted by a single champion in a project, rather than all committers. We also offer insights into why some features may be adopted sooner and others features may be held back.