The authors developed and meta-analytically examined hypotheses designed to test and extend work design theory by integrating motivational, social, and work context characteristics. Results from a summary of 259 studies and 219,625 participants showed that 14 work characteristics explained, on average, 43% of the variance in the 19 worker attitudes and behaviors examined. For example, motivatio...

The module theorem by Janhunen et al. demonstrates how to provide a modular structure in answer set programming, where each module has a well-defined input/output interface which can be used to establish the compositionality of answer sets. The theorem is useful in the analysis of answer set programs, and is a basis of incremental grounding and reactive answer set programming. We extend the mod...

After committing an offense, transgressors can optimize their chances of reconciling with the victim by accepting responsibility. However, transgressors may be motivated to avoid admitting fault because it can feel threatening to accept blame for harmful behavior. Who, then, is likely to accept responsibility for a transgression? We examined how implicit theories of personality— whether people ...

Surveying results from [5] and [6], we motivate and introduce the theory behind formalizing rich interfaces for software and hardware components. Rich interfaces specify the protocol aspects of component interaction. Their formalization, called interface automata, permits a compiler to check the compatibility of component interaction protocols. Interface automata support incremental design and ...

This paper addresses the problem of mitigating the order effects in incremental learning, a phenomenon observed when different ordered sequences of observations lead to different results. A modification of an ILP incremental learning system, with the aim of making it order-independent, is presented. A backtracking strategy on theories is incorporated in its refinement operators, which causes a ...

At present most privacy preserving algorithms based on l-diversity model are limited only to static data release. It is low efficiency and vulnerable to inference attack if these anonymous algorithms are directly applied to dynamic data publishing. To address this issue, this paper analyzes various inference channels that possibly exist between multiple anonymized datasets and discusses how to ...

In this paper, based on the nonlocal Timoshenko beam theory, a nonlinear model is presented for the vibrational behavior of carbon nanotubes (CNTs) embedded in elastic medium in thermal environment. Using the Timoshenko beam theory and nonlocal elasticity of Eringen, the influences of rotary inertia, transverse shear deformation and small scale effect are taken into account. To model the intera...

Simplified Fuzzy Adaptive Resonance Theory Map (SFAM) is a family of neural networks that performs incremental supervised learning of recognition categories and multidimensional maps of both binary and analog patterns. SFAM is fast, interactive, incremental and stable and it is being applied to prediction in many areas. Medical diagnoses present many challenges in classifying patients based on ...

We show how Finite Model Computation (FMC) of first-order theories can efficiently and transparently be solved by taking advantage of an extension of Answer Set Programming, called incremental Answer Set Programming (iASP). The idea is to use the incremental parameter in iASP programs to account for the domain size of a model. The FMC problem is then successively addressed for increasing domain...

Incremental on-line learning is an important branch of machine learning. One class of approaches particularly well-suited to such tasks are Adaptive Resonance Theory (ART) neural networks. This paper presents a novel ART network combining the ability of noise-insensitive, incremental clustering and topology learning at different levels of detail from TopoART with Euclidean similarity measures a...