When Emotions Rule Knowledge

Although emotions play an important role in human behavior and knowledge studies, knowledge management (KM) research considers them from specific angles and, to date, has lacked a comprehensive understanding of the emotions dominating KM. To offer a holistic view, this study investigates the presence of emotions in KM publications by applying a sentiment analysis. The authors present a sentiment dictionary tailored to KM, apply it to KM publications to determine where and how emotions occur, and categorize them on an emotion scale. The considerable amount of positive and negative emotions expressed in KM studies prove their relevance to and dominance in KM. There is high term diversity but also a need to consolidate terms and emotion categories in KM. This study's results provide new insights into the relevance of emotions in KM research, while practitioners can use this method to detect emotion-laden language and successfully implement KM initiatives.

Accounting for the Parameters and the Problem with the High-High Term

This chapter provides a list of all the parameters and summarizes their current status while also discussing the problem with the High–High term. These include the parameters used to mollify the Reynolds stress in space and along the coarse scale flow, the parameter used to mollify the velocity field in space, the lifespan parameter, and the parameter used to make sure the phase functions are high frequency. In addition to the High–High term, the chapter considers the Transport term, which becomes large when a short lifespan is chosen. It also shows how the Transport term could be made to have the size required for regularity up to 1/3.

Constructing Continuous Solutions

This chapter demonstrates how the preceding construction, combined with a few estimates from Part V, can be used to prove the Main Lemma for continuous solutions. The first step is to mollify the velocity, followed by mollification of the stress. The lifespan is then chosen, preferring a small parameter to ensure that the first term in the parametrix for the High–High term is controlled. The chapter proceeds by discussing the bounds for the new stress and solving the divergence equation, along with the bounds for the corrections and finally, control of the energy increment. The equation for the energy increment includes a smooth vector field and involves bounding the error term.

Where Are All The Bugs?

In a large code and complex code base, it becomes unfeasible to manually develop tests for every feature and combination of features. The key to quality assurance in this context is automation and focus. Automatic generation of tests creates its own problems, however, as the execution of a complete cross-product of all interactions will take too long to execute, and small defects can give rise to large numbers of regression failures that must be manually analyzed. Manually identifying the interactions is itself a challenging undertaking, as is automatically generating meaningful test cases. It becomes important to make smart choices about what to expend effort on so as to minimize the risk of undetected code defects. This paper reports on an attempt to find areas to focus testing on in a large XQuery code base by performing XQuery introspection on that code base, treating the set of functions and parameter and return types as vocabularies, and computing TF-IDF scores over the terms in those vocabularies. To the extent that function names and types follow classic Zipf distributions, using TF-IDF scoring over those vocabularies makes mathematical sense. Terms that score high will be those that are common enough to be important (high term frequency) but not so ubiquitous that they tend not to be covered by other tests (high inverse document frequency).

Quadratic algebras with few relations

Throughout V will be a finite dimensional vector space over a field k and T(V) will denote the tensor algebra over V. For simplicity the symbol ⊗ will be omitted in the writing of the elements of T(V). Let be a basis of V ordered by Xi<Xi+1 for all i. Then we order the non-commutative monomials and 1 ≤ is ≤ n for s = 1,…, l} lexicographically from the left. D. Anick [1, p. 652] defines the high term of an element b in T(V) to be the highest monomial appearing in b. As a consequence of [1,3.2], if the set of the high terms of homogeneous relations is combinatorically free in the sense of no overlap ambiguities, then the connected algebra has global dimension 2. The purpose of this note is to prove this result and more for quadratic algebras under other hypotheses on the relations.