Abstract
Information Systems projects are often complex enterprises, since they involve the adoption of information technologies in organizational contexts. As such, they should be carefully managed considering the various aspects that influence their success. This paper presents a literature review focused on the success factors of information technology and information systems projects.

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Abstract
We present a method for improving the prediction accuracy using multiple predictive algorithms. Several techniques have been developed to tackle this issue such as bagging, boosting and stacking. In contrary to the first two that, usually, generate homogeneous ensembles of classifiers, stacking techniques have demonstrated success using heterogeneous ensembles. In our method, we adopt the stacking mechanism. Several models are generated using different learning algorithms. Forward stepwise selection is implemented to link each instance to its appropriate learning model. Experiments with three datasets benchmarked with four learning schemes show that this novel method improves prediction accuracy and can serve as a bridge to transfer knowledge between tasks given the same feature space but different data distributions. © 2018, Springer Nature Switzerland AG.

Abstract
Considering the avalanche of evolving data and the memory constraints, streaming networks’ sampling has gained much attention in the recent decade. However, samples choosing data uniformly from the beginning to the end of a temporal stream are not very relevant for temporally evolving networks where recent activities are more important than the old events. Moreover, the relationships also change overtime. Recent interactions are evident to show the current status of relationships, nevertheless some old stronger relations are also substantially significant. Considering the above issues we propose a fast memory less dynamic sampling mechanism for weighted or multi-graph high-speed streams. For this purpose, we use a forgetting function with two parameters that help introduce biases on the network based on time and relationship strengths. Our experiments on real-world data sets show that our samples not only preserve the basic properties like degree distributions but also maintain the temporal distribution correlations. We also observe that our method generates samples with increased efficiency. It also outperforms current sampling algorithms in the area. © 2019, Springer Nature Switzerland AG.

Abstract
This special issue collects selected articles from the Third International Workshop on Linearity (LINEARITY 2014), which was held in Vienna, on July 13th, 2014. The workshop was a one-day satellite event of FLoC 2014, the sixth Federated Logic Conference, which was held as part of the 2014 Vienna Summer of Logic. Copyright © Cambridge University Press 2016

Abstract
The most broadly perceived relative directions are right, left, up, down, backward and forward. This research paper presents a new computational technique to learn human's relative directions, where one intelligent computer can learn any human's right, left, up, down, backward and forward or different relative directions. The present paper portrays models describing the essential structures of relative direction learning process between human and intelligent machine. We developed two proficient algorithms for solving this approach. In our experiment we propose Human Relative Direction Learning (HRDL) algorithm for learning human's relative directions and Human Direction Identification (HDI) algorithm for tracking any human position and identity human's relative directions from different direction points.