Factors impacting knowledge transfer success in information systems outsourcing

Despite increased research interest on knowledge transfer in information systems (IS) outsourcing, the field still lacks sound and holistic understanding of the key factors influencing knowledge transfer success. The present paper attempts to provide a synthesis of existing theoretical perspectives and empirical findings related to the factors that facilitate or hamper knowledge transfer success in IS outsourcing. The data collection method is discussed and the key findings are presented. Conclusion is drawn and further research is suggested

Deep Projective 3D Semantic Segmentation

Semantic segmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionized the field of image semantic segmentation, its impact on point cloud data has been limited so far. Recent attempts, based on 3D deep learning approaches (3D-CNNs), have achieved below-expected results. Such methods require voxelizations of the underlying point cloud data, leading to decreased spatial resolution and increased memory consumption. Additionally, 3D-CNNs greatly suffer from the limited availability of annotated datasets. In this paper, we propose an alternative framework that avoids the limitations of 3D-CNNs. Instead of directly solving the problem in 3D, we first project the point cloud onto a set of synthetic 2D-images. These images are then used as input to a 2D-CNN, designed for semantic segmentation. Finally, the obtained prediction scores are re-projected to the point cloud to obtain the segmentation results. We further investigate the impact of multiple modalities, such as color, depth and surface normals, in a multi-stream network architecture. Experiments are performed on the recent Semantic3D dataset. Our approach sets a new state-of-the-art by achieving a relative gain of 7.9 %, compared to the previous best approach.Comment: Submitted to CAIP 201

Bergmann-Thomson energy-momentum complex for solutions more general than the Kerr-Schild class

In a very well-known paper, Virbhadra's research group proved that the Weinberg, Papapetrou, Landau and Lifshitz, and Einstein energy-momentum complexes ``coincide'' for all metrics of Kerr-Schild class. A few years later, Virbhadra clarified that this ``coincidence'' in fact holds for metrics more general than the Kerr-Schild class. In the present paper, this study is extended for the Bergmann-Thomson complex and it is proved that this complex also ``coincides'' with those complexes for a more general than the Kerr-Schild class metric.Comment: RevTex, 12 page

3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration

In this paper, we propose the 3DFeat-Net which learns both 3D feature detector and descriptor for point cloud matching using weak supervision. Unlike many existing works, we do not require manual annotation of matching point clusters. Instead, we leverage on alignment and attention mechanisms to learn feature correspondences from GPS/INS tagged 3D point clouds without explicitly specifying them. We create training and benchmark outdoor Lidar datasets, and experiments show that 3DFeat-Net obtains state-of-the-art performance on these gravity-aligned datasets.Comment: 17 pages, 6 figures. Accepted in ECCV 201

Teleparallel Energy-Momentum Distribution of Static Axially Symmetric Spacetimes

This paper is devoted to discuss the energy-momentum for static axially symmetric spacetimes in the framework of teleparallel theory of gravity. For this purpose, we use the teleparallel versions of Einstein, Landau-Lifshitz, Bergmann and M$\ddot{o}$ller prescriptions. A comparison of the results shows that the energy density is different but the momentum turns out to be constant in each prescription. This is exactly similar to the results available in literature using the framework of General Relativity. It is mentioned here that M$\ddot{o}$ller energy-momentum distribution is independent of the coupling constant $\lambda$. Finally, we calculate energy-momentum distribution for the Curzon metric, a special case of the above mentioned spacetime.Comment: 14 pages, accepted for publication in Mod. Phys. Lett.

Energy and Momentum Distributions of Kantowski and Sachs Space-time

We use the Einstein, Bergmann-Thomson, Landau-Lifshitz and Papapetrou energy-momentum complexes to calculate the energy and momentum distributions of Kantowski and Sachs space-time. We show that the Einstein and Bergmann-Thomson definitions furnish a consistent result for the energy distribution, but the definition of Landau-Lifshitz do not agree with them. We show that a signature switch should affect about everything including energy distribution in the case of Einstein and Papapetrou prescriptions but not in Bergmann-Thomson and Landau-Lifshitz prescriptions.Comment: 12 page

Boosting Object Recognition in Point Clouds by Saliency Detection

Object recognition in 3D point clouds is a challenging task, mainly when time is an important factor to deal with, such as in industrial applications. Local descriptors are an amenable choice whenever the 6 DoF pose of recognized objects should also be estimated. However, the pipeline for this kind of descriptors is highly time-consuming. In this work, we propose an update to the traditional pipeline, by adding a preliminary filtering stage referred to as saliency boost. We perform tests on a standard object recognition benchmark by considering four keypoint detectors and four local descriptors, in order to compare time and recognition performance between the traditional pipeline and the boosted one. Results on time show that the boosted pipeline could turn out up to 5 times faster, with the recognition rate improving in most of the cases and exhibiting only a slight decrease in the others. These results suggest that the boosted pipeline can speed-up processing time substantially with limited impacts or even benefits in recognition accuracy.Comment: International Conference on Image Analysis and Processing (ICIAP) 201

Mobile wallet inhibitors: Developing a comprehensive theory using an integrated model

© 2018 Elsevier Ltd The concept of the mobile wallet is increasingly adopted in developed and developing countries for improving the scale, productivity, and excellence of banking services. Oman is one of the most growing countries of the Middle Eastern economies. Acceptance of mobile wallets in Oman is being hindered by various inhibitors. There is no study in the Middle Eastern countries that addressed the concerns of probable inhibitors influencing mobile wallet acceptance from expert's perspective. In this study, eleven key inhibitors to mobile wallet adoption are identified from the literature and expert's feedback. This study employed Interpretive Structural Modelling (ISM) in conjunction with fuzzy MICMAC to reveal the intricate relationship among inhibitors to mobile wallet acceptance. To the end, an integrated hierarchical model is developed to understand the influence of a particular inhibitor on others. ‘Anxiety towards new technology’ ‘Lack of new technology skills’ ‘Lack of awareness of mobile wallet benefits’ and ‘Complexity of new technology’ have been reported as key inhibitors to promote mobile wallets in Oman. This study also suggests several recommendations for banking organizations and policymakers in developing the effective model to popularize mobile wallets in Oman

Teleparallel Energy-Momentum Distribution of Spatially Homogeneous Rotating Spacetimes

The energy-momentum distribution of spatially homogeneous rotating spacetimes in the context of teleparallel theory of gravity is investigated. For this purpose, we use the teleparallel version of Moller prescription. It is found that the components of energy-momentum density are finite and well-defined but are different from General Relativity. However, the energy-momentum density components become the same in both theories under certain assumptions. We also analyse these quantities for some special solutions of the spatially homogeneous rotating spacetimes.Comment: 12 pages, accepted for publication in Int. J. Theor. Phy

Compactifying the state space for alternative theories of gravity

In this paper we address important issues surrounding the choice of variables when performing a dynamical systems analysis of alternative theories of gravity. We discuss the advantages and disadvantages of compactifying the state space, and illustrate this using two examples. We first show how to define a compact state space for the class of LRS Bianchi type I models in $R^n$-gravity and compare to a non--compact expansion--normalised approach. In the second example we consider the flat Friedmann matter subspace of the previous example, and compare the compact analysis to studies where non-compact non--expansion--normalised variables were used. In both examples we comment on the existence of bouncing or recollapsing orbits as well as the existence of static models.Comment: 18 pages, revised to match published versio