K-means: Clustering by gradual data transformation

Traditional approach to clustering is to fit a model (partition or prototypes) for the given data. We propose a completely opposite approach by fitting the data into a given clustering model that is optimal for similar pathological data of equal size and dimensions. We then perform inverse transform from this pathological data back to the original data while refining the optimal clustering structure during the process. The key idea is that we do not need to find optimal global allocation of the prototypes. Instead, we only need to perform local fine-tuning of the clustering prototypes during the transformation in order to preserve the already optimal clustering structure.     

The movement patterns and the experiential components of virtual environments

Human movement in virtual environments (VEs) is a largely unstudied area, and there are no well-established methods of measuring it in VEs. Consequently, it is unclear how movement affects the experiential side of VEs. We introduce a novel method of measuring and modelling human movement. A specific information entropy-based modelling method enabled us to identify different movement patterns and analyse the experiential components related to them. The data was collected by registering the movement patterns of 68 participants who were in a virtual house doing a search task. The experiential side of the VE was measured with the Experimental Virtual Environment Questionnaire (EVEQ). Four movement patterns were identified. In addition, fluent movement in VEs was related to a high sense of presence. Moreover, the participants who moved fluently in the environment assessed their skills high. The results show how movement is related the way in which people experience the VE. The movement analysis method introduced here is applicable to other related research areas as well.     

What levels of moral reasoning and values explain adherence to information security rules? An empirical study

It is widely agreed that employee non-adherence to information security policies poses a major problem for organizations. Previous research has pointed to the potential of theories of moral reasoning to better understand this problem. However, we find no empirical studies that examine the influence of moral reasoning on compliance with information security policies. We address this research gap by proposing a theoretical model that explains non-compliance in terms of moral reasoning and values. The model integrates two well-known psychological theories: the Theory of Cognitive Moral Development by Kohlberg and the Theory of Motivational Types of Values by Schwartz. Our empirical findings largely support the proposed model and suggest implications for practice and research on how to improve information security policy compliance.     

Limitations on post-processing assisted quantum programming

A quantum multimeter is a programmable device that can implement measurements of different observables depending on the programming quantum state inserted into it. The advantage of this arrangement over a single-purpose device is in its versatility: one can realize various measurements simply by changing the programming state. The classical manipulation of measurement output data is known as post-processing. In this work we study the post-processing assisted quantum programming, which is a protocol where quantum programming and classical post-processing are combined. We provide examples showing that these two processes combined can be more efficient than either of them used separately. Furthermore, we derive an inequality relating the programming resources to their corresponding programmed observables, thereby enabling us to study the limitations on post-processing assisted quantum programming.     

Identifying software project risks in Nigeria: an International Comparative Study

We report a study where software project risk factors were solicited in Nigeria. The study repeats a research design, which has been used in a Delphi study in the U.S., Finland, and Hong Kong to rank software development risks. The study produces a rank-order list of software risk factors and compares results with the earlier study, showing significant differences. Obtained rankings signal the importance of the infrastructure related and socio-economic software risks in developing countries like Nigeria. This demonstrates the importance of understanding the broader socio-economic context in identifying and managing software risks. The study also shows that some risks in Nigeria are located on a different scale. The choice of contingent mitigation strategies of project managers play a less important role, because the risks are so deeply engrained with the poor quality of basic infrastructure services.     

Weight initialization with reference patterns

The problem of weight initialization in multilayer perceptron networks is considered. A new computationally simple weight initialization method based on the usage of reference patterns is presented. A reference pattern is a vector which is used to represent data points that fall in its vicinity in the data space. On one hand, the proposed method aims to set the initial weight values to be such that inputs to network nodes are within the active region (in other words, nodes are not saturated). On the other hand, the goal is to distribute the discriminant functions formed by the hidden units evenly into the input space area where training data is located. The proposed method is tested with the widely used two-spirals classification benchmark problem and channel equalization problem where several alternatives for obtaining suitable reference patterns are investigated. Also, the effect of the initialization is studied when two commonly used cost functions are used in the training phase. These are the mean square error and relative entropy cost functions. A comparison with the conventional random initialization shows that significant improvement in convergence can be achieved with the proposed method. In addition, the computational cost of the initialization was found to be negligible compared with the cost of training.     

Strategies for software-based hybrid business models

The open source approach to software development has been used by software organizations in tandem with their existing business models, which are based on proprietary software licensing. This led to the creation of hybrid business models that merge open source and proprietary paradigms. This paper explores the practices used by software product vendors using hybrid business models and proposes strategies emerging out of these practices using interpretive, single case study research design.