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.     

Quadrature ���� Modulators for Cognitive Radio��I/Q Imbalance Analysis and Complex Multiband Principle

This article discusses the applicability of quadrature ΣΔ modulator (QΣΔM) based analog-to-digital (A/D) conversion in cognitive radio (CR) receivers. First, unavoidable in-phase/quadrature (I/Q) mismatch effects, limiting the dynamic range, are analyzed in closed-form in the case of a first-order modulator. In addition, using the derived analytical converter model, it is shown that notching the signal transfer function (STF) of the modulator at the mirror frequencies of the desired signals will effectively cancel the I/Q imbalance induced mirror-frequency interference in case of the modulator feedback mismatch. In practice, such STF design is easy to implement within the existing converter circuitry, as will be demonstrated in this article. The latter part of the article proposes a novel complex multiband QΣΔM scheme, particularly aimed for the CR receivers. This multiband scheme allows parallel reception of scattered frequency chunks in the CR context and is stemming from the additional degrees of freedom in noise transfer function (NTF) design, provided by the QΣΔM principle. Here multiple noise shaping notches on distinct frequencies are effectively realized through proper design of complex NTF. The modulator structure also allows flexible reconfigurability of the notches with straightforward parameterization of the modulator transfer functions. When combined with the above mirror-frequency rejecting STF design, the concept is demonstrated and proved effective and robust against I/Q imbalances using practical radio signal simulations in realistic received signal conditions.     

AlphaFold2: A Role for Disordered Protein/Region Prediction?

The development of AlphaFold2 marked a paradigm-shift in the structural biology community. Herein, we assess the ability of AlphaFold2 to predict disordered regions against traditional sequence-based disorder predictors. We find that AlphaFold2 performs well at discriminating disordered regions, but also note that the disorder predictor one constructs from an AlphaFold2 structure determines accuracy. In particular, a naïve, but non-trivial assumption that residues assigned to helices, strands, and H-bond stabilized turns are likely ordered and all other residues are disordered results in a dramatic overestimation in disorder; conversely, the predicted local distance difference test (pLDDT) provides an excellent measure of residue-wise disorder. Furthermore, by employing molecular dynamics (MD) simulations, we note an interesting relationship between the pLDDT and secondary structure, that may explain our observations and suggests a broader application of the pLDDT for characterizing the local dynamics of intrinsically disordered proteins and regions (IDPs/IDRs).