Ordinal Multidimensional Scaling Research Articles

E-privacy concerns: a facet theoretical approach

Although the ubiquitous use of smartphones and social media poses serious risks to the privacy of users, research is sparse regarding how users perceive these risks. We present a study investigating the perception of e-privacy risks, assuming that risk perception depends on context and situation, and employing a facet theory approach to define and analyze privacy risk perceptions. Specifically, we define three facets that characterize situations involving an e-privacy risk: Facet A refers to the type of data disclosed, distinguishing three types: a person’s identity information, information about health, and information about private activities. Facet B refers to the type of actor misusing the information, distinguishing between commercial organizations, public authorities, social networks, and criminal actors. Facet C distinguishes three kinds of harm that might be experienced as a consequence: financial loss, physical harm, and negative psycho-social experiences. Questionnaire items were constructed by creating fictitious but realistic scenarios, each representing a combination of one element from each facet, yielding 36 (3 × 4 × 3) scenarios. For each scenario, respondents rated the likelihood and the negativity of experiencing that scenario. Following the facet theoretical paradigm, item intercorrelations were analyzed via ordinal multidimensional scaling. Results from a representative survey among 500 adult Norwegians yield a distinct partitioning with respect to Facets A and B, called a radex configuration. Facet B (actors) shows an angular partition. Facet C (type of harm) yields a contrast of financial versus psycho-social harm. In sum, we conclude that our three-faceted definition provides a satisfying first approximation to people’s perception of privacy risks on the Internet while remaining open for extensions with additional facets.

An approach to exploring associations between hospital structural measures and patient satisfaction by distance\u2010based analysis

BackgroundPatient satisfaction studies have explored domains of patient satisfaction, the determinants of domains, and score differences of domains by patient/hospital structural measures but reports on the structure of patient satisfaction with respect to similarities among domains are scarce. This study is to explore by distance-based analysis whether similarities among patient-satisfaction domains are influenced by hospital structural measures, and to design a model evaluating relationships between the structure of patient satisfaction and hospital structural measures.MethodsThe Hospital Consumer Assessment of Healthcare Providers and Systems 2012 survey scores and their structural measures from the Hospital Compare website reported adjusted percentages of scale for each hospital. Contingency tables of nine measures and their ratings were designed based on hospital structural measures, followed by three different distance-based analyses - clustering, correspondence analysis, and ordinal multidimensional scaling – for robustness to identify homogenous groups with respect to similarities.ResultsOf 4,677 hospitals, 3,711 (79.3%) met the inclusion criteria and were analyzed. The measures were divided into three groups plus cleanliness. Certain combinations of these groups were shown to be dependent on hospital structural measures. High value ratings for communication and low value ratings for medication explanation, quietness and staff responsiveness were not influenced by hospital structural measures, but the varied-ratings domain group similarities, including items such as global evaluation and pain management, were affected by hospital structural measures.ConclusionsDistance-based analysis can reveal the hidden structure of patient satisfaction. This study suggests that hospital structural measures including hospital size, the ability to provide acute surgical treatment, and hospital interest in improving medical care quality are factors which may influence the structure of patient satisfaction.

Innovative Techniques for Attack Detection in Wireless Ad-Hoc Networks

As a result of the inherent weaknesses of the wireless medium, ad hoc networks are susceptible to a broad variety of threats and assaults. As a direct consequence of this, intrusion detection, as well as security, privacy, and authentication in ad-hoc networks, have developed into a primary focus of the current study. This body of research aims to identify the dangers posed by a variety of assaults that are often seen in wireless ad-hoc networks and provide strategies to counteract those dangers. The Black hole assault, Wormhole attack, Selective Forwarding attack, Sybil attack, and Denial-of-Service attack are the specific topics covered in this proposed work. In this paper, we describe a trust-based safe routing protocol with the goal of mitigating the interference of black hole nodes while routing in mobile ad-hoc networks. The overall performance of the network is negatively impacted when there are black hole nodes in the route that routing takes. As a result, we have developed a routing protocol that reduces the likelihood that packets would be lost because of black hole nodes. This routing system has been subjected to experimental testing to guarantee that the most secure path will be selected for the delivery of packets between a source and a destination. The invasion of wormholes into wireless networks results in the segmentation of the network as well as a disorder in the routing. As a result, we provide an effective approach for locating wormholes by using ordinal multi-dimensional scaling and round-trip duration in wireless ad hoc networks with either sparse or dense topologies. Wormholes that are linked by both short-route and long-path wormhole linkages may be found using the approach that was given. To guarantee that this ad hoc network does not include any wormholes that go unnoticed, this method is subjected to experimental testing. To fight against selective forwarding attacks in wireless ad-hoc networks, we have developed three different techniques. The first method is an incentive-based algorithm that makes use of a reward-punishment system to drive cooperation among three nodes for forwarding messages in crowded ad-hoc networks. A unique adversarial model has been developed by our team, and inside it, three distinct types of nodes and the activities they participate in are specified. We have demonstrated that the proposed method that is based on incentives prevents nodes from adopting individualistic behaviour, which ensures cooperation in the process of packet forwarding. In the second algorithm, a game theoretic model is proposed that uses non-cooperative game theory to ensure that intermediate nodes in resource-constrained ad-hoc networks faithfully forward packets.

Who benefits from HEIs engagement? An analysis of priority stakeholders and activity profiles of HEIs in the United Kingdom

ABSTRACTIt has been suggested that higher education institutions (HEIs) may develop different activity profiles (including research, teaching and socio-economic engagement) in their attempt to maximise the fit between institutional resources and strategic opportunities; the latter include strategies of engagement with different groups of external stakeholders. Understanding the extent to which HEIs’ resources and activity profiles are aligned with their strategic prioritisation of stakeholder groups allows us to better understand the different ways in which HEIs drive socio-economic development. Using non-parametric techniques – qualitative and quantitative ordinal multidimensional scaling – applied to data on the universe of HEIs in the United Kingdom, we show that HEIs with different institutional resources and undertaking different sets of activities prioritise their engagement with different stakeholder groups. We also confirm the complex associations between HEIs’ institutional resources, activity profiles and stakeholder prioritisation strategies, which lock HEIs into configurations that are difficult to change.

The Choice of Initial Configurations in Multidimensional Scaling: Local Minima, Fit, and Interpretability

Multidimensional scaling (MDS) algorithms can easily end up in local minima, depending on the starting configuration. This is particularly true for 2-dimensional ordinal MDS. A simulation study shows that there can be many local minima that all have an excellent model fit (i.e., small Stress) even if they do not recover a known latent configuration very well, and even if they differ substantially among each other. MDS programs give the user only one supposedly Stress-optimal solution. We here present a procedure for analyzing all MDS solutions resulting from using a variety of different starting configurations. The solutions are compared in terms of fit and configurational similarity. This allows the MDS user to identify different types of solutions with acceptable Stress, if they exist, and then pick the one that is best interpretable.

Wormhole Detection Based on Ordinal MDS Using RTT in Wireless Sensor Network

In wireless communication, wormhole attack is a crucial threat that deteriorates the normal functionality of the network. Invasion of wormholes destroys the network topology completely. However, most of the existing solutions require special hardware or synchronized clock or long processing time to defend against long path wormhole attacks. In this work, we propose a wormhole detection method using range-based topology comparison that exploits the local neighbourhood subgraph. The Round Trip Time (RTT) for each node pair is gathered to generate neighbour information. Then, the network is reconstructed by ordinal Multidimensional Scaling (MDS) followed by a suspicion phase that enlists the suspected wormholes based on the spatial reconstruction. Iterative computation of MDS helps to visualize the topology changes and can localize the potential wormholes. Finally, a verification phase is used to remove falsely accused nodes and identify real adversaries. The novelty of our algorithm is that it can detect both short path and long path wormhole links. Extensive simulations are executed to demonstrate the efficacy of our approach compared to existing ones.

Regional collaborations and indigenous innovation capabilities in China: A multivariate method for the analysis of regional innovation systems

In this study we analyse the emerging patterns of regional collaboration for innovation projects in China, using official government statistics of 30 Chinese regions. We propose the use of Ordinal Multidimensional Scaling and Cluster analysis as a robust method to study regional innovation systems. Our results show that regional collaborations amongst organisations can be categorised by means of eight dimensions: public versus private organisational mindset; public versus private resources; innovation capacity versus available infrastructures; innovation input (allocated resources) versus innovation output; knowledge production versus knowledge dissemination; and collaborative capacity versus collaboration output. Collaborations which are aimed to generate innovation fell into 4 categories, those related to highly specialised public research institutions, public universities, private firms and governmental intervention. By comparing the representative cases of regions in terms of these four innovation actors, we propose policy measures for improving regional innovation collaboration within China.

Evaluating the success of educational policy in Mexican Higher Education

State support in higher education (HE) is often justified on the grounds that HE is important for the development of the country. However, little analysis is normally done in order to assess the impact of education quality initiatives. The Mexican government has been engaging in a policy of HE quality improvement that can be traced to 1989. In this paper we use data from the period 2007–2010, when the various quality programmes were in action, in order to see if, on the basis of public information, it is possible to trace their impact on Federal, State, and Private Universities in Mexico. The technical approach is based on Multivariate Statistical Analysis, particularly in Ordinal Multidimensional Scaling. It was found that, during the period studied, the performance indicators used in this study show a general quality improvement in Mexican HE, and that this improvement has extended to both the State and the Private sectors. Whether this improvement is just in the data or it is real, we cannot say using published data.

How does Involvement in Decision Making Affect Individual Participation in Knowledge Management?

This paper highlights the need for an individual-centric approach for the decision making process of organisational knowledge management (KM) practices, to enhance KM effectiveness. Initial interviews with managers helped identify the key constructs in this research. Key constructs were then included in a questionnaire that was administered to employees of a medium sized company. The collected data were analysed with Ordinal Multi Dimensional Scaling (OMDS) and Hierarchical Cluster Analysis (HCA). The findings show that individuals’ understanding of, and support for KM, is essential for their meaningful participation in KM practices. However, positive attitudes toward KM depend on individuals’ involvement in the decisions made on KM practices. Organisations thriving to manage knowledge effectively ought to take an individual- centric approach to KM where individuals, who are meant to participate in KM practices, are placed at the centre of the decision making process on KM practices. Through this study it...

Construction of Local Anchor Map for Indoor Position Measurement System

A prerequisite for a position measurement system is knowledge of anchor positions. In this paper, a local anchor-positioning method based on rotational time-difference-of-arrival (TDOA) measurement and ordinal multidimensional scaling (MDS) is proposed. The calibrated anchor positions serve as basis points to construct the local coordinate system for subsequent target localization. During the calibration phase, each anchor acts as a transmitter in turn, and the TDOA of the signal at rest of the anchors is measured. Although the true interanchor ranges are unknown, we can infer their relative rank from their range differences. Ordinal MDS allows us to find a configuration of anchor positions that preserves the order of interanchor range measurement without involving the exact value of the true range. As the number of anchors increases, the solution space of ordinal MDS rapidly shrinks; hence, an accurate local map of anchors can be constructed. Our simulation shows that, when the number of anchors exceeds ten, ordinal-based MDS outperforms classical metric MDS, as it is more robust to range offset errors.

Ordinal MDS-based localisation for wireless sensor networks

There are various applications in wireless sensor networks which require knowing the relative or actual position of the sensor nodes. Over the past few years, there have been different localisation algorithms proposed in the literature. The algorithms based on classical Multi-Dimensional Scaling (MDS) only require 3 or 4 anchor nodes and can provide higher accuracy than some other schemes. In this paper, we propose and analyse another type of MDS (called ordinal MDS) for localisation in wireless sensor networks. Ordinal MDS differs from classical MDS in that it only requires a monotonicity constraint between the shortest path distance and the Euclidean distance for each pair of nodes. We conduct simulation studies under square and C-shaped topologies with different connectivity levels and number of anchors. Results show that ordinal MDS provides a lower position estimation error than classical MDS in both hop-based and range-based scenarios.

Testing the Social Process model on Selection through expert analysis

For a long time, academics have treated the social process side of personnel selection in a stepmotherly way. Based on a literature review we presented the Social Process model on Selection (SPS model), reflecting applicants' expectations and valuing of procedural and product characteristics of the selection encounter (Derous & De Witte, 2001). In this paper, eight characteristics of the SPS model are tested through expert analysis. Expert analysis is seen as a preliminary step in the development of a questionnaire. Data were generated through Q‐sorting: Thirty experts in selection sorted 69 items into mutually exclusive piles. Additionally, experts were asked to label each pile according to the underlying meaning of the items. Classic ordinal multidimensional scaling was applied on Q‐sorts, revealing a two‐dimensional configuration, comprising six regions (stress=.18 and R2=.85). Corroborative evidence on six clusters was found on the basis of additive tree modelling. To estimate the degree of consistency in labelling, the Consistency in Meaning index (CIM index) was developed. Although experts were not informed about the research goal, both the graphic solution and labelling task came close to the theoretical categories of the SPS model. Based on the results of the expert analysis, the adapted SPS model is presented.

The Geometry of Shape Space: Application to Influenza

Shape space was proposed over 20 years ago as a conceptual formalism in which to represent antibody/antigen binding. It has since played a key role in computational immunology. Antigens and antibodies are considered to be points in an abstract “shape space”, where coordinates of points in this space represent generalized physico-chemical properties associated with various (unspecified) physical properties related to binding, such as geometric shape, hydrophobicity, charge, etc. Distances in shape space between points representing antibodies and (the shape complement) of antigens are assumed to be related to their affinity, with small distances corresponding to high affinity.In this paper, we provide algorithms, related to metric and ordinal multidimensional scaling algorithms first developed in the mathematical psychology literature, which construct explicit, quantitative coordinates for points in shape space given experimental data such as hemagglutination inhibition assays, or other general affinity assays. Previously, such coordinates had been conceptual constructs and totally implicit. The dimension of shape space deduced from hemagglutination inhibition assays for influenza is low, approximately five dimensional.The deduction of the explicit geometry of shape space given experimental affinity data provides new ways to quantify the similarity of antibodies to antibodies, antigens to antigens, and the affinity of antigens to antibodies. This has potential utility in, e.g. strain selection decisions for annual influenza vaccines, among other applications. The analysis techniques presented here are not restricted to the analysis of antibody–antigen interactions and are generally applicable to affinity data resulting from binding assays.

A uniform approach to multidimensional scaling

We present a method and an algorithm that puts interval and ordinal multidimensional scaling at two ends of a continuum. Theory and simulation show that the method compares favorably with classical scaling methods. A parameter is identified that produces scaling that combines benefits of interval, and ordinal scaling.

The Cambridge Structural Database in molecular graphics: techniques for the rapid identification of conformational minima

Techniques are described for the rapid and automatic identification of the low-energy conformations of molecular residues. The techniques use data retrieved from the Cambridge Structural Database and include reduced and marginal ordering, scattergrams of principal component scores, ordinal multidimensional scaling, Andrews' plotting and single- and complete-linkage cluster analysis. When testedon a trial dataset of 110 β-1'-aminofwanoside fragments, all of the techniques proved useful. Single-linkage cluster analysis was particularly successful.

INVARIANCE OF MULTIDIMENSIONAL CONFIGURATIONS

A method of testing for significant relationships between multidimensional configurations is presented. The ‘index of invariance” is defined as the correlation between the inter‐point distances from one configuration and the corresponding inter‐point distances from a second configuration. Empirical distributions of the index are developed for varying numbers of points and dimensions. Fitting the Gram‐Charlier family of curves to these data, additional distributions of the index are estimated based on two parameters, namely, the number of points and the number of dimensions. Tables of significance are presented for the index in terms of the two parameters. Applications based on ordinal multidimensional scaling and other multivariate techniques are discussed.