Exploring negativity bias in brand beliefs and stated brand switching propensity

Negativity bias has been well studied by psychologists but limited research has been conducted on it in a marketing context. Given previous research, this exploratory study aims to examine whether there are any negativity bias effects in brand beliefs and whether there is any influence on stated brand switching propensity amongst current users of a brand. The results suggest that there is a negativity bias evident in brand image data.

Adaptive data based neural network leader-follower control of multi-agent networks

In this paper, we propose a data based neural network leader-follower control for multi-agent networks where each agent is described by a class of high-order uncertain nonlinear systems with input perturbation. The control laws are developed using multiple-surface sliding control technique. In particular, novel set of sliding variables are proposed to guarantee leader-follower consensus on the sliding surfaces. Novel switching is proposed to overcome the unavailability of instantaneous control output from the neighbor. By utilizing RBF neural network and Fourier series to approximate the unknown functions, leader-follower consensus can be reached, under the condition that the dynamic equations of all agents are unknown. An O(n) data based algorithm is developed, using only the network’s measurable input/output data to generate the distributed virtual control laws. Simulation results demonstrate the effectiveness of the approach.

Discriminating DDoS attack traffic from flash crowd through packet arrival patterns

Current DDoS attacks are carried out by attack tools, worms and botnets using different packet-transmission strategies and various forms of attack packets to beat defense systems. These problems lead to defense systems requiring various detection methods in order to identify attacks. Moreover, DDoS attacks can mix their traffics during flash crowds. By doing this, the complex defense system cannot detect the attack traffic in time. In this paper, we propose a behavior based detection that can discriminate DDoS attack traffic from traffic generated by real users. By using Pearson's correlation coefficient, our comparable detection methods can extract the repeatable features of the packet arrivals. The extensive simulations were tested for the accuracy of detection. We then performed experiments with several datasets and our results affirm that the proposed method can differentiate traffic of an attack source from legitimate traffic with a quick response. We also discuss approaches to improve our proposed methods at the conclusion of this paper.

Activity recognition and abnormality detection with the switching hidden semi-Markov model

This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which is an important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the activities and their typical duration. To this end, we introduce the Switching Hidden Semi-Markov Model (S-HSMM), a two-layered extension of the hidden semi-Markov model (HSMM) for the modeling task. Activities are modeled in the S-HSMM in two ways: the bottom layer represents atomic activities and their duration using HSMMs; the top layer represents a sequence of high-level activities where each high-level activity is made of a sequence of atomic activities. We consider two methods for modeling duration: the classic explicit duration model using multinomial distribution, and the novel use of the discrete Coxian distribution. In addition, we propose an effective scheme to detect abnormality without the need for training on abnormal data. Experimental results show that the S-HSMM performs better than existing models including the flat HSMM and the hierarchical hidden Markov model in both classification and abnormality detection tasks, alleviating the need for presegmented training data. Furthermore, our discrete Coxian duration model yields better computation time and generalization error than the classic explicit duration model.

An analysis of Fiji's monetary policy transmission

Purpose – The purpose of this paper is to examine the monetary policy transmission mechanism for the Fiji Islands using a structural vector autoregressive (SVAR) model for the period 1975 to 2005.

Design/methodology/approach – The SVAR model investigates how a monetary policy shock – defined as a temporary and exogenous rise in the short-term interest rate – affects real and nominal macro variables; namely real output, prices, exchange rates, and money supply.

Findings – The results suggest that a monetary policy shock statistically significantly reduces output initially, but then output is able to recover to its pre-shock level. A monetary policy shock generates inflationary pressure, leads to an appreciation of the Fijian currency and reduces the demand for money. The paper also analysed the impact of a nominal effective exchange rate (NEER) shock (an appreciation) on real output and found that it leads to a statistically significant negative effect on real output.

Practical implications – The findings of this study should be of direct relevance to the research and policy work undertaken at the Reserve Bank of Fiji.

Originality/value – For a small economy, such as Fiji, where monetary policy is key to sustainable macroeconomic management, this is the first paper that undertakes a dynamic analysis of monetary policy transmission. The paper uses time series data over three decades and builds a structural VAR model, rooted in theory. This paper will be of direct relevance to the Reserve Bank of Fiji. The approach and model proposed will also be useful for applied monetary policy researchers in other developing countries where inflation rate targeting is a key element of the monetary policy setting.

Predicted packet padding for anonymous web browsing against traffic analysis attacks

Anonymous communication has become a hot research topic in order to meet the increasing demand for web privacy protection. However, there are few such systems which can provide high level anonymity for web browsing. The reason is the current dominant dummy packet padding method for anonymization against traffic analysis attacks. This method inherits huge delay and bandwidth waste, which inhibits its use for web browsing. In this paper, we propose a predicted packet padding strategy to replace the dummy packet padding method for anonymous web browsing systems. The proposed strategy mitigates delay and bandwidth waste significantly on average. We formulated the traffic analysis attack and defense problem, and defined a metric, cost coefficient of anonymization (CCA), to measure the performance of anonymization. We thoroughly analyzed the problem with the characteristics of web browsing and concluded that the proposed strategy is better than the current dummy packet padding strategy in theory. We have conducted extensive experiments on two real world data sets, and the results confirmed the advantage of the proposed method.

Fuzzy logic optimized wireless sensor network routing protocol

Wireless sensor networks (WSNs) are used in health monitoring, tracking and security applications. Such networks transfer data from specific areas to a nominated destination. In the network, each sensor node acts as a routing element for other sensor nodes during the transmission of data. This can increase energy consumption of the sensor node. In this paper, we propose a routing protocol for improving network lifetime and performance. The proposed protocol uses type-2 fuzzy logic to minimize the effects of uncertainty produced by the environmental noise. Simulation results show that the proposed protocol performs better than a recently developed routing protocol in terms of extending network lifetime and saving energy and also reducing data packet lost.

A mathematical model for the spread of streptococcus pneumoniae with transmission due to sequence type

This paper discusses a simple mathematical model to describe the spread of Streptococcus pneumoniae. We suppose that the transmission of the bacterium is determined by multi-locus sequence type. The model includes vaccination and is designed to examine what happens in a vaccinated population if MLSTs can exist as both vaccine and non vaccine serotypes with capsular switching possible from the former to the latter. We start off with a discussion of Streptococcus pneumoniae and a review of previous work. We propose a simple mathematical model with two sequence types and then perform an equilibrium and (global) stability analysis on the model. We show that in general there are only three equilibria, the carriage-free equilibrium and two carriage equilibria. If the effective reproduction number Re is less than or equal to one, then the carriage will die out. If Re > 1, then the carriage will tend to the carriage equilibrium corresponding to the multi-locus sequence type with the largest transmission parameter. In the case where both multi-locus sequence types have the same transmission parameter then there is a line of carriage equilibria. Provided that carriage is initially present then as time progresses the carriage will approach a point on this line. The results generalize to many competing sequence types. Simulations with realistic parameter values confirm the analytical results.

Development of a laboratory-based transmission diffraction technique for in situ deformation studies of Mg alloys

A laboratory-based transmission X-ray diffraction technique was developed to measure elastic lattice strains parallel to the loading direction during in situ tensile deformation. High-quality transmission X-ray diffraction data were acquired in a time frame suitable for in situ loading experiments by application of a polycapillary X-ray optic with a conventional laboratory Cu X-ray source. Based on the measurement of two standard reference materials [lanthanum hexaboride (NIST SRM 660b) and silicon (NIST SRM 640c)], precise instrumental alignment and calibration of the transmission diffraction geometry were realized. These results were also confirmed by the equivalent data acquired using the standard Bragg-Brentano measurement geometry. An empirical Caglioti function was employed to describe the instrumental broadening, while an axis of rotation correction was used to measure and correct the specimen displacement from the centre of the goniometer axis. For precise Bragg peak position and hkil intensity information, a line profile fitting methodology was implemented, with Pawley refinement used to measure the sample reference lattice spacings (d o (hkil)). It is shown that the relatively large X-ray probe size available (7 × 714mm) provides a relatively straightforward approach for improving the grain statistics for the study of metal alloys, where grain sizes in excess of 114μm can become problematic for synchrotron-based measurements. This new laboratory-based capability was applied to study the lattice strain evolution during the elastic-plastic transition in extruded and rolled magnesium alloys. A strain resolution of 2 × 10-4 at relatively low 2θ angles (20-65° 2θ) was achieved for the in situ tensile deformation studies. In situ measurement of the elastic lattice strain accommodation with applied stress in the magnesium alloys indicated the activation of dislocation slip and twin deformation mechanisms. Furthermore, measurement of the relative change in the intensity of 0002 and 10 3 was used to quantify {10 2} 011 tensile twin onset and growth with applied load.

Fast and energy efficient data storage for information discovery in multi-dimensional WSNs

Multidimensional WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multi-dimensional data). An efficient information dis-covery for multi-dimensional WSNs deployed in mission–critical environments has become an essential research consideration. Timely and energy efficient information discovery is very impor-tant to maintain the QoS of such mission critical applications. An inefficient information discovery mechanism will result in high transmission of data packets over the network creating bottlenecks leading to unbalanced energy consumption over the network. High latency and inefficient energy consumption will have a direct effect on the QoS of mission-critical applications of particular importance in this regard is the minimization of hotspots.

A feasible IP traceback framework through dynamic deterministic packet marking

DDoS attack source traceback is an open and challenging problem. Deterministic packet marking (DPM) is a simple and effective traceback mechanism, but the current DPM based traceback schemes are not practical due to their scalability constraint. We noticed a factor that only a limited number of computers and routers are involved in an attack session. Therefore, we only need to mark these involved nodes for traceback purpose, rather than marking every node of the Internet as the existing schemes doing. Based on this finding, we propose a novel marking on demand (MOD) traceback scheme based on the DPM mechanism. In order to traceback to involved attack source, what we need to do is to mark these involved ingress routers using the traditional DPM strategy. Similar to existing schemes, we require participated routers to install a traffic monitor. When a monitor notices a surge of suspicious network flows, it will request a unique mark from a globally shared MOD server, and mark the suspicious flows with the unique marks. At the same time, the MOD server records the information of the marks and their related requesting IP addresses. Once a DDoS attack is confirmed, the victim can obtain the attack sources by requesting the MOD server with the marks extracted from attack packets. Moreover, we use the marking space in a round-robin style, which essentially addresses the scalability problem of the existing DPM based traceback schemes. We establish a mathematical model for the proposed traceback scheme, and thoroughly analyze the system. Theoretical analysis and extensive real-world data experiments demonstrate that the proposed traceback method is feasible and effective.

Cross-strand disulfides in the non-hydrogen bonding site of antiparallel β-sheet (aCSDns): poised for biological switching

Forbidden disulfides are stressed disulfides found in recognisable protein contexts previously defined as structurally forbidden. The torsional strain of forbidden disulfides is typically higher than for structural disulfides, but not so high as to render them immediately susceptible to reduction under physionormal conditions. The meta-stability of forbidden disulfides makes them likely candidates as redox switches. Here we mined the Protein Data Bank for examples of the most common forbidden disulfide, the aCSDn. This is a canonical motif in which disulfide-bonded cysteine residues are positioned directly opposite each other on adjacent anti-parallel β-strands such that the backbone hydrogen bonded moieties are directed away from each other. We grouped these aCSDns into homologous clusters and performed an extensive physicochemical and informatic analysis of the examples found. We estimated their torsional energies using quantum chemical calculations and studied differences between the preferred conformations of the computational model and disulfides found in solved protein structures to understand the interaction between the forces imposed by the disulfide linkage and typical constraints of the surrounding β-sheet. In particular, we assessed the twisting, shearing and buckling of aCSDn-containing β-sheets, as well as the structural and energetic relaxation when hydrogen bonds in the motif are broken. We show the strong preference of aCSDns for the right-handed staple conformation likely arises from its compatibility with the twist, shear and Cα separation of canonical β-sheet. The disulfide can be accommodated with minimal distortion of the sheet, with almost all the strain present as torsional strain within the disulfide itself. For each aCSDn cluster, we summarise the structural and strain data, taxonomic conservation and any evidence of redox activity. aCSDns are known substrates of thioredoxin-like enzymes. This, together with their meta-stability, means they are ideally suited to biological switching roles and are likely to play important roles in the molecular pathways of oxidative stress.