Confusion Effect Research Articles

Impact of green marketing, greenwashing and green confusion on green brand equity

Purpose This study aims to examine the negative effects of excessive product packaging (EPP), greenwashing and green confusion on green brand equity (GBE). Furthermore, the moderating role of brand credibility in mitigating the negative effects of green marketing was investigated. Design/methodology/approach A within-subject experiment was conducted to evaluate excessive versus minimal product packaging to test the proposed hypotheses. Data analysis was performed with SmartPLS 3.3.3, which analyzed data from 206 consumers. Findings The results showed that EPP positively predicts greenwashing and green confusion. However, greenwashing has a negative impact on GBE. Brand credibility was also discovered to moderate the negative relationship between greenwashing and GBE, thereby reducing the negative effect of greenwashing. Research limitations/implications The findings imply that marketing managers should understand the consumers’ concerns for the environment, making product and brand strategies that promote environmental protection and sustainability. Originality/value This study contributes to the green marketing literature by empirically validating the positive impacts of EPP on greenwashing and green confusion, as well as the negative influence of greenwashing on GBE. Furthermore, it reveals how brand credibility can reduce the harmful effects of greenwashing on GBE.

The Sensitivity of GPz Estimates of Photo-z Posterior PDFs to Realistically Complex Training Set Imperfections

The accurate estimation of photometric redshifts is crucial to many upcoming galaxy surveys, for example, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). Almost all Rubin extragalactic and cosmological science requires accurate and precise calculation of photometric redshifts; many diverse approaches to this problem are currently in the process of being developed, validated, and tested. In this work, we use the photometric redshift code GPz to examine two realistically complex training set imperfections scenarios for machine learning based photometric redshift calculation: (i) where the spectroscopic training set has a very different distribution in color–magnitude space to the test set, and (ii) where the effect of emission line confusion causes a fraction of the training spectroscopic sample to not have the true redshift. By evaluating the sensitivity of GPz to a range of increasingly severe imperfections, with a range of metrics (both of photo-z point estimates as well as posterior probability distribution functions, PDFs), we quantify the degree to which predictions get worse with higher degrees of degradation. In particular, we find that there is a substantial drop-off in photo-z quality when line-confusion goes above ∼1%, and sample incompleteness below a redshift of 1.5, for an experimental setup using data from the Buzzard Flock synthetic sky catalogs.

Spatial Audio Scene Characterization (SASC): Automatic Localization of Front-, Back-, Up-, and Down-Positioned Music Ensembles in Binaural Recordings

The automatic localization of audio sources distributed symmetrically with respect to coronal or transverse planes using binaural signals still poses a challenging task, due to the front–back and up–down confusion effects. This paper demonstrates that the convolutional neural network (CNN) can be used to automatically localize music ensembles panned to the front, back, up, or down positions. The network was developed using the repository of the binaural excerpts obtained by the convolution of multi-track music recordings with the selected sets of head-related transfer functions (HRTFs). They were generated in such a way that a music ensemble (of circular shape in terms of its boundaries) was positioned in one of the following four locations with respect to the listener: front, back, up, and down. According to the obtained results, CNN identified the location of the ensembles with the average accuracy levels of 90.7% and 71.4% when tested under the HRTF-dependent and HRTF-independent conditions, respectively. For HRTF-dependent tests, the accuracy decreased monotonically with the increase in the ensemble size. A modified image occlusion sensitivity technique revealed selected frequency bands as being particularly important in terms of the localization process. These frequency bands are largely in accordance with the psychoacoustical literature.

Encryption Algorithm for Securing Non-Disclosure Agreements in Outsourcing Offshore Software Maintenance

Properly created and securely communicated, non-disclosure agreement (NDA) can resolve most of the common disputes related to outsourcing of offshore software maintenance (OSMO). Occasionally, these NDAs are in the form of images. Since the work is done offshore, these agreements or images must be shared through the Internet or stored over the cloud. The breach of privacy, on the other hand, is a potential threat for the image owners as both the Internet and cloud servers are not void of danger. This article proposes a novel algorithm for securing the NDAs in the form of images. As an agreement is signed between the two parties, it will be encrypted before sending to the cloud server or travelling through the public network, the Internet. As the image is input to the algorithm, its pixels would be scrambled through the set of randomly generated rectangles for an arbitrary amount of time. The confusion effects have been realized through an XOR operation between the confused image, and chaotic data. Besides, 5D multi-wing hyperchaotic system has been employed to spawn the chaotic vectors due to good properties of chaoticity it has. The machine experimentation and the security analysis through a comprehensive set of validation metric vividly demonstrate the robustness, defiance to the multifarious threats and the prospects for some real-world application of the proposed encryption algorithm for the NDA images.

The Impact of Live-Streaming E-commerce Key Opinion Leader Traits on Consumer Engagement——Based on the Mediating and Moderating Effect of Consumer Confusion and Platform Enabling

Live-streaming e-commerce has become a significant technique for businesses to perform marketing operations because to the rapid development of social emerging media. Consumers are more exposed to live shopping, and in order to save time and money, they rely more on Key Opinion Leaders (KOL). KOLs deliver more thorough product information to customers, eliminate buyer confusion, and improve consumer engagement behaviors. As a result, this study investigates the impact of KOL traits in connection to consumer engagement and offers a moderated mediation model to investigate if customer confusion serves as a mediating factor and whether this process is controlled by platform enabling. The findings of the empirical investigation, which used 368 valid samples, reveal that: consumer confusion partially mediates the association between KOL traits and consumer engagement; and platform enabling positively moderates the relationship between KOL traits and consumer confusion. These findings have significant implications for businesses looking to improve the quality of their marketing initiatives and broaden their market reach.

Do health literacy levels of nursing students change throughout the study programme? A cross-sectional study

ObjectivesThe aim of this research was to analyse if the level of health literacy (HL) of nursing students changes throughout the study programme.DesignA cross-sectional study with anonymous self-reporting was conducted.Participants/setting329...

Fish waves as emergent collective antipredator behavior

The collective behavior of animals has attracted considerable attention in recent years, with many studies exploring how local interactions between individuals can give rise to global group properties.1-3 The functional aspects of collective behavior are less well studied, especially in the field,4 and relatively few studies have investigated the adaptive benefits of collective behavior insituations where prey are attacked by predators.5,6 This paucity of studies is unsurprising because predator-prey interactions in the field are difficult to observe. Furthermore, the focus in recent studies on predator-prey interactions has been on the collective behavior of the prey7-10 rather than on the behavior of the predator (but see Ioannou etal.11 and Handegard etal.12). Here we present a field study that investigated the anti-predator benefits of waves produced by fish at the water surface when diving down collectively in response to attacks of avian predators. Fish engaged in surface waves that were highly conspicuous, repetitive, and rhythmic involving many thousands of individuals for up to 2min. Experimentally induced fish waves doubled the time birds waited until their next attack, therefore substantially reducing attack frequency. In one avian predator, capture probability, too, decreased with wave number and birds switched perches in response to wave displays more often than in control treatments, suggesting that they directed their attacks elsewhere. Taken together, these results support an anti-predator function of fish waves. The attack delay could be a result of a confusion effect or a consequence of waves acting as a perception advertisement, which requires further exploration.

Development of Light-Weight TRIP/TWIP FCC High Entropy Alloy with High Specific Strength and Large Ductility

In this study we developed a novel (TRIP+TWIP) high entropy alloy (HEA) with high specific strength and large ductility. First, by controlling the atomic constitution of the 3d transition metals (Cr, Mn, Fe, Co, and Ni), we designed a light-weight TRIP-assisted dual-phase HEA with a non-equiatomic composition of Cr22Mn6Fe40Co26Ni6, which exhibited 5% lighter density than the Cantor HEA. Secondly, we systematically added Al (a lightweight element (2.7 g/cm3), which has a large atomic size misfit with 3d transition metals, and Ferrite stabilizer) up to 5 at.% in Cr22Mn6Fe40Co26Ni6 HEA. With increasing Al content, the phase constitution of the alloy changed from a dual-phase of FCC and HCP (0 to 2.0 at.%) to a FCC single-phase (2.5 to 3.5 at.%), to a dual-phase of FCC and BCC (4.0 to 5.0 at.%). In particular, the (Cr22Mn6Fe40Co26Ni6)97.5Al2.5 HEA with the FCC single-phase exhibited a large Hall-Petch coefficient and relatively lower thermal conductivity due to its three times larger atomic size mismatch (δ) than the Cantor HEA, which causes the superior solid solution strengthening effect. Furthermore, the (Cr22Mn6Fe40Co26Ni6)96Al4.0 HEA, a boundary composition of BCC precipitation in the FCC phase, exhibited a 10% higher specific strength than the Cantor HEA as well as 50% larger strain, due to the unique TRIP and TWIP complex deformation mechanism. This result shows that the addition of Al in Cr22Mn6Fe40Co26Ni6 HEA can result not only in greater chemical complexity due to the multicomponent high entropy compositions, but also microstructural complexity due to the increase in competing crystalline phases. The confusion effect caused by both complexities lets the alloy overcome the trade-off relationship among conflicting intrinsic properties, such as strength versus ductility (or density). Consequently, these results pave the way for a new design strategy of a novel (TRIP+TWIP) HEA with high specific strength and large ductility.

A new class of light-weight metastable high entropy alloy with high strength and large ductility

The strength-ductility (or density, or thermal conductivity) trade-off is unavoidable in most conventional alloys due to the limited composition space. Herein, we developed a new class of light-weight metastable high entropy alloy (LW metastable HEA) with high specific strength and large ductility. Firstly, a property-targeted alloy design to develop the LW TRIP-assisted dual-phase HEA was performed based on CALPHAD calculation by constituting the alloy with a higher fraction of LW elements in 3d transition metal elements and having the extremely low stacking fault energy. Secondly, we systematically add Al (low density, large atomic size misfit, and Ferrite stabilizer) up to 5 at.% in the non-equiatomic Cr24Mn6Fe40Co24Ni6 HEA developed through the first step. As a result, we confirmed that the phase constitution is changed with increasing Al contents from (FCC+HCP) dual-phase to FCC single phase to (FCC+BCC (B2)) dual-phase. In particular, the FCC single-phase HEA exhibits (1) a relatively low density (∼10% reduction compared to Cantor alloy), (2) an abnormally large lattice distortion (3 times greater atomic size mismatch compared to Cantor alloy), which can be confirmed by extremely low thermal conductivity as well as large lattice parameter; (3) a large solid solution strengthening, which can be confirmed by a significant Hall-Petch coefficient (∼50% increase compared to Cantor alloy); (4) TRIP/TWIP complex deformation mechanisms (large ductility over ∼40%). Thus, it can be understood that the developed single- phase FCC HEAs can provide exceptional combinations of density, strength, and ductility in the trade-off relationship through the confusion effect by the microstructural complexity as well as the chemical complexity. Consequently, these results provide a guideline on how to design a new class of LW metastable HEA with promising properties as a new generation of structural material.

Confusion Effects of Facial Expression Recognition in Patients With Major Depressive Disorder and Healthy Controls.

Facial expression recognition plays a crucial role in understanding the emotion of people, as well as in social interaction. Patients with major depressive disorder (MDD) have been repeatedly reported to be impaired in recognizing facial expressions. This study aimed to investigate the confusion effects between two facial expressions that presented different emotions and to compare the difference of confusion effect for each emotion pair between patients with MDD and healthy controls. Participants were asked to judge the emotion category of each facial expression in a two-alternative forced choice paradigm. Six basic emotions (i.e., happiness, fear, sadness, anger, surprise, and disgust) were examined in pairs, resulting in 15 emotion combinations. Results showed that patients with MDD were impaired in the recognition of all basic facial expressions except for the happy expression. Moreover, patients with MDD were more inclined to confuse a negative emotion (i.e., anger and disgust) with another emotion as compared to healthy controls. These findings highlight the importance that patients with MDD show a deficit of sensitivity in distinguishing specific two facial expressions.

Can Strategic Behaviour Facilitate Confusion Resolution? The Interplay Between Confusion and Metacognitive Strategies in Betty’s Brain

Confusion may benefit learning when it is resolved or partially resolved. Metacognitive strategies (MS) may help learners to resolve confusion when it occurs during learning and problem solving. This study examined the relationship between confusion and MS that students evoked in Betty’s Brain, a computer-based learning-by-modelling environment where elementary and middle school students learn science by building causal maps. Participants were sixth graders. Emotion data were collected from real-time observations by trained researchers. MS and task performance information were determined by analyzing the action logs. Pre- and post-tests were used to assess learning gains. The results revealed that the use of MS was a function of the state of student confusion. However, confusion resolution was not related to MS behaviour, and MS did not moderate the effect of confusion on student task performance in Betty’s Brain or on learning gains.

Consumer confusion, shopping fatigue, and negative purchasing behavior in internet shopping environment

This study analyzed the effects of consumer confusion on shopping fatigue and negative purchasing behaviors in an internet shopping environment. Further, the effects of shopping fatigue on negative purchasing behaviors were analyzed. The survey was conducted among consumers in their 20s and 40s in the Seoul metropolitan area who had experience of purchasing fashion products through internet shopping. A total of 392 questionnaire were analysis, with frequency, reliability, factor, correlation, and regression analysis completed using the SPSS statistics program. The results of the study showed that consumer confusion and shopping fatigue in internet shopping environment affected negative purchasing behaviors. First, consumer confusion comprised overload confusion, similarity confusion, and ambiguity confusion. It was found that overload confusion and similarity confusion significantly affected shopping fatigue. Second, shopping fatigue significantly affected negative purchasing behaviors, and an increased level of shopping fatigue result in, increased purchase delay and non-purchase behavior. Third, consumer confusion (overload confusion, similarity confusion, ambiguity confusion) significantly affected purchase delay behavior, while similarity confusion and ambiguity confusion significantly affected non-purchasing behavior. These results will provide useful data for e-CRM and marketing directions of internet companies and will contribute to rational decision-making of internet consumers and improve the quality of consumer life.

Motion: enhancing signals and concealing cues.

ABSTRACTAnimal colour patterns remain a lively focus of evolutionary and behavioural ecology, despite the considerable conceptual and technical developments over the last four decades. Nevertheless, our current understanding of the function and efficacy of animal colour patterns remains largely shaped by a focus on stationary animals, typically in a static background. Yet, this rarely reflects the natural world: most animals are mobile in their search for food and mates, and their surrounding environment is usually dynamic. Thus, visual signalling involves not only animal colour patterns, but also the patterns of animal motion and behaviour, often in the context of a potentially dynamic background. While motion can reveal information about the signaller by attracting attention or revealing signaller attributes, motion can also be a means of concealing cues, by reducing the likelihood of detection (motion camouflage, motion masquerade and flicker-fusion effect) or the likelihood of capture following detection (motion dazzle and confusion effect). The interaction between the colour patterns of the animal and its local environment is further affected by the behaviour of the individual. Our review details how motion is intricately linked to signalling and suggests some avenues for future research. This Review has an associated Future Leader to Watch interview with the first author.

Peran Ambiguity Confusion dalam Word of Mouth pada Platform Marketplace

This study is an case study that aims to analyze the effect of ambiguity confusion on word of mouth. The type of data used in this study is primary data from platfom marketplace user. The number of samples in this study were 150 user that selected by purposive sampling method. The independent variable used is ambiguity confusion, while dependent variable is word of mouth. The analysis model used in this research is multi linear regression analysis using SPSS 25. The result show that the ambiguity has positive effect on word of mouth.

Design and implementation of code obfuscator based on random opcode

Software obfuscation is a common way of software protection. Source obfuscation and binary obfuscation are unable to achieve universal obfuscation algorithms, while existing compile-time obfuscation algorithms can be effectively restored by symbolic execution. In this paper, an innovative code obfuscation algorithm based on random opcode is proposed. The random number generator is a linear congruence generator written recursively. The algorithm can be used for code obfuscation across languages, platforms and architectures, and has application value for general-purpose software protection. The results of our experiments show this algorithm proposed in this paper can significantly increase the time complexity and space complexity of symbol execution and greatly increase the cost of reverse engineering, which can effectively resist the restore by symbolic execution and has a good confusion effect.

Collective Predator Perception Advertisement in Fish

The idea that prey might advertise their perception of predators to avoid attacks has received considerable attention in the literature. However, evidence for reduced attack frequencies or attack success in predators as a result of perception advertisement in prey is limited or indirect. Here we present a field study which investigated the benefits of waves produced by fish at the water surface when diving down collectively in response to attacks of avian predators. Instead of merely escaping, fish engaged in surface waves that were highly conspicuous, repetitive and rhythmic involving many thousands of individuals for up to 2 min. Experimentally-induced fish waves doubled the time birds waited until their next attack therefore substantially reducing attack frequency. In one avian predator, capture probability, too, decreased with wave number and birds switched perches in response to wave displays more often than in control treatments suggesting that they directed their attacks elsewhere. Taken together these results support fish waves’ function of perception advertisement. A predator confusion effect is also possible but so far is not known to be associated with waving behaviour especially given that rhythmic waving could make prey more predictable. Collectively produced perception advertisements raise some intriguing questions of how fish synchronise to produce such signals and which pathway has led to their evolution.

Neural correlates of confusability in recognition of morphologically complex Korean words.

When people confuse and reject a non-word that is created by switching two adjacent letters from an actual word, is called the transposition confusability effect (TCE). The TCE is known to occur at the very early stages of visual word recognition with such unit exchange as letters or syllables, but little is known about the brain mechanisms of TCE. In this study, we examined the neural correlates of TCE and the effect of a morpheme boundary placement on TCE. We manipulated the placement of a morpheme boundary by exchanging places of two syllables embedded in Korean morphologically complex words made up of lexical morpheme and grammatical morpheme. In the two experimental conditions, the transposition syllable within-boundary condition (TSW) involved exchanging two syllables within the same morpheme, whereas the across-boundary condition (TSA) involved the exchange of syllables across the stem and grammatical morpheme boundary. During fMRI, participants performed the lexical decision task. Behavioral results revealed that the TCE was found in TSW condition, and the morpheme boundary, which is manipulated in TSA, modulated the TCE. In the fMRI results, TCE induced activation in the left inferior parietal lobe (IPL) and intraparietal sulcus (IPS). The IPS activation was specific to a TCE and its strength of activation was associated with task performance. Furthermore, two functional networks were involved in the TCE: the central executive network and the dorsal attention network. Morpheme boundary modulation suppressed the TCE by recruiting the prefrontal and temporal regions, which are the key regions involved in semantic processing. Our findings propose the role of the dorsal visual pathway in syllable position processing and that its interaction with other higher cognitive systems is modulated by the morphological boundary in the early phases of visual word recognition.

A new deep transfer learning network based on convolutional auto-encoder for mechanical fault diagnosis

Deep learning has gained a great achievement in the intelligent fault diagnosis of rotating machineries. However, the labeled data is scarce in actual engineering and the marginal distribution of data is discrepant under different conditions. Transfer learning provides a feasible way to overcome these difficulties. Considering the effect of noise on the transfer fault diagnosis, this work puts forward a new deep transfer learning network based on convolutional auto-encoder(CAE-DTLN) to implement the mechanical fault diagnosis in target domain without labeled data. In the proposed framework, CAE is used as the feature extractor as it has the ability of noise removal. Moreover, both CORrelation ALignment (CORAL) loss and domain classification loss are integrated to enhance the effect of domain confusion. The proposed model is applied to the fault transfer diagnosis of planetary gearboxes under different working loads and noise levels, and it is compared with other typical fault transfer diagnosis models. The experimental results show that CAE-DTLN has higher diagnosis accuracy and stronger generalization ability. The average diagnostic accuracy of CAE-DTLN is over 99%. Moreover, the proposed transfer learning model has better anti-noise performance.

An effective approach for the protection of user commodity viewing privacy in e-commerce website

Along with the rapid development of network technologies, the server-side of an e-commerce website is becoming more and more untrustworthy. Thus, how to prevent the disclosure of users’ behavior privacy in online business activities has attracted people’s wide attention. Aiming at the protection of users’ commodity viewing privacy in a commercial website, this paper proposes to construct a group of dummy requests on a trusted client, then, which are submitted together with a user commodity viewing request to the untrusted server-side, so as to confuse and cover up the user preferences. First, we define a privacy model for a user commodity viewing service, in which we introduce a concept called entropy for commodity viewing probability to measure the confusion effect of dummy requests on user requests, and we introduce a concept called regional distance among commodity categories to measure the cover-up effect of dummy requests on users’ commodity viewing preferences. Second, we design an implementation algorithm to generate a group of ideal dummy requests that can meet the constraints formulated in the privacy model. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed approach, i.e., which can improve the security of users’ commodity viewing privacy on the untrusted server-side, without compromising the availability of an e-commerce website. In this paper, we present a valuable research attempt to the protection of users’ behavior privacy in a commercial website, which is of positive significance for building a privacy-preserving e-commerce platform.

MEMF: Multi-level-attention embedding and multi-layer-feature fusion model for person re-identification

Person re-identification (re-ID) methods need to extract representative, rich and discriminative features in order to deal with the effect of imperfect pedestrian detectors, illumination changes, occlusions, and background confusion. In this paper, a multi-level-attention embedding and multi-layer-feature fusion (MEMF) model is proposed for person re-ID. Specifically, a novel backbone network is designed, in which multi-level-attention blocks are embedded into a multi-layer-feature fusion architecture. Multi-level-attention blocks can highlight representative features and assist global feature expression, and multi-layer-feature fusion can increase the fine granularity of feature expression and obtain richer features. Besides, a new eigenvalue difference orthogonality (EDO) loss is designed to reduce the correlation between features. The final loss is defined as the combination of the cross-entropy loss and the EDO loss, which improves re-ID results. The proposed method is evaluated on four popular and challenging datasets. Detailed experiments demonstrate that the application of various elements of the MEMF model can help improve person re-ID performance. Compared with start-of-the-art methods, the MEMF model gets a promising result.