Cohort normalization based sparse representation for undersampled face recognition

Abstract. In recent years, sparse representation based classification(SRC) has received much attention in face recognition with multipletraining samples of each subject. However, it cannot be easily applied toa recognition task with insufficient training samples under uncontrolledenvironments. On the other hand, cohort normalization, as a way of mea-suring the degradation effect under challenging environments in relationto a pool of cohort samples, has been widely used in the area of biometricauthentication. In this paper, for the first time, we introduce cohort nor-malization to SRC-based face recognition with insufficient training sam-ples. Specifically, a user-specific cohort set is selected to normalize theraw residual, which is obtained from comparing the test sample with itssparse representations corresponding to the gallery subject, using poly-nomial regression. Experimental results on AR and FERET databases show that cohort normalization can bring SRC much robustness against various forms of degradation factors for undersampled face recognition.

An efficient online secondary path estimation for feedback active noise control systems

In practical cases for active noise control (ANC), the secondary path has usually a time varying behavior. For these cases, an online secondary path modeling method that uses a white noise as a training signal is required to ensure convergence of the system. The modeling accuracy and the convergence rate are increased when a white noise with a larger variance is used. However, the larger variance increases the residual noise, which decreases performance of the system and additionally causes instability problem to feedback structures. A sudden change in the secondary path leads to divergence of the online secondary path modeling filter. To overcome these problems, this paper proposes a new approach for online secondary path modeling in feedback ANC systems. The proposed algorithm uses the advantages of white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the algorithm and to prevent the instability effect of the white noise. In this approach, instead of continuous injection of the white noise, a sudden change in secondary path during the operation makes the algorithm to reactivate injection of the white noise to correct the secondary path estimation. In addition, the proposed method models the secondary path without the need of using off-line estimation of the secondary path. Considering the above features increases the convergence rate and modeling accuracy, which results in a high system performance. Computer simulation results shown in this paper indicate effectiveness of the proposed method.

Managing the therapeutic relationship in online cognitive behavioural therapy for depression: Therapists' treatment of clients' contributions

This article examines how therapists and clients manage the therapeutic relationship in online psychotherapy. Our study focuses on early sessions of therapy involving 22 therapist-client pairs participating in online Cognitive Behavioural Therapy (CBT) for depression. Using Conversation Analysis (CA), we examine how therapists can orient to clients’ contributions, while also retaining control of the therapeutic trajectory. We report two practices that therapists can use, at their discretion, following clients’ responses to requests for information. The first, thanking, accepts clients’ responses, orienting to the neutral affective valence of those responses. The second, commiseration, orients to the negative affective valence of clients’ responses. We argue that both practices are a means by which therapists can simultaneously manage developing rapport, while also retaining control of the therapeutic process.

Heteroscedastic probabilistic linear discriminant analysis for manifold learning in video-based face recognition

To recognize faces in video, face appearances have been widely modeled as piece-wise local linear models which linearly approximate the smooth yet non-linear low dimensional face appearance manifolds. The choice of representations of the local models is crucial. Most of the existing methods learn each local model individually meaning that they only anticipate variations within each class. In this work, we propose to represent local models as Gaussian distributions which are learned simultaneously using the heteroscedastic probabilistic linear discriminant analysis (PLDA). Each gallery video is therefore represented as a collection of such distributions. With the PLDA, not only the within-class variations are estimated during the training, the separability between classes is also maximized leading to an improved discrimination. The heteroscedastic PLDA itself is adapted from the standard PLDA to approximate face appearance manifolds more accurately. Instead of assuming a single global within-class covariance, the heteroscedastic PLDA learns different within-class covariances specific to each local model. In the recognition phase, a probe video is matched against gallery samples through the fusion of point-to-model distances. Experiments on the Honda and MoBo datasets have shown the merit of the proposed method which achieves better performance than the state-of-the-art technique.

A variable step-size FxLMS algorithm for feedforward active noise control systems based on a new online secondary path modelling technique

Several approaches have been introduced in literature for active noise control (ANC) systems. Since FxLMS algorithm appears to be the best choice as a controller filter, researchers tend to improve performance of ANC systems by enhancing and modifying this algorithm. This paper proposes a new version of FxLMS algorithm. In many ANC applications an online secondary path modelling method using a white noise as a training signal is required to ensure convergence of the system. This paper also proposes a new approach for online secondary path modelling in feedfoward ANC systems. The proposed algorithm stops injection of the white noise at the optimum point and reactivate the injection during the operation, if needed, to maintain performance of the system. Benefiting new version of FxLMS algorithm and not continually injection of white noise makes the system more desirable and improves the noise attenuation performance. Comparative simulation results indicate effectiveness of the proposed approach.

A new online secondary path modelling method for feedforward active noise control systems

An online secondary path modelling method using a white noise as a training signal is required in many applications of active noise control (ANC) to ensure convergence of the system. Not continually injection of white noise during system operation makes the system more desirable. The purposes of the proposed method are two folds: controlling white noise by preventing continually injection, and benefiting white noise with a larger variance. The modelling accuracy and the convergence rate increase when a white noise with larger variance is used, however larger the variance increases the residual noise, which decreases performance of the system. This paper proposes a new approach for online secondary path modelling in feedfoward ANC systems. The proposed algorithm uses the advantages of the white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the system. Comparative simulation results shown in this paper indicate effectiveness of the proposed approach in controlling active noise.

Improving opportunity recognition and business performance in small and medium manufacturing enterprises through design innovation programs

Increasing awareness of the benefits of stimulating entrepreneurial behaviour in small and medium enterprises has fostered strong interest in innovation programs. Recently many western countries have invested in design innovation for better firm performance. This research presents some early findings from a study of companies that participated in a holistic approach to design innovation, where the outcomes include better business performance and better market positioning in global markets. Preliminary findings from in-depth semi-structured interviews indicate the importance of firm openness to new ways of working and to developing new processes of strategic entrepreneurship. Implications for theory and practice are discussed.

How the environment affects opportunity recognition by entrepreneurs at the bottom of the pyramid

There is an army of bottom of the pyramid entrepreneurs (BOPE) who have the potential to transform developing economies, if they can identify and exploit business opportunities. BOPE could have unidentified resources that could lead to the recognition of radical new opportunities. This study paper asks how environmental factors and identification of resources affect Opportunity Recognition by BOP entrepreneurs in developing economies. To investigate this research question we conduct a literature review and plan semi-structured interviews of existing and nascent entrepreneurs in the largest and arguably the poorest country in Africa, the Democratic Republic of the Congo. In this paper we review the context of BOPE and describe the methodology we will use to gather and analyse data. Finally, we describe our access to suitable respondents for this study and how it will be conducted.

The OO files : providing online formative feedback on Contract Law learning in a narrative setting

Feedback, both formative and summative, enables students to reflect on their understandings and to restructure their thinking to develop their capabilities. It can also encourage positive motivation and help boost self-esteem. Online multiple choice questions can be an efficient and effective means of providing timely formative feedback. At the same time, locating learning in a narrative environment can facilitate engaging and effective learning experiences. Narratives can help learners to navigate through information and support cognitive and imaginative engagement. This article will discuss The 00 Files, an online suite of modules containing multiple choice questions situated in the narrative of a fictional law firm. It notes student responses to the program and discusses lessons that may be learnt from its development which may be of assistance to academics considering the development of similar programs for their courses.

I-vector based speaker recognition using advanced channel compensation techniques

This paper investigates advanced channel compensation techniques for the purpose of improving i-vector speaker verification performance in the presence of high intersession variability using the NIST 2008 and 2010 SRE corpora. The performance of four channel compensation techniques: (a) weighted maximum margin criterion (WMMC), (b) source-normalized WMMC (SN-WMMC), (c) weighted linear discriminant analysis (WLDA), and; (d) source-normalized WLDA (SN-WLDA) have been investigated. We show that, by extracting the discriminatory information between pairs of speakers as well as capturing the source variation information in the development i-vector space, the SN-WLDA based cosine similarity scoring (CSS) i-vector system is shown to provide over 20% improvement in EER for NIST 2008 interview and microphone verification and over 10% improvement in EER for NIST 2008 telephone verification, when compared to SN-LDA based CSS i-vector system. Further, score-level fusion techniques are analyzed to combine the best channel compensation approaches, to provide over 8% improvement in DCF over the best single approach, (SN-WLDA), for NIST 2008 interview/ telephone enrolment-verification condition. Finally, we demonstrate that the improvements found in the context of CSS also generalize to state-of-the-art GPLDA with up to 14% relative improvement in EER for NIST SRE 2010 interview and microphone verification and over 7% relative improvement in EER for NIST SRE 2010 telephone verification.

An optimized online secondary path modeling method for single-channel feedback ANC systems

This paper proposes a new method for online secondary path modeling in feedback active noise control (ANC) systems. In practical cases, the secondary path is usually time varying. For these cases, online modeling of secondary path is required to ensure convergence of the system. In literature the secondary path estimation is usually performed offline, prior to online modeling, where in the proposed system there is no need for using offline estimation. The proposed method consists of two steps: a noise controller which is based on an FxLMS algorithm, and a variable step size (VSS) LMS algorithm which is used to adapt the modeling filter with the secondary path. In order to increase performance of the algorithm in a faster convergence and accurate performance, we stop the VSS-LMS algorithm at the optimum point. The results of computer simulation shown in this paper indicate effectiveness of the proposed method.

Online environmental citizenship : blogs, green marketing and consumer sentiment in the 21st Century

Over the last three decades, the rise in consumer generated content has enabled more environmentally conscious points of view to effect mainstream opinion (Kalafatis, Pollard, East & Tsogas, 1999; Barber, Taylor & Strick, 2009). Consequently, more people are buying into environmentalist ideology and organizing themselves to influence social change. Focus has shifted from attracting public awareness to concern for green ideas, discourse, and environmental citizenship, the latter becoming the guideline by which debates on such topics are regulated (Follows & Jobber, 2000; Dobson, 2003).

Tag-based interaction in online and mobile banking : a preliminary study of the effect on usability

In this paper we describe tag-based interaction afforded by a tag-based interface in online and mobile banking, and present our preliminary usability evaluation findings. We conducted a pilot usability study with a group of banking users by comparing the present 'conventional' interface and tag-based interface. The results show that participants perceive the tag-based interface as more usable in both online and mobile contexts. Participants also rated the tag-based interface better despite their unfamiliarity and perceived it as more user-friendly. Additionally, the results highlight that tag-based interaction is more effective in the mobile context especially to inexperienced mobile banking users. This in turn could have a positive effect on the adoption and acceptance of mobile banking in general and also specifically in Australia. We discuss our findings in more detail in the later sections of this paper and conclude with a discussion on future work.

Motivations for eWOM exchanges in an online community : self-development, problem solving support, relaxation

Electronic word-of-mouth (eWOM) has gained significant attention from academics and practitioners since it has become an important source of consumers’ product information, which can influence consumer purchase intentions (Cheung & Lee, 2012). eWOM exchanges exist in two types of online communities: online communities of practice and online communities of interest. A few prior studies in online communities of interest have examined members’ motivations for product knowledge exchange (Hung & Li, 2007; Ma & Agarwal, 2007). However, there is a lack of understanding of member motivations for exchanging social bonds and enjoyment in addition to exchanging knowledge pertaining to products in the community. It is important to have an initial comprehension of motivation as an antecedent of these three eWOM exchanges so as to be able to determine the driving factors that lead members to generate eWOM communication. Thus, the research problem "What are the driving factors for members to exchange eWOM in an online community?" was justified for investigation. The purpose of this study was to examine different member motivations for exchanging three types of eWOM. Resource exchange theory and theory on consumer motivation and behavior were applied to develop a conceptual framework for this study. This study focused on an online beauty community since there is an increasing trend of consumers turning to online beauty resources so as to exchange useful beauty product information (SheSpot, 2011). As this study examined consumer motivation in an online beauty community, a web-based survey was the most effective and efficient way to gain responses from beauty community members and these members were appropriate samples from which to draw a conclusion about the whole population. Multiple regression analysis was used to test the relationships between member motivations and eWOM exchanges. It was found that members have different motivations for exchanging knowledge, social bonds, and enjoyment related to products: self-development, problem solving support, and relaxation, respectively. This study makes three theoretical contributions. First, this study identifies the influence of self-development motivation on knowledge exchange in an online community of interest, just as this motivation has previously been found in online communities of practice. This study highlights that members of the two different types of online communities share similar goals of knowledge exchange, despite the two communities evincing different attributes (e.g., member characteristics and tasks’ objectives). Further, this study will assist researchers to understand other motivations identified by prior research in online communities of practice since such motivations may be applicable to online communities of interest. Second, this study offers a new perspective on member motivation for social bonding. This study indicates that in addition to social support from friends and family, consumers are motivated to build social bonds with members in an online community of interest since they are an important source of problem solving support in regard to products. Finally, this study extends the body of knowledge pertaining to member motivation for enjoyment exchange. This study provides a basis for researchers to understand that members in an online community of interest value experiential aspects of enjoyable consumption activities, and thus based on group norms, members have a mutual desire for relaxation from enjoyment exchange. The major practical contribution is that this study provides an important guideline for marketing managers to develop different marketing strategies based on member motivations for exchanging three types of eWOM in an online community of interest, such as an online beauty community. This will potentially help marketing managers increase online traffic and revenue, and thus bring success to the community. Although, this study contributes to the literature by highlighting three distinctive member motivations for eWOM exchanges in an online community of interest, there are some possible research limitations. First, this study was conducted in an online beauty community in Australia. Hence, further research should replicate this study in other industries and nations so as to give the findings greater generalisability. Next, online beauty community members are female skewed. Thus, future research should examine whether similar patterns of motivations would emerge in other online communities that tend to be populated by males (e.g., communities focused on football). Further, a web-based survey has its limitations in terms of self-selection and self-reporting (Bhatnagar & Ghose, 2004). Therefore, further studies should test the framework by employing different research methods in order to overcome these weaknesses.

Synchronised integrated online e-health profiles

Web-based social networking applications have become increasingly important in recent years. The current applications in the healthcare sphere can support the health management, but to date there is no patient-controlled integrator. This paper proposes a platform called Multiple Profile Manager (MPM) that enables a user to create and manage an integrated profile that can be shared across numerous social network sites. Moreover, it is able to facilitate the collection of personal healthcare data, which makes a contribution to the development of public health informatics. Here we want to illustrate how patients and physicians can be benefited from enabling the platform for online social network sites. The MPM simplifies the management of patients' profiles and allows health professionals to obtain a more complete picture of the patients' background so that they can provide better health care. To do so, we demonstrate a prototype of the platform and describe its protocol specification, which is an XMPP (Extensible Messaging and Presence Protocol) [1] extension, for sharing and synchronising profile data (vCard²) between different social networks.