No evidence for subliminal affective priming with emotional facial expression primes

The present study investigated whether facial expressions of emotion presented outside consciousness awareness will elicit evaluative responses as assessed in affective priming. Participants were asked to evaluate pleasant and unpleasant target words that were preceded by masked or unmasked schematic (Experiment 1) or photographic faces (Experiments 1 and 2) with happy or angry expressions. They were either required to perform the target evaluation only or to perform the target evaluation and to name the emotion expressed by the face prime. Prime-target interval was 300 ms in Experiment 1 and 80 ms in Experiment 2. Naming performance confirmed the effectiveness of the masking procedure. Affective priming was evident after unmasked primes in tasks that required naming of the facial expressions for schematic and photographic faces and after unmasked primes in tasks that did not require naming for photographic faces. No affective priming was found after masked primes. The present study failed to provide evidence for affective priming with masked face primes, however, it indicates that voluntary attention to the primes enhances affective priming.

Discovering the best feature extraction and selection algorithms for spontaneous facial expression recognition

Feature extraction and selection are critical processes in developing facial expression recognition (FER) systems. While many algorithms have been proposed for these processes, direct comparison between texture, geometry and their fusion, as well as between multiple selection algorithms has not been found for spontaneous FER. This paper addresses this issue by proposing a unified framework for a comparative study on the widely used texture (LBP, Gabor and SIFT) and geometric (FAP) features, using Adaboost, mRMR and SVM feature selection algorithms. Our experiments on the Feedtum and NVIE databases demonstrate the benefits of fusing geometric and texture features, where SIFT+FAP shows the best performance, while mRMR outperforms Adaboost and SVM. In terms of computational time, LBP and Gabor perform better than SIFT. The optimal combination of SIFT+FAP+mRMR also exhibits a state-of-the-art performance.

Improved facial expression recognition via uni-hyperplane classification

Large margin learning approaches, such as support vector machines (SVM), have been successfully applied to numerous classification tasks, especially for automatic facial expression recognition. The risk of such approaches however, is their sensitivity to large margin losses due to the influence from noisy training examples and outliers which is a common problem in the area of affective computing (i.e., manual coding at the frame level is tedious so coarse labels are normally assigned). In this paper, we leverage the relaxation of the parallel-hyperplanes constraint and propose the use of modified correlation filters (MCF). The MCF is similar in spirit to SVMs and correlation filters, but with the key difference of optimizing only a single hyperplane. We demonstrate the superiority of MCF over current techniques on a battery of experiments.

Multisensory integration weightings underlying haptic feedback laparoscopic surgery

"This letter aims to highlight the multisensory integration weighting mechanisms that may account for the results in studies investigating haptic feedback in laparoscopic surgery. The current lack of multisensory theoretical knowledge in laparoscopy is evident, and “a much better understanding of how multimodal displays in virtual environments influence human performance is required” ...publisher website

Intensity pulsations in distributed feedback Yb:Phosphate waveguide lasers

The intensity pulsations of a cw 1030 nm Yb:Phosphate monolithic waveguide laser with distributed feedback are described. We show that the pulsations could result from the coupling of the two orthogonal polarization modes through the two photon process of cooperative luminescence. The predictions of the presented theoretical model agree well with the observed behaviour.

Text mining in negative relevance feedback

It is a big challenge to clearly identify the boundary between positive and negative streams. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on RCV1, and substantial experiments show that the proposed approach achieves encouraging performance.

Robust facial feature extraction and matching

Automated feature extraction and correspondence determination is an extremely important problem in the face recognition community as it often forms the foundation of the normalisation and database construction phases of many recognition and verification systems. This paper presents a completely automatic feature extraction system based upon a modified volume descriptor. These features form a stable descriptor for faces and are utilised in a reversible jump Markov chain Monte Carlo correspondence algorithm to automatically determine correspondences which exist between faces. The developed system is invariant to changes in pose and occlusion and results indicate that it is also robust to minor face deformations which may be present with variations in expression.

Orals ain’t orals : the role of prepared oral presentations in the classroom and beyond. How do instruction and feedback practices guide delivery choices?

Despite an ostensibly technology-driven society, the ability to communicate orally continues to feature as an essential ability for students at school and university, as it is for graduates in the workplace. Pedagogically, one rationale is that the need to develop effective oral communication skills is tied to life-long learning which includes successful participation in future work-related tasks. One tangible way that educators have assessed proficiency in the area of communication is through prepared oral presentations. While much of the literature uses the terms 'oral communication' and 'oral presentation' interchangeably, some writers question the role more formal presentations play in the overall development of oral communication skills. However, such formal speaking tasks continue to be a recognised assessment practice in both the secondary school and academy, and, therefore, worthy of further investigation. Adding to the discussion, this thesis explores the knowledge and skills students bring into the academy from previous educational experiences. It examines some of the teaching and assessment methods used in secondary schools to develop oral communication skills through the use of formal oral presentations. Specifically, it investigates criterion-referenced assessment sheets and how these tools are used as a form of instruction, as well as their role and effectiveness in the evaluation of student ability. The focus is on the student's perspective and includes 12 semi-structured interviews with school students. The purpose of this thesis is to explore key thematics underpinning oral communication and to identify tensions between expectations and practice. While acknowledging the breadth and depth of material available under the heading of 'communication theory', this study specifically draws on an expanded view of the rhetorical tradition to fully interrogate the assumptions supporting the practice of assessing oral presentations. Finally, this thesis recommends reconnecting with an updated understanding of rhetoric as a way of assisting in the development of expressive, articulate and discerning communicators.

Towards spontaneous facial expression recognition in real-world video

Facial expression is an important channel of human social communication. Facial expression recognition (FER) aims to perceive and understand emotional states of humans based on information in the face. Building robust and high performance FER systems that can work in real-world video is still a challenging task, due to the various unpredictable facial variations and complicated exterior environmental conditions, as well as the difficulty of choosing a suitable type of feature descriptor for extracting discriminative facial information. Facial variations caused by factors such as pose, age, gender, race and occlusion, can exert profound influence on the robustness, while a suitable feature descriptor largely determines the performance. Most present attention on FER has been paid to addressing variations in pose and illumination. No approach has been reported on handling face localization errors and relatively few on overcoming facial occlusions, although the significant impact of these two variations on the performance has been proved and highlighted in many previous studies. Many texture and geometric features have been previously proposed for FER. However, few comparison studies have been conducted to explore the performance differences between different features and examine the performance improvement arisen from fusion of texture and geometry, especially on data with spontaneous emotions. The majority of existing approaches are evaluated on databases with posed or induced facial expressions collected in laboratory environments, whereas little attention has been paid on recognizing naturalistic facial expressions on real-world data. This thesis investigates techniques for building robust and high performance FER systems based on a number of established feature sets. It comprises of contributions towards three main objectives: (1) Robustness to face localization errors and facial occlusions. An approach is proposed to handle face localization errors and facial occlusions using Gabor based templates. Template extraction algorithms are designed to collect a pool of local template features and template matching is then performed to covert these templates into distances, which are robust to localization errors and occlusions. (2) Improvement of performance through feature comparison, selection and fusion. A comparative framework is presented to compare the performance between different features and different feature selection algorithms, and examine the performance improvement arising from fusion of texture and geometry. The framework is evaluated for both discrete and dimensional expression recognition on spontaneous data. (3) Evaluation of performance in the context of real-world applications. A system is selected and applied into discriminating posed versus spontaneous expressions and recognizing naturalistic facial expressions. A database is collected from real-world recordings and is used to explore feature differences between standard database images and real-world images, as well as between real-world images and real-world video frames. The performance evaluations are based on the JAFFE, CK, Feedtum, NVIE, Semaine and self-collected QUT databases. The results demonstrate high robustness of the proposed approach to the simulated localization errors and occlusions. Texture and geometry have different contributions to the performance of discrete and dimensional expression recognition, as well as posed versus spontaneous emotion discrimination. These investigations provide useful insights into enhancing robustness and achieving high performance of FER systems, and putting them into real-world applications.

Please take out your phones : on the spot solicitation of student feedback in class

The use of mobile devices such as smart phones and tablets in classrooms has been met with mixed sentiments. Some instructors and teachers see them as a distraction and regularly ban their usage. Others who see their potential to enhance learning have started to explore ways to integrate them into their teaching in an attempt to improve student engagement. In this paper we report on a pilot study that forms part of a university-wide project reconceptualising its approach to the student evaluation of learning and teaching. In a progressive decision to embrace mobile technology, the university decided to trial a smart phone app designed for students to check-in to class and leave feedback on the spot. Our preliminary findings from trialling the app indicate that the application establishes a more immediate feedback loop between students and teachers. However, the app’s impact depends on how feedback is shared with students and how the teaching team responds.

Student feedback systems in higher education: a focused literature review and environmental scan

In recent times, higher education institutions have paid increasing attention to the views of students to obtain feedback on their experience of learning and teaching through internal surveys. This article reviews research in the field and reports on practices in other Australian universities. Findings demonstrate that while student feedback is valued and used by all Australian universities, survey practices are idiosyncratic and in the majority of cases, questionnaires lack validity and reliability; data are used inadequately or inappropriately; and they offer limited potential for cross-sector benchmarking. The study confirms the need for institutions to develop an overarching framework for evaluation in which a valid, reliable, multidimensional and useful student feedback survey constitutes just one part. Given external expectations and internal requirements to collect feedback from students on their experience of learning and teaching, the pursuit of sound evaluation practices will continue to be of interest at local, national and international levels.

Curbing resource consumption using team-based feedback : paper printing in a longitudinal case study

This paper details a team-based feedback approach for reducing resource consumption. The approach uses paper printing within office environments as a case study. It communicates the print usage of each participant’s team rather than the participant’s individual print usage. Feedback is provided weekly via emails and contains normative information, along with eco-metrics and team-based comparative statistics. The approach was empirically evaluated to study the effectiveness of the feedback method. The experiment comprised of 16 people belonging to 4 teams with data on their print usage gathered over 58 weeks, using the first 30-35 weeks as a baseline. The study showed a significant reduction in individual printing with an average of 28%. The experiment confirms the underlying hypothesis that participants are persuaded to reduce their print usage in order to improve the overall printing behaviour of their teams. The research provides clear pathways for future research to qualitatively investigate our findings.

Retrieving information from microblog using pattern mining and relevance feedback

Retrieving information from Twitter is always challenging due to its large volume, inconsistent writing and noise. Most existing information retrieval (IR) and text mining methods focus on term-based approach, but suffers from the problems of terms variation such as polysemy and synonymy. This problem deteriorates when such methods are applied on Twitter due to the length limit. Over the years, people have held the hypothesis that pattern-based methods should perform better than term-based methods as it provides more context, but limited studies have been conducted to support such hypothesis especially in Twitter. This paper presents an innovative framework to address the issue of performing IR in microblog. The proposed framework discover patterns in tweets as higher level feature to assign weight for low-level features (i.e. terms) based on their distributions in higher level features. We present the experiment results based on TREC11 microblog dataset and shows that our proposed approach significantly outperforms term-based methods Okapi BM25, TF-IDF and pattern based methods, using precision, recall and F measures.

Overview of the INEX 2011 relevance feedback track

The INEX 2011 Relevance Feedback track offered a refined approach to the evaluation of Focused Relevance Feedback algorithms through simulated exhaustive user feedback. Run in largely identical fashion to the Relevance Feedback track in INEX 2010[2], we simulated a user-in-the loop by re-using the assessments of ad-hoc retrieval obtained from real users who assess focused ad-hoc retrieval submissions. We present the evaluation methodology, its implementation, and experimental results obtained for four submissions from two participating organisations. As the task and evaluation methods did not change between INEX 2010 and now, explanations of these details from the INEX 2010 version of the track have been repeated verbatim where appropriate.

The effectiveness of energy feedback for conservation and peak demand : a literature review

This paper reviews electricity consumption feedback literature to explore the potential of electricity feedback to affect residential consumers’ electricity usage patterns. The review highlights a substantial amount of literature covering the debate over the effectiveness of different feedback criteria to residential customer acceptance and overall conservation and peak demand reduction. Researchers studying the effects of feedback on everyday energy use have observed substantial variation in effect size, both within and between studies. Although researchers still continue to question the types of feedback that are most effective in encouraging conservation and peak load reduction, some trends have emerged. These include that feedback be received as quickly as possible to the time of consumption; be related to a standard; be clear and meaningful and where possible both direct and indirect feedback be customised to the customer. In general, the literature finds that feedback can reduce electricity consumption in homes by 5 to 20 per cent, but that significant gaps remain in our knowledge of the effectiveness and cost benefit of feedback.