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Abstract Currently, libraries and other information services are facing new professional challenges in relation to digitization policies and orphan works after the publication of European Directive 2012/28/EU on certain permitted uses of orphan works and Spanish Royal Decree 224/2016. This paper aims to analyse how Spanish library and information centres specialising in theatre and performing arts meet users' textual and non-textual information needs, by respecting copyright issues and being involved in a dynamic and digital environment. For this purpose, an online questionnaire, which was used as a methodological tool, was sent to library directors in February 2018. At the same time, some research questions based on orphan works and digitisation approaches are discussed in the paper, specifically in reference to graphic and audiovisual materials. [ABSTRACT FROM AUTHOR]

As one of the pivotal technologies leading towards embodied intelligence, audio-visual segmentation is geared towards achieving precise segmentation of sounding objects, offering vast application prospects in scenarios such as emergency rescue and natural exploration. Nevertheless, the performance of audio-visual segmentation technology encounters limitations stemming from challenges related to the adaptation and fusion of cross-modal information encoding, as well as the decoding and generation of masks. To address these issues, this paper explores the adaptation of multi-modal information based on a shared encoder by employing a neural architecture search method to design a hierarchical encoder cooperation module for enhanced information interaction. An intermediate loss is leveraged to help the encoder to keep spatial knowledge reserved. Furthermore, an audio-guided class-aware decoder is devised to guide the generation of masks. Our approach has yielded competitive experimental results across multiple datasets, thus substantiating its effectiveness. [ABSTRACT FROM AUTHOR]

Videos inherently blend multiple modalities in real-world scenarios, primarily visual and auditory cues. When synergized, these cues foster enhanced data representations. Standard clustering techniques, primarily designed for managing vectorial data in Euclidean spaces, struggle to handle multidimensional data with nonlinear manifold structures, such as video or image sets. While recent subspace clustering methods using Riemannian manifold representation tackle this issue, they often sideline auditory information, overlooking the potential harmony between visual and auditory modalities. This paper presents an innovative approach that crafts multiple Riemannian manifold-valued descriptors to bridge this gap, encapsulating multimodal video information in a unified structure. We architect a single-modality Riemannian subspace clustering for individual modal data and extend it to a multi-modality framework, leveraging the interplay of audio-visual data. Detailed optimization and convergence analysis are also provided. The proposed approach significantly outperforms the existing state-of-the-art methods, improving accuracy by 4%, 1%, and 2% on UCF-101, UCF-sport, and AVE datasets, respectively. • Audio-visual integration boosts video clustering. • Riemannian descriptors ensure precise video representation. • Multiple Riemannian descriptors unify video's multimodal information. • Experiments reveal improved video clustering performance. [ABSTRACT FROM AUTHOR]

This review aims to: (1) map the use of sonification in human movement tasks for physical therapy; (2) identify methods of data capture, tasks and its effects on human subjects; (3) suggest future research directions. Sonification can be described as a technique to translate data into sound. It has been used for human motion analysis tasks even if it is not part of most physical therapist's lexicon. Identify and analyze publications where sonification was used as an audio-feedback technique for physical therapy. Thirty-five papers were included, 13 randomized-control-trials. Thirteen papers reported an investigation on a specific dysfunction, while upper limb movements were investigated in fifteen papers. Inertial measurement units were the most commonly used technology to capture human movement, 10 papers reported improvements in motor control and/or movement quality. Gaps in the literature were identified: (1) absence of sonification framework for rehabilitation, (2) no long-term comparison with gold-standard interventions for specific populations, (3) approaches for cardio-respiratory physical therapy and injury prevention were absent. Sonification has the potential to support rehabilitation for physical therapy. Effects of sonification were varied and ranged from improvements in movement quality/control, increased movement and body-awareness and improvements in performance when compared with activities with audio-visual or non-specific audio-feedback among others. Data for sonification was mainly captured using inertial measurement units, smartphones and optical tracking devices but others are also commonly used. Well-designed clinical trials supported by current promising results need to be developed. We recommend testing different sonification techniques in common physical therapy disfunctions using significant outcome measures to understand and maximize its effects on motor learning and control while scoping for further benefits. [ABSTRACT FROM AUTHOR]

This paper examines 25 lecture listening coursebooks for their representativeness of ‘real’ lectures with a view to helping EAP practitioners make informed decisions about materials selection and development. The aspects of representativeness examined are language, lecture authenticity and research-informedness. The representativeness of language was assessed by comparing signposts of important points with those retrieved from a corpus of 160 authentic lectures. Lecture authenticity in terms of source, delivery and length was established by examining the audiovisual materials, transcripts and information provided by authors. Whether materials were research-informed was determined by noting references to lecture and listening research. Results suggest that current lecture listening materials tend not to reflect the language and lectures students are likely to encounter on their degree programmes. Moreover, materials are typically not (systematically) informed by listening and lecture discourse research. These findings highlight the need for EAP practitioners to approach published materials critically and supplement or modify them in ways that would better serve students. The paper concludes with recommendations on how this could be done. [ABSTRACT FROM AUTHOR]

Advancements in computer vision and deep learning have led to difficulty in distinguishing the generated Deepfake media. In addition, recent forgery techniques also modify the audio information based on the forged video, which brings new challenges. However, due to the cross-modal bias, recent multimodal detection methods do not well explore the intra-modal and cross-modal forgery clues, which leads to limited detection performance. In this paper, we propose a novel audio-visual aware multimodal Deepfake detection framework to magnify intra-modal and cross-modal forgery clues. Firstly, to capture temporal intra-modal defects, Forgery Clues Magnification Transformer (FCMT) module is proposed to magnify forgery clues based on sequence-level relationships. Then, the Distribution Difference based Inconsistency Computing (DDIC) module based on Jensen–Shannon divergence is designed to adaptively align multimodal information for further magnifying the cross-modal inconsistency. Next, we further explore spatial artifacts by connecting multi-scale feature representation to provide comprehensive information. Finally, a feature fusion module is designed to adaptively fuse features to generate a more discriminative feature. Experiments demonstrate that the proposed framework outperforms independently trained models, and at the same time, yields superior generalization capability on unseen types of Deepfake. • We proposed a Deepfake detection framework that magnifies intra-modal and cross-modal forgery clues of multiple modalities to conduct more effective detection. • We propose Forgery Clues Magnification Transformer (FCMT) module to magnify the temporal intra-modal forgery clues. Further, we devise the distribution difference-based inconsistency computing (DDIC) module to align cross-modal information for exploring the cross-modal inconsistency. • We also explore spatial artifacts by connecting the concept of multi-scale representations of frame. In addition, a feature fusion module is proposed to adaptively capture the relationship between multiple features. [ABSTRACT FROM AUTHOR]

We performed a systematic review to look at the role of alternative or complementary medicine such as music, acupressure, acupuncture, transcutaneous electrical nerve stimulation (TENS) and audiovisual distractions to decrease analgesia requirement and alleviate anxiety during SWL. Twenty-three papers(2439 participants) were included: Music (n = 1056.6%), Acupuncture (n = 517.7%), Acupressure (n = 13.8%), TENS (n = 617.2%), and audiovisual distraction (n = 14.6%). Most of the studies showed that complementary therapy, lowered pain, and anxiety with higher patient satisfaction and willingness to undergo the procedure. With its feasibility and convenience, urological guidelines need to endorse it, and more should be done to promote its use in outpatient urological procedures. [ABSTRACT FROM AUTHOR]

• A novel two-stream CNN that uses audiovisual data to estimate affective dimensions. • Selection of CNN-based features using minimum redundancy and maximum relevancy rule. • Regression of selected CNN-based features outperforms the end-to-end CNN mapping. • Proposed model shows robust performance of estimation on generalization experiments. Automatic estimation of emotional state has been of great interest as emotion is an important component in user-oriented interactive technologies. This paper investigates the usage of feed-forward convolutional neural network (CNN) and features extracted from such networks for predicting dimensions of continuous-level emotional states. In this context, a two-stream CNN architecture wherein the video and audio data are learned simultaneously, is proposed. End-to-end mapping of audiovisual data to emotional dimensions reveals that the two-stream network performs better than its single-stream counterpart. The representations learned by the CNNs are refined through a minimum redundancy maximum relevance statistical selection method. Then, the support vector regression applied to selected CNN-based features estimates the instantaneous values of emotional dimensions. The proposed method is trained and tested using the audiovisual conversations of well-known RECOLA and SEMAINE databases. Experimentally it is verified that the regression of the CNN-based features outperforms the traditional audiovisual affective features as well as the end-to-end CNN mapping. Through generalization experiments, it is also observed that the learned representations are robust enough to provide an acceptable prediction performance, when the settings of training and testing datasets are widely different. [ABSTRACT FROM AUTHOR]

Highlights • An audio–visual emotion recognition system using CNNs is proposed. • A two-stage ELM based fusion after CNNs is introduced. • Two databases, one Big Data, and another not Big Data, were used. • ELM based fusion outperformed others strategies of fusion. Abstract This paper proposes an emotion recognition system using a deep learning approach from emotional Big Data. The Big Data comprises of speech and video. In the proposed system, a speech signal is first processed in the frequency domain to obtain a Mel-spectrogram, which can be treated as an image. Then this Mel-spectrogram is fed to a convolutional neural network (CNN). For video signals, some representative frames from a video segment are extracted and fed to the CNN. The outputs of the two CNNs are fused using two consecutive extreme learning machines (ELMs). The output of the fusion is given to a support vector machine (SVM) for final classification of the emotions. The proposed system is evaluated using two audio–visual emotional databases, one of which is Big Data. Experimental results confirm the effectiveness of the proposed system involving the CNNs and the ELMs. [ABSTRACT FROM AUTHOR]

• An efficient multimodal feature fusion network for audio-visual speech enhancement. • The first to use fine-grained 3D lip landmarks as auxiliary visual information. • The MEM based on a multi-branch structure can capture critical multi-scale features. • The AFM utilizes a mutually reinforcing strategy to fuse visual and audio features. • An exhaustive experimental study on two audio-visual speech enhancement datasets. Traditional speech enhancement uses only audio signals to obtain clean speech by removing background noise. Audio-visual speech enhancement uses additional visual information to improve the intelligibility and perceptual quality of noisy speech, which can be applied to video conferencing. However, the original visual cues contain redundant information, which causes high latency and makes the model prone to overfitting. Meanwhile, related methods do not pay much attention to exploiting critical visual features and fusing multimodal audio-visual features. In this paper, we propose an efficient multimodal feature fusion network (MFF-Net) for audio-visual speech enhancement. Specifically, we are the first to use fine-grained 3D lip landmarks to represent visual features. This visual representation can provide refined 3D visual information while protecting privacy. We design a multi-scale enhancement module (MEM) with a multi-branch structure that extracts critical multi-scale features by using dilated convolutions and attention mechanisms. Besides, we propose an audio-visual fusion module (AFM) that uses a mutually reinforcing strategy to effectively fuse visual and audio features. To verify the effectiveness of our method, we construct two speech enhancement datasets based on lip landmarks and conduct related experiments. Extensive experimental results show that our proposed MFF-Net has competitive performance compared to other methods. [ABSTRACT FROM AUTHOR]

Abstract Moving towards the next generation of personalized learning environments requires intelligent approaches powered by analytics for advanced learning contexts with enriched digital content. Micro-Learning through Massive Open Online Courses is riding the wave of popularity as a novel paradigm for delivering short educational videos in small pre-organized chunks over time, so that learners can get knowledge in a manageable way. However, with the ever-increasing number of videos, it has become challenging to arrange and search them according to specific categories. In this paper, we get around the problem by bridging Learning Analytics and Cognitive Computing to analyze the content of large video collections, going over traditional term-based methods. We propose an efficient and effective approach to automatically classify a collection of educational videos on pre-existing categories which uses (i) a Speech-to-Text tool to get video transcripts, (ii) Natural Language Processing and Cognitive Computing methods to extract semantic concepts and keywords from video transcripts for their representation, and (iii) Apache Spark as Big Data technology for scalability. Several classifiers are trained on the feature vectors extracted by Cognitive Computing tools. Then, we compared our approach with other combinations of state-of-the-art feature types and classifiers over a large-scale dataset we collected from Coursera. Considering the experimental results, we expect our approach can facilitate the development of Learning Analytics tools powered by Cognitive Computing to support content managers on micro-learning video management while improving how learners search videos. Highlights • An efficient and effective approach to classify micro-learning videos is proposed. • Cognitive Computing is leveraged to extract features from micro-learning videos. • Using concepts and keywords as features improves overall classification performance. [ABSTRACT FROM AUTHOR]

Abstract In this commentary, we examine the papers in a special issue on "Developments and Trends in Learning with Instructional Video". In particular, we focus on basic findings concerning which instructional features improve learning with instructional video (i.e., breaking the lesson into segments paced by the learner; recording from both first- and third-person perspectives) and which features or learner attributes do not (i.e., matching the instructor's gender to the learner's gender; having the instructor's face on the screen; adding practice without feedback; inserting pauses throughout the video; and spatial ability). In addition, we offer recommendations for future work on designing effective video lessons. Highlights • Commentary on articles in the special issue on learning from video. • Effective methods: e.g., segmenting video; presenting video from multiple perspectives. • Ineffective methods: e.g., matching learner and instructor gender; showing the instructor's face. [ABSTRACT FROM AUTHOR]

Abstract Instructional animation research has been extensive but the results are inconsistent. Amongst a number of possible factors to explain these inconclusive results (e.g., the negative influence of transient information), the influence of spatial ability and gender are less explored. This paper reports three experiments that compared the effectiveness of learning a hand-manipulative task (Lego construction) under various conditions with direct examination of the relationship between gender, spatial ability and instructional visualisation. Regression analyses revealed that only one objective measure related to spatial ability (Corsi test) predicted overall test performance, whereas the Card Rotations Test and the Mental Rotations Test did not. However, there was a number of significant gender–spatial ability interactions showing that the spatial ability predictors of male performance were different from those of females. Furthermore a number of subjective measures of spatial ability and experience with instructional animations and static pictures were found to be significant predictors. The results suggest that gender and the type of spatial ability measures used both have a significant impact on gauging the effectiveness of instructional animations. Spatial ability measures should be tailored to gender and the specific nature of the learning domains to yield more consistent research results. Highlights • Predictors of scores learning from animations are different for males and females. • Corsi test and subjective spatial measures predicts performance regardless gender. • No evidence that animations benefit females because of having lower spatial ability. • It suggests including gender and various spatial tests in future animation research. [ABSTRACT FROM AUTHOR]

Video summarization facilitates rapid browsing and efficient video indexing in many applications. A good user video summary should be interesting and satisfy the user’s intention for taking the video. While many existing methods generate trailers from conventionally constructed videos based on low-level features, this paper focuses on creating interesting video summaries from user videos using three semantic features, namely memorability, snap points, and motion. In the proposed framework, we first apply a novel memorability-based video segmentation method. We use a large image dataset with annotated memorability to fine-tune a Hybrid-AlexNet and predict the memorability scores of all video images using the fine-tuned deep network. Next, we estimate the visual importance scores of the segments based on their memorability scores, snap point scores, and motion scores. We then select an optimal subset of the segments to create a memorable and interesting summary that contains image sequences intentionally taken by the video capturer. Finally, we evaluate our method using the SumMe dataset, which contains 15 − 18 human-labeled video summaries. The experimental results demonstrate that our method generates high-quality video summaries that are comparable to human-created summaries and those created by state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper is concerned with key concepts in conversation analytic research: projection, conditional relevance and the adjacency pair. Focusing on a specific type of adjacency pair (instructing/instructed action) that constitutes an exemplary case for the analysis of embodied practices, the paper examines how verbal and embodied resources are mobilized, temporally organized and oriented to by the participants to form instructing and instructed actions. A recurring practice in my data is the combined use of the German deictic form “so” with concurrent bodily demonstrations. In instructional sequences, the expression “so” not only serves as a turn-internal flag (Streeck, 2002) that directs the addressee's visual attention to the speaker's bodily activities, but it can also be part of a projecting first (instruction) or of a projected second pair part (instructed action). Drawing on video recordings from different settings, it will be shown how demonstrations carried out by the instructing person on the one hand and by the instructed person on the other hand are embedded within the sequential format of instructions. The paper argues for a multimodal conceptualization of central notions of CA such as turn, adjacency pair, and conditional relevance. It also raises the theoretical question of how the concept of conditional relevance as it defines the classical adjacency pair relates to the concept of projection when both are (re)considered from a multimodal perspective. [Copyright &y& Elsevier]

The concept underlying the research reported in this paper is that of a semi-automatic system with which groups of distributed users interact, primarily by exchanging multimedia data such as short sound recordings, photographs or video clips taken with mobile phones. The generative website is a place where the public can post their own contents such as articles and videos, and then the website generates possible and meaningful sequences of that data by combining the content provided in an interesting and stimulating way. The paper describes the interaction concept, the algorithms for the generative website and an implementation. An evaluation of user experience with the prototype system is also presented. The overall aim of this research is to design and develop methods for interactive generative systems as stimulants to human creativity and it is claimed that this aim is advanced by the research reported. [Copyright &y& Elsevier]

Abstract: Today''s world is characterized by the increasing availability of audiovisual content, created in many application domains and, thus, with rather different semantic characteristics. Audiovisual content is easily acquired, produced, processed, coded, stored and distributed, progressively making more important the ability for users to selectively consume the large amounts of content available. Automatic audiovisual summarization is a technology playing a major role towards facilitating the user''s effective consumption of large amounts of audiovisual data in a reduced amount of time as time is getting more and more precious and scarce. This paper proposes an automatic summarization system for generic audiovisual content based on MPEG-7 compliant summary descriptions. To evaluate the quality of the created summaries, a user evaluation methodology was designed, and applied with promising results, showing that the developed application is able to summarize with success generic audiovisual content, especially high action content. The main novelty of this paper is related to the usage of the MPEG-7 summarization tools in combination with an arousal-based audiovisual summarization model, and to the novel user evaluation methodology and study. [Copyright &y& Elsevier]

Abstract: In a visual cryptography scheme a secret image is encoded into shares, in the form of transparencies. The shares are then distributed to participants. Qualified subsets of participants can recover the secret image by superimposing their transparencies. Non-qualified subsets of participants have no information about the secret image. In this paper we consider the case when the secret image is a colored image. Most of the previous work on colored visual cryptography allows the superposition of pixels having the same color assuming that the resulting pixel still has the same color. This is not what happens in reality since when superimposing two pixels of the same color one gets a darker version of that color, which effectively is a different color. Superimposing many pixels of the same color might result in a so dark version of the color that the resulting pixel might not be distinguishable from a black pixel. In this paper we propose a model where the reconstruction has to guarantee that the reconstructed secret pixel has the exact same color of the original one and not a darker version of it. We consider -threshold schemes where a qualified set of participants consists of any participants. We provide a general construction for any , and a construction for the special case . We also prove lower bounds on the pixel expansion (which is a measure of the goodness of the scheme) for the cases and . The lower bounds match the pixel expansion of the schemes provided in these two cases, thus proving that our schemes are optimal with respect to the pixel expansion. We also provide an upper bound on the contrast of -threshold schemes and -threshold schemes with optimal contrast. [Copyright &y& Elsevier]

Abstract: This paper presents the whole process of creation of audio-visual speech synthesis system. Such system consists of two main parts, the acoustic synthesis emulating human speech and the facial animation emulating the human lip articulation. The acoustic subsystem is based on concatenation-based speech synthesis. The visual subsystem is designed as a realistic, fully three-dimensional parametrically controllable facial animation model. To be able to parametrically control the animation to emulate human articulation, the set of visual parameters has to be obtained for all basic speech units. To provide realistic animation, the database of lip movements of a real person need to be recorded and expressed by suitable parameterization. The set of control parameters for visual animation is then derived from this database. The 3D model of a head based on a head of a real person also makes the animation more realistic. To obtain such model, a 3D scanning of a real person has to be adopted. We present the design and implementation of above-mentioned process. The aim is to obtain realistic audio-visual speech synthesis with possibility to change the 3D head model according to particular person. The design, acquisition and processing of audio-visual speech corpus for such purpose is presented. Next, the process of both acoustic and visual speech synthesis is described. The visual speech synthesis comprises the tasks of model training, animation control, and co-articulation modelling. A facial animation can also increase intelligibility of a telephone speech to people with hearing disabilities. In such case the textual information to control the animation is not available. Solution to the problem of mapping visual parameters from speech signal either directly or through recognized text is presented. Furthermore, the 3D scanning algorithm is presented. It allows to obtain realistic 3D model based on a head of a real person and thus to personalize the talking head. In the end of this paper, evaluation of intelligibility of the presented audio-visual speech synthesis and its possible applications are presented. [Copyright &y& Elsevier]

The ever-growing amount of audiovisual content available has raised the need to develop systems allowing each of us to consume the information considered ‘essential’, adapted to our tastes, our preferences, our time and also to our capacities of receiving, consuming and storing that information. In other words, there is an increasing need to develop systems able to automatically summarize audiovisual information. This paper proposes a novel query-based summary creation mechanism using a relevance metric and a constraints schema implemented in the context of an automatic video summarization system based on MPEG-7 descriptions. In the context of the same system, this paper also proposes a human skin filter allowing to build summaries based on the presence or absence of human skin. This skin colour filter is based solely on the MPEG-7 Dominant Colour descriptor, which means that the content is skin filtered with a rather small amount of processing, without accessing and processing the video data. [Copyright &y& Elsevier]

Secret sharing schemes allow a secret to be shared among a group of participants so that only qualified subsets of participants can recover the secret. A visual cryptography scheme (VCS) is a special kind of secret sharing scheme in which the secret to share consists of an image and the shares consist of xeroxed transparencies which are stacked to recover the shared image.In this paper, we analyze the relationship between secret sharing schemes and VCSs, focusing our attention on the amount of randomness required to generate the shares. We prove that secret sharing schemes for a set of secrets of size two (BSSs) and VCSs are “equivalent” with respect to the randomness. Indeed, we show how to transform a BSS for a given access structure into a VCS for the same access structure while preserving the randomness of the original scheme. We provide both upper and lower bounds on the randomness of BSSs.All VCSs presented in this paper allow a perfect reconstruction of black pixels. [Copyright &y& Elsevier]

This paper proposes a novel weight-based R- λ scheme for rate control in HEVC, to improve the perceived visual quality of conversational videos. For rate control in HEVC, the conventional R- λ scheme is based on bit per pixel (bpp) to allocate bits. However, bpp does not reflect the visual importance variation of pixels. Therefore, we propose a novel weight-based R- λ scheme to consider this visual importance for rate control in HEVC. We first conducted an eye-tracking experiment on training videos to figure out different importance of background, face, and facial features, thus generating weight maps of encoding videos. Upon the weight maps, our scheme is capable of allocating more bits to the face (especially facial features), using a new term bit per weight. Consequently, the visual quality of face and facial features can be improved such that perceptual video coding is achieved for HEVC, as verified by our experimental results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel spatiotemporal saliency model based on superpixel-level trajectories for saliency detection in videos. The input video is first decomposed into a set of temporally consistent superpixels, on which superpixel-level trajectories are directly generated, and motion histograms at superpixel level as well as frame level are extracted. Based on motion vector fields of multiple successive frames, the inside–outside maps are estimated to roughly indicate whether pixels are inside or outside objects with motion different from background. Then two descriptors, i.e. accumulated motion histogram and trajectory velocity entropy, are exploited to characterize the short-term and long-term temporal features of superpixel-level trajectories. Based on trajectory descriptors and inside–outside maps, superpixel-level trajectory distinctiveness is evaluated and trajectory classification is performed to obtain trajectory-level temporal saliency. Superpixel-level and pixel-level temporal saliency maps are generated in turn by exploiting motion similarity with neighboring superpixels around each trajectory, and color-spatial similarity with neighboring superpixels around each pixel, respectively. Finally, a quality-guided fusion method is proposed to integrate the pixel-level temporal saliency map with the pixel-level spatial saliency map, which is generated based on global contrast and spatial sparsity of superpixels, to generate the pixel-level spatiotemporal saliency map with reasonable quality. Experimental results on two public video datasets demonstrate that the proposed model outperforms the state-of-the-art spatiotemporal saliency models on saliency detection performance. [ABSTRACT FROM AUTHOR]

This paper presents a confidence-based late fusion framework and its application to audio-visual biometric identification. We assign each biometric matcher a confidence value calculated from the matching scores it produces. Then a transformation of the matching scores is performed using a novel confidence-ratio ( C -ratio) i.e., the ratio of a matcher confidence obtained at the test phase to the corresponding matcher confidence obtained at the training phase. We also propose modifications to the highest rank and Borda count rank fusion rules to incorporate the matcher confidence. We demonstrate by experiments that our proposed confidence-based fusion framework is more robust compared to the state-of-the-art late (score- and rank-level) fusion approaches. [ABSTRACT FROM AUTHOR]

European regulation of the media is limited to audio–visual media services and is influenced by the economic regulation of electronic communications, and of e-commerce, that have a close scope of application. However, media regulation has one peculiar differentiating characteristic: it cannot concentrate only on market competition, as the rest of modern economic regulation does, but has to pursue other fundamental values. In particular, media pluralism and media freedom emerge as policy goals that are essential for democracy and human rights in Europe. In this paper, we discuss the EU's search for a point of equilibrium within resistance from member states to relinquishing power in the sector; we describe the current debate, and suggest some possible directions for development. [ABSTRACT FROM AUTHOR]

Negation is an important concept in human language, yet little is known about children’s ability to comprehend negative sentences. In this paper, we explore how 2–5-year-old children’s comprehension of negation changes depending on the context in which a negative sentence occurs. We collected eye-tracking data while children watched a video in which they heard positive and negative sentences. Negative sentences, such as “look at the boy with no apples,” referred to a boy with nothing (Experiment 1) or a boy with an alternative object (Experiment 2). All children showed greater difficulty in resolving the referent when negative sentences referred to the boy with nothing, despite suggestions that nonexistence negations of this type are produced early in development. In addition, 3- and 4-year-old children showed an initial tendency to look away from the target and towards the named noun when the referent of the negative utterance was an alternative object. We argue that the processing of negation in young children is influenced by the cognitive demands of the pragmatic context in which the negative utterance occurs. [ABSTRACT FROM AUTHOR]

Abstract: There is growing evidence that mindfulness and acceptance-based interventions have positive consequences for psychological and physical health. The most well-established of these interventions typically involve relatively large resource commitments, in terms of both the provider and participant. A number of recent studies have begun to explore whether the benefits of such interventions can be generalised to less intensive methods. Methods include pure and guided self-help utilising resources such as books and workbooks, computer programmes and applications and audio-visual materials. This paper presents a systematic review and meta-analysis of studies that have evaluated the effectiveness and acceptability of low-intensity interventions including mindfulness and acceptance-based components. Fifteen RCTs (7 ACT-based, 4 mindfulness-based and 4 multi-component interventions including elements of mindfulness and/or acceptance) were identified and reviewed. Interventions that included mindfulness and/or acceptance-based components produced significant benefits in comparison to control conditions on measures of mindfulness/acceptance, depression and anxiety with small to medium effect sizes. Engagement with the self-help interventions varied but on average two-thirds of participants completed post-intervention measures. Emerging research into low-intensity mindfulness and acceptance-based interventions is hopeful. Recommendations for research and practice are presented. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to set out a teaching innovation project that seeks to advance in the development and assessment one of the fundamental students’ competencies from any Business Administration Degree, such as: critical thinking. We are going to adapt an audiovisual case methodology, developed and already proved in traditional universities, in order to help the students develop and boost one of the competencies required to improve the efficiency and efficacy of their daily activity in organizations, and which previously has been highlighted as fundamental by the academia when the European Higher Education Area (EHEA) was designed. This methodology uses short clips of films – usually, true stories- to help students to understand the practical implications of the theoretical concepts explained at class. We are going to evaluate the implementation of this methodology, and also its impact on the students’ learning process in an Open University. [Copyright &y& Elsevier]

Abstract: The paper addresses the question of whether TV consumption has an impact on material aspirations. We exploit a natural experiment that took place during the period in which Germany was divided. Owing to geographical reasons TV programs from the Federal Republic of Germany could not be received in all parts of the German Democratic Republic. Therefore a natural variation occurred in exposure to West German television. We find robust evidence that watching TV is positively correlated with aspirations. Our identification strategy implies a causal relationship running from TV to aspirations. This conclusion resists various sets of alternative specifications and samples. [Copyright &y& Elsevier]

Current common Presentation System (PS) models are slide based oriented and lack synchronization analysis either with temporal or spatial constraints. Such models, in fact, tend to lead to synchronization problems, particularly on parallel synchronization with spatial constraints between multimedia element presentations. However, parallel synchronization can be properly applied in non-slide based presentation rather than in slide based. This paper presents a novel model of SMIL-driven PS which is able to perform both slide and non-slide based presentations. This novel model of PS has been developed based on the characteristics derived from the unification of characteristics of common PS models and SMIL specifications. By such concept makes the PS has synchronization models i.e. parallel and sequential synchronization with temporal and spatial constraints. Also it has ability to separate between presenter''s and audiences'' views. Thus, to prove and to evaluate our SMIL-driven PS model, we applied the new PS in real world using scenario based approach. Finally, we have a PS that is able to perform both hybrid slide and non-slide based presentation since it has robust synchronization models that support temporal and spatial constraints. [Copyright &y& Elsevier]

Nurses are increasingly involved in a range of strategies to encourage patient behaviours that improve self-management. If nurses are to be involved in, or indeed lead, the development of such interventions then processes that enhance the likelihood that they will lead to evidence that is both robust and usable in practice are required. Although behavioural interventions have been predominantly based on written text or the spoken word increasing numbers are now drawing on visual media to communicate their message, despite only a growing evidence base to support it. The use of such media in health interventions is likely to increase due to technological advances enabling easier and cheaper production, and an increasing social preference for visual forms of communication. However, the development of such media is often highly pragmatic and developed intuitively rather than with theory and evidence informing their content and form. Such a process may be at best inefficient and at worst potentially harmful. This paper performs two functions. Firstly, it discusses and argues why visual based interventions may be a powerful media for behaviour change; and secondly, it proposes a model, developed from the MRC Framework for the Development and Evaluation of Complex Interventions, to guide the creation of theory informed visual interventions. It employs a case study of the development of an intervention to motivate involvement in a lifestyle intervention among people with increased cardiovascular risk. In doing this we argue for a step-wise model which includes: (1) the identification of a theoretical basis and associated concepts; (2) the development of visual narrative to establish structure; (3) the visual rendering of narrative and concepts; and (4) the assessment of interpretation and impact among the intended patient group. We go on to discuss the theoretical and methodological limitations of the model. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a novel framework based on a collective network of evolutionary binary classifiers (CNBC) to address the problems of feature and class scalability. The main goal of the proposed framework is to achieve a high classification performance over dynamic audio and video repositories. The proposed framework adopts a “Divide and Conquer” approach in which an individual network of binary classifiers (NBC) is allocated to discriminate each audio class. An evolutionary search is applied to find the best binary classifier in each NBC with respect to a given criterion. Through the incremental evolution sessions, the CNBC framework can dynamically adapt to each new incoming class or feature set without resorting to a full-scale re-training or re-configuration. Therefore, the CNBC framework is particularly designed for dynamically varying databases where no conventional static classifiers can adapt to such changes. In short, it is entirely a novel topology, an unprecedented approach for dynamic, content/data adaptive and scalable audio classification. A large set of audio features can be effectively used in the framework, where the CNBCs make appropriate selections and combinations so as to achieve the highest discrimination among individual audio classes. Experiments demonstrate a high classification accuracy (above 90%) and efficiency of the proposed framework over large and dynamic audio databases. [Copyright &y& Elsevier]

Abstract: This paper proposes a multimodal approach to distinguish silence from speech situations, and to identify the location of the active speaker in the latter case. In our approach, a video camera is used to track the faces of the participants, and a microphone array is used to estimate the Sound Source Location (SSL) using the Steered Response Power with the phase transform (SRP-PHAT) method. The audiovisual cues are combined, and two competing Hidden Markov Models (HMMs) are used to detect silence or the presence of a person speaking. If speech is detected, the corresponding HMM also provides the spatio-temporally coherent location of the speaker. Experimental results show that incorporating the HMM improves the results over the unimodal SRP-PHAT, and the inclusion of video cues provides even further improvements. [Copyright &y& Elsevier]

Abstract: Sixty-four percent of consumers believe television advertising still has the greatest impact on them. Therefore, there is a great application to provide accurate and real-time TV advertising identification for government and advertisement providers. As the integration of multi-modal method takes full account of video and audio information, this paper aims to handle composite fingerprinting in a unified framework for advertising identification. The Improved Harris Combining Motion feature which is based on the differences between the adjacent video frames can produce video fingerprint. Meanwhile the proposed FIR filter based Fast Audio Fingerprint is focused on extracting the differences between the equivalent bands from adjacent frames. Moreover, this multi-model framework combines the audio and video fingerprint by weighted manner. Experimental results show that compared with the current methods, both audio and video fingerprint has the advantage of higher discrimination, stronger robustness and lower time complexity. Moreover, multi model fingerprint can enhances the performance of the unique fingerprint. [Copyright &y& Elsevier]

Abstract: In this paper, we aimed to study the semantic association of ecologically unrelated synchronous audio-visual information in cognitive integration. A moving particle, which speed varied, was taken as a visual stimulus, while a simple tone, which frequency varied, was used as an auditory stimulus, both were synchronously presented to subjects in the form of a video. Behavioral results confirmed our hypothesis that the moving particle with varied speed and the simple tone with varied frequency were highly associated. Event-related potential (ERP) results showed that an N400 effect and a late posterior negativity (LPN) were elicited under the Incongruent condition as compared to the Congruent condition. It was further determined that there was semantic association between ecologically unrelated synchronous audio-visual information in cognitive integration. We considered that the N400 effect in our results reflected the process that stimulus-driven activities are bound together through a temporal semantic network (TSN) to form multimodal representations, while the state of this temporal semantic network was determined by both long-term learned association among stimuli and short-term experience of incoming information. The LPN might reflect the process that the human brain searches and retrieves context-specifying information in order to make a judgment, and the context-specifying information might have originated from the long-term learned association stored in the brain. [Copyright &y& Elsevier]

Abstract: In the real world, some of the auditory and visual information received by the human brain are temporally asynchronous. How is such information integrated in cognitive processing in the brain? In this paper, we aimed to study the semantic integration of differently asynchronous audio–visual information in cognitive processing using ERP (event-related potential) method. Subjects were presented with videos of real world events, in which the auditory and visual information are temporally asynchronous. When the critical action was prior to the sound, sounds incongruous with the preceding critical actions elicited a N400 effect when compared to congruous condition. This result demonstrates that semantic contextual integration indexed by N400 also applies to cognitive processing of multisensory information. In addition, the N400 effect is early in latency when contrasted with other visually induced N400 studies. It is shown that cross modal information is facilitated in time when contrasted with visual information in isolation. When the sound was prior to the critical action, a larger late positive wave was observed under the incongruous condition compared to congruous condition. P600 might represent a reanalysis process, in which the mismatch between the critical action and the preceding sound was evaluated. It is shown that environmental sound may affect the cognitive processing of a visual event. [Copyright &y& Elsevier]

Abstract: During the last years, we have witnessed the boom of the digital market due to the proliferation of emergent audiovisual services and the increasing number of broadband networks. In this scenario, users insistently demand innovative services for exchanging and sharing their own audiovisual contents. In order to meet these needs, in this paper we propose a system that broadcasts user-generated audiovisual contents for handheld devices in a mobile network based on the DVB-H broadcasting standard. Besides, our system offers diverse added-value services to these new active users, such as: (i) multi modal access (via Web or by client applications running locally in handheld devices) to digital contents, (ii) exploitation of return channels to transmit interactive contents that enhance the user’s experience, and (iii) annotation, sharing and personalized distribution of audiovisual contents. To achieve these goals, our system adopts well-known technologies for broadcasting and semantic annotation of audiovisual contents, as well as emergent technology from the so-called Web 2.0. A prototype of our system has been experimentally evaluated with a group of students from the University of Vigo, who were enthusiastic about the personalization capabilities offered by our TV system for a mobile setting. [ABSTRACT FROM AUTHOR]

Abstract: The effects of audiovisual communications on the emotional and psychological well-being of participants in the legal system have not been previously examined. Using as a framework for analysis what Slobogin (1996) calls internal balancing (of therapeutic versus antitherapeutic effects) and external balancing (of therapeutic jurisprudence [TJ] effects versus effects on other legal values), this brief paper discusses three examples that suggest the complexity of evaluating courtroom audiovisuals in TJ terms. In each instance, audiovisual displays that are admissible based on their arguable probative or explanatory value – day-in-the-life movies, victim impact videos, and computer simulations of litigated events – might well reduce stress and thus improve the psychological well-being of personal injury plaintiffs, survivors, and jurors, respectively. In each situation, however, other emotional and cognitive effects may prove antitherapeutic for the target or other participants, and/or may undermine other important values including outcome accuracy, fairness, and even the conception of the legal decision maker as a moral actor. [ABSTRACT FROM AUTHOR]

Abstract: Design of an embedded audio–visual tracking and speech purification system is described in this paper. The system is able to perform human face tracking, voice activity detection, sound source direction estimation, and speech enhancement in real-time. Estimating the sound source directions helps to initialize the human face tracking module when the target changes the direction. The implementation architecture is based on an embedded dual-core processor, Texas Instruments DM6446 platform (Davinci), which contains an ARM core and a DSP core. For speech signal processing, an eight-channel digital microphone array is developed and the associated pre-processing and interfacing features are designed using the Altera Cyclone II FPGA. All the experiments are conducted in a real environment and the experimental results show that this system can execute all the audition and vision functions in real-time. [ABSTRACT FROM AUTHOR]

Abstract: Accurate measurement of the perceived quality of audio–visual services at the end-user is becoming a crucial issue in digital applications due to the growing demand for compression and transmission of audio–visual services over communication networks. Content providers strive to offer the best quality of experience for customers linked to their different quality of service (QoS) solutions. Therefore, developing accurate, perceptual-based quality metrics is a key requirement in multimedia services. In this paper, we survey state-of-the-art signal-driven perceptual audio and video quality assessment methods independently, and investigate relevant issues in developing joint audio–visual quality metrics. Experiments with respect to subjective quality results have been conducted for analyzing and comparing the performance of the quality metrics. We consider emerging trends in audio–visual quality assessment, and propose feasible solutions for future work in perceptual-based audio–visual quality metrics. [ABSTRACT FROM AUTHOR]

Abstract: In this paper an effective dynamic video summarisation algorithm is presented using audio-visual features extracted from videos. Audio, colour and motion features are dynamically fused using an adaptively weighting mechanism. Dissimilarities of temporal video segments are formulated using the extracted features before these segments are clustered using a fuzzy c-means algorithm with an optimally determined cluster number. The experimental results demonstrate the ability of the proposed algorithm to automatically summarise the videos with good performance. [Copyright &y& Elsevier]

Abstract: Video target tracking is the process of estimating the current state, and predicting the future state of a target from a sequence of video sensor measurements. Multitarget video tracking is complicated by the fact that targets can occlude one another, affecting video feature measurements in a highly non-linear and difficult to model fashion. In this paper, we apply a multisensory fusion approach to the problem of multitarget video tracking with occlusion. The approach is based on a data-driven method (CFA) to selecting the features and fusion operations that improve a performance criterion. Each sensory cue is treated as a scoring system. Scoring behavior is characterized by a rank–score function. A diversity measure, based on the variation in rank–score functions, is used to dynamically select the scoring systems and fusion operations that produce the best tracking performance. The relationship between the diversity measure and the tracking accuracy of two fusion operations, a linear score combination and an average rank combination, is evaluated on a set of 12 video sequences. These results demonstrate that using the rank–score characteristic as a diversity measure is an effective method to dynamically select scoring systems and fusion operations that improve the performance of multitarget video tracking with occlusions. [Copyright &y& Elsevier]

Abstract: Marker pixel detection is a low-level task used by video-based motion capture systems for motion analysis. This is a highly time-consuming task because there are usually a large number of frames to be processed in motion analysis applications. In this paper a novel method for determining marker pixels is proposed. The basic idea behind the proposed method is to reduce the repeated observations of pixels that have already been examined. The method is compared to two classic fill algorithms that are used for this purpose. The results indicate that the proposed method performs better for small circular markers used in this type of applications. [Copyright &y& Elsevier]

Abstract: This paper describes the experimental results for humidity-dependent resistive and capacitive response of methyl-red thin films in a Ag/methyl-red/Ag surface-type cell. A 300-nm-thick film of methyl-red was deposited from 10wt% solution in benzene by a spin coater at an angular speed of 2000 revolutions per minute (RPM) on a glass substrate with preliminary deposited metal electrodes to form the Ag/methyl-red/Ag surface-type cell. The length and width of the gap between the electrodes were 50μm and 15mm, respectively. The resistance of the film reduced from 37 to 17MΩ with an elevation of relative humidity level over the whole humidity range. It was also observed that under the effect of humidity, the capacitance of the methyl-red thin film increased by 12 times. The capacitive/resistive sensor has a quasi-linear function with relative humidity in the range of 30–95% and has a small hysteresis. The response and recovery time of the sensor was about 10s for the capacitive sensor. The humidity-dependent resistive and capacitive properties of this sensor make it promising for use in a humidity meter. [Copyright &y& Elsevier]

Abstract: The analysis presented in this paper explores the effect of air inertia on the squeeze film air contact operation and assesses the contribution of air inertia to the mechanism of squeeze film formation. Derivation of the Reynolds equation containing inertia term for squeeze film air contact is outlined. An average inertia concept is used in dealing with inertia force across the squeeze film thickness. Numerical iteration is used in solving for the non-linear inertia term in the modified Reynolds equation. Results are compared with the performance of the squeeze film air contact without inertia. [Copyright &y& Elsevier]

Abstract: Audiovisual integration has been known to enhance perception; nevertheless, another fundamental audiovisual interaction, i.e. attention rivalry, has not been well investigated. This paper studied the attention rivalry under irrelevant audiovisual stimulation using event-related potential (ERP) and behavioral analysis, and tested the existence of a vision dominated rivalry model. Participants need respond to the target in a bi- or unimodal audiovisual stimulation paradigm. The enhanced amplitude of central P300 under visual target bimodal stimulus indicated that vision demanded more cognitive resources, and the significant amplitude of frontal P200 under bimodal stimulus with non-target auditory stimulus implied that the brain mostly restrained the process of the non-target auditory information. ERP results, together with the analysis of the behavioral data and the subtraction waves, indicated a vision dominated attention rivalry model involved in audiovisual interaction. Furthermore, the latencies of P200 and P300 components implied that audiovisual attention rivalry occurred within the first 300ms after stimulus onset, i.e. significant differences were found in P200 latencies among three target bimodal stimuli, while no difference existed in P300 latencies. Attention shifting and re-directing might be the cause of such early audiovisual rivalry. [Copyright &y& Elsevier]

Abstract: This paper is to investigate the multilayer deposition technique with selective DC magnetron sputtering for the fabrication of partially film deposited magnetostrictive actuator. The multilayer of this study was consisted of TbDyFe and Ni layers. The magneto-mechanical characteristics of TbDyFe (called as Terfenol-D) and Ni multilayered thin films with different thickness ratios of TbDyFe/Ni/TbDyFe (0.2/0.1/0.2, 0.2/0.1/0.1, 0.2/0.1/0.0μm) were considered and the magnetostrictive multilayers were deposited on two kinds of micro-multi-body actuator systems to check the size effect. To fabricate the multilayered film on Si based micro-frame, micromachining process and selective DC magnetron sputtering techniques were combined. The deposited film thicknesses were measured by X-ray diffraction (XRD). To characterize the magneto-mechanical properties of the TbDyFe/Ni/TbDyFe film, the magnetization of the film on the fabricated actuator was observed using vibrating sample magnetometer (VSM) and the magnetostriction of the actuator was determined by measuring the differences of the curvature of the coated silicon substrates using the optical cantilever method. For the application to a micro-actuator, the maximum deflection of the actuator under 0.5T was 280ppm. The results indicate that the Ni layer makes 3–5% lower coercive force and 10–15% enhancement of magnetostriction in low magnetic field (<0.5T). [Copyright &y& Elsevier]

Abstract: In this paper, we report the first results obtained for pyroelectric LiTaO3 thin films prepared on silicon substrates by electrostatic spray pyrolysis (ESP), a simple, low cost and efficient technique not widely used for LiTaO3 deposition, using lithium acetylacetonate and tantalum ethoxide in a mixture of methanol. The effect of the growth and annealing temperature on the structural and optical properties has been investigated. X-ray diffraction measurements have shown that LiTaO3 thin films became preferentially oriented in the (0,1,2) plane after annealing treatment in an oxygen environment using the rapid thermal processing. On the other hand, a thermal stress''s modeling is performed to observe the effect of growth temperature on the as-deposited films and the substrates. The SEM images have shown that the film heat-treated at 600°C became more homogeneous and smoother than that before annealing. The optical phonon modes of the LiTaO3 thin films have been also investigated using infrared reflectivity (FTIR) and Raman spectroscopy. [Copyright &y& Elsevier]

Abstract: The aim of this paper is the analysis of hypersound excitation in GaN films. Simulation of process is presented for a case of a thin film geometry on a non-piezoelectric substrate. The frequency range considered is from 50GHz up to 200GHz. The excitation is due to coupling with space charge waves (SCWs) in GaN film. The amplification of SCWs is related with negative differential conductivity in GaN films. Possible spatial increments are obtained. The amplified SCWs can excite hypersonic waves at the same frequency due to piezoeffect and deformation potential mechanisms. The first effect is stronger and causes an effective resonant excitation of hypersonic waves in the case of full mechanic contact of GaN film and non-piezoelectric substrate. [Copyright &y& Elsevier]