Affective Computing (AC) is a relatively new, dynamic and interdisciplinary research field. Numerous contributions from fields like computer science, psychology, cognitive science, sociology, physiology and medical science have been made. Consequently, it is difficult to track all recently published trends for early insight utilisation in practise or as basis for innovative research. Even if this fact holds true for many other research fields, AC in this respect is stimulating, due to its dynamic and interdisciplinary characteristics. However, Emergent Entities Recognition is a new concept introduced for early detection and prediction of developing professional terminology. Initial software developments have been completed and briefly analysed in general databases (e.g. MEDLINE). Here, we are interested in its evaluation for AC. In this respect, we have created and used a new Benchmark for Emergent Entities recognition specifially for the field of AC and show evaluation results in comparison to state of the art trained named entity recognition models and to a generic corpus (MEDLINE).

Affective Computing is a rather new and multidisciplinary research field that seeks sophisticated automation in emotion detection for later analysis. However, the automated emotion detection and analysis require as well comprehensive data management support, e.g. to keep control of data produced, and to enable its efficient reuse through classification with established terminology. This paper contributes to data management aspects in Affective Computing and to automation support in emotion classification on the basis of a personal traits analysis. Hence, we describe the implementation of a taxonomy management system, derived from requirements of a case study that investigates the relationship between personality and emotions in Affective Computing. The study makes use of machine learning software developed by SenseCare, an EU-funded R&D project that applies Affective Computing to enhance and advance future healthcare processes and systems.

The reduced cost of DNA sequencing allows metagenomics to be applied on a larger scale. With metagenomic analysis, we have better insight into supplement usage, methane production, and feed conversion efficiency in livestock systems. Nevertheless, sequencing machines generate an enormous amount of complex data. Conventional methods used in the analysis of genomic data involve pre-processing and synchronous reconstruction by multiple systems, which is time consuming and prone to failure. Furthermore, the sequencing datasets and analysis results need to be organized and stored properly in order for scientists to search and access them. To tackle these challenges, a new workflow for metagenomic analysis with improved infrastructure is needed. The MetaPlat project supports experts in both academic and non-academic sectors dealing with challenges in the field of metagenomics by focusing on improved hardware and software platforms. High-performance, fault-tolerant, flexible, and scalable processors and analysis systems will help to increase the effectiveness and efficiency of current metagenomics studies. In this paper, we propose such as an infrastructure applying emerging technologies, such as Kafka, Docker, and Hadoop. Details of the infrastructure solution and some preliminary results are also discussed.

Streaming media on the Internet can be unreliable. Services such as audio-on-demand drastically increase the loads on networks; therefore, new, robust, and highly efficient coding algorithms are necessary. One method overlooked to date, which can work alongside existing audio compression schemes, is that which takes into account the semantics and natural repetition of music. Similarity detection within polyphonic audio has presented problematic challenges within the field of music information retrieval. One approach to deal with bursty errors is to use self-similarity to replace missing segments. Many existing systems exist based on packet loss and replacement on a network level, but none attempt repairs of large dropouts of 5 seconds or more. Music exhibits standard structures that can be used as a forward error correction (FEC) mechanism. FEC is an area that addresses the issue of packet loss with the onus of repair placed as much as possible on the listener's device. We have developed a server--client-based framework (SoFI) for automatic detection and replacement of large packet losses on wireless networks when receiving time-dependent streamed audio. Whenever dropouts occur, SoFI swaps audio presented to the listener between a live stream and previous sections of the audio stored locally. Objective and subjective evaluations of SoFI where subjects were presented with other simulated approaches to audio repair together with simulations of replacements including varying lengths of time in the repair give positive results.

Enjoyment of audio has now become about flexibility and personal freedom. Digital audio content can be acquired from many sources and wireless networking allows digital media devices and associated peripherals to be unencumbered by wires. However, despite recent improvements in capacity and quality of service, wireless networks are inherently unreliable communications channels for the streaming of audio, being susceptible to the effects of range, interference, and occlusion. This time-varying reliability of wireless audio transfer introduces data corruption and loss, with unpleasant audible effects that can be profound and prolonged in duration. Traditional communications techniques for error mitigation perform poorly and in a bandwidth inefficient manner in the presence of such large-scale defects in a digital audio stream. A novel solution that can complement existing techniques takes account of the semantics and natural repetition of music. Through the use of self-similarity metadata, missing or damaged audio segments can be seamlessly replaced with similar undamaged segments that have already been successfully received. We propose a technology to generate relevant self-similarity metadata for arbitrary audio material and to utilize this metadata within a wireless audio receiver to provide sophisticated and real-time correction of large-scale errors. The primary objectives are to match the current section of a song being received with previous sections while identifying incomplete sections and determining replacements based on previously received portions of the song. This article outlines our approach to Forward Error Correction (FEC) technology that is used to “repair” a bursty dropout when listening to time-dependent media on a wireless network. Using self-similarity analysis on a music file, we can “automatically” repair the dropout with a similar portion of the music already received thereby minimizing a listener's discomfort.

In this paper, the concept of object-oriented embedding (OOE) is introduced into information hiding in general and particularly to steganography which is the science that involves undetectable communication of secret data in an appropriate multimedia carrier. The proposal takes advantage of computer vision to orient the embedding process. Although, any existing algorithm can benefit from this technique to enhance its performance against steganalysis attacks, however this work also considers a new embedding algorithm in the wavelet domain using the Binary Reflected Gray Code (BRGC). In the realm of information hiding, one wing focuses on robustness, i.e., watermarking, and another wing focuses on imperceptibility, i.e., steganography. This work advocates for a new steganographic model that meets both robustness as well as imperceptibility. Resilience against common steganalysis attacks including the 274-D merged Markov and DCT features while surviving various image processing manipulations are reported. A neural network classifier was trained with features derived from 400 images. Comparisons with existing systems will also be highlighted.

This paper presents a novel dynamic threshold approach to discriminate skin pixels and non-skin pixels in color images. Fixed decision boundaries (or fixed threshold) classification approaches are successfully applied to segment human skin. These fixed thresholds mostly failed in two situations as they only search for a certain skin color range: 1) any non-skin object may be classified as skin if non-skin objects's color values belong to fixed threshold range. 2) any true skin may be mistakenly classified as non-skin if that skin color values do not belong to fixed threshold range. Therefore in this paper, instead of predefined fixed thresholds, novel online learned dynamic thresholds are used to overcome the above drawbacks. The experimental results show that our method is robust in overcoming these drawbacks.

The majority of existing methods have one thing in common which is the de-correlation of luminance from the considered colour channels. It is believed that the luminance is underestimated here since it is seen as the least contributing colour component to skin colour detection. This work questions this claim by showing that luminance can be useful in separating skin and non-skin clusters. To this end, this work uses a new colour space which contains error signals derived from differentiating the grayscale map and the non-encoded-red grayscale version. The advantages of this approach are the reduction of space dimensionality from 3D to 1D space and the construction of a rapid classifier necessary for real time applications. This method is meant to assist digital image steganography to orient the embedding process since skin information is deemed to be psycho-visually redundant.

Previous studies have shown that Steganography tools such as the S-Tools application outperforms counterpart tools in hiding data in the spatial domain. Another tool called F5 is identified as a robust tool in implementing Steganography in the frequency domain. This paper presents a Steganographic system which exploits the YCbCr colour space. YCbCr is intended to take advantage of human colour-response characteristics. Results show that our algorithm outperforms both F5 and S-Tools. Moreover, our system demonstrates improved performance in retrieving the hidden data after applying image processing attacks in the form of additive artificial noise. As a performance measurement for image distortion Peak-Signal-to-Noise Ratio (PSNR), which is classified under the difference distortion metrics, can be applied to the Stego images. This study also shows that by adopting an object oriented Steganography mechanism, in the sense that we track skin tone objects in video frames, we get a higher PSNR value.

This paper presents the latest research into a mobile intelligent multimedia presentation system called TeleMorph which can dynamically generate a multimedia presentation using output modalities that are determined by the bandwidth available on a mobile device's wireless connection. To demonstrate the effectiveness of this research TeleTuras, a tourist information guide will implement the solution provided by TeleMorph, thus demonstrating its effectiveness. This paper highlights issues surrounding such a system & introduces the architecture.

Abstract Navigation is the process by which people control their movement in virtual environments and is a corefunctional requirement for all virtual environment (VE) applications. Users require the ability to move, controllingorientation, direction of movement and speed, in order to achieve a particular goal within a VE. Navigation israrely the end point in itself (which is typically interaction with the visual representations of data) but applicationsoften place a high demand on navigation skills, which in turn means that a high level of support for navigationis required from the application. On desktop systems navigation in non‐immersive systems is usually supportedthrough the usual hardware devices of mouse and keyboard. Previous work by the authors shows that many usersexperience frustration when trying to perform even simple navigation tasks — users complain about getting lost,becoming disorientated and finding the interface `difficult to use'. In this paper we report on work in progressin exploiting natural language processing (NLP) technology to support navigation in non‐immersive virtualenvironments. A multi‐modal system has been developed which supports a range of high‐level (spoken) navigationcommands and indications are that spoken dialogue interaction is an effective alternative to mouse and keyboardinteraction for many tasks. We conclude that multi‐modal interaction, combining technologies such as NLP withmouse and keyboard may offer the most effective interaction with VEs and identify a number of areas where furtherwork is necessary. ACM CSS: I.3.6 Computer Graphics Methodology and Techniques—Interaction and Techniques, I.3.7 Three‐DimensionalGraphics and Realism—Virtual Reality, I.2.7 Natural Language Processing—Speech Recognitionand Synthesis

The present paper presents a generic environment for intelligent multi media applications, denoted "The Intellimedia WorkBench ". The aim of the workbench is to facilitate development and research within the field of multi modal user interaction. Physically it is a table with various devices mounted above and around. These include: A camera and a laser projector mounted above the workbench, a microphone array mounted on the walls of the room, a speech recogniser and a speech synthesiser. The camera is attached to a vision system capable of locating various objects placed on the workbench. The paper presents two applications utilising the workbench. One is a campus information system, allowing the user to ask for directions within a part of the university campus. The second application is a pool trainer, intended to provide guidance to novice players. Introduction 1. INTRODUCTION Background. Driven by the current move towards multi modal interaction an activity was initiated at Aalb...