Automatic emotional state detection using facial expression dynamic in videos

In this paper, an automatic emotion detection system is built for a computer or machine to detect the emotional state from facial expressions in human computer communication. Firstly, dynamic motion features are extracted from facial expression videos and then advanced machine learning methods for classification and regression are used to predict the emotional states. The system is evaluated on two publicly available datasets, i.e. GEMEP_FERA and AVEC2013, and satisfied performances are achieved in comparison with the baseline results provided. With this emotional state detection capability, a machine can read the facial expression of its user automatically. This technique can be integrated into applications such as smart robots, interactive games and smart surveillance systems

Managing Communication Latency-Hiding at Runtime for Parallel Programming Languages and Libraries

This work introduces a runtime model for managing communication with support for latency-hiding. The model enables non-computer science researchers to exploit communication latency-hiding techniques seamlessly. For compiled languages, it is often possible to create efficient schedules for communication, but this is not the case for interpreted languages. By maintaining data dependencies between scheduled operations, it is possible to aggressively initiate communication and lazily evaluate tasks to allow maximal time for the communication to finish before entering a wait state. We implement a heuristic of this model in DistNumPy, an auto-parallelizing version of numerical Python that allows sequential NumPy programs to run on distributed memory architectures. Furthermore, we present performance comparisons for eight benchmarks with and without automatic latency-hiding. The results shows that our model reduces the time spent on waiting for communication as much as 27 times, from a maximum of 54% to only 2% of the total execution time, in a stencil application.Comment: PREPRIN

Applications of Face Analysis and Modeling in Media Production

Facial expressions play an important role in day-by-day communication as well as media production. This article surveys automatic facial analysis and modeling methods using computer vision techniques and their applications for media production. The authors give a brief overview of the psychology of face perception and then describe some of the applications of computer vision and pattern recognition applied to face recognition in media production. This article also covers the automatic generation of face models, which are used in movie and TV productions for special effects in order to manipulate people's faces or combine real actors with computer graphics

Fault tolerant architectures for integrated aircraft electronics systems, task 2

The architectural basis for an advanced fault tolerant on-board computer to succeed the current generation of fault tolerant computers is examined. The network error tolerant system architecture is studied with particular attention to intercluster configurations and communication protocols, and to refined reliability estimates. The diagnosis of faults, so that appropriate choices for reconfiguration can be made is discussed. The analysis relates particularly to the recognition of transient faults in a system with tasks at many levels of priority. The demand driven data-flow architecture, which appears to have possible application in fault tolerant systems is described and work investigating the feasibility of automatic generation of aircraft flight control programs from abstract specifications is reported

Programming exercises evaluation systems: an interoperability survey

Learning computer programming requires solving programming exercises. In computer programming courses teachers need to assess and give feedback to a large number of exercises. These tasks are time consuming and error-prone since there are many aspects relating to good programming that should be considered. In this context automatic assessment tools can play an important role helping teachers in grading tasks as well to assist students with automatic feedback. In spite of its usefulness, these tools lack integration mechanisms with other eLearning systems such as Learning Management Systems, Learning Objects Repositories or Integrated Development Environments. In this paper we provide a survey on programming evaluation systems. The survey gathers information on interoperability features of these systems, categorizing and comparing them regarding content and communication standardization. This work may prove useful to instructors and computer science educators when they have to choose an assessment system to be integrated in their e-Learning environment

Enhanced frequency management for automatic HF radio communication systems

The work described in this thesis aims to enhance the frequency management of automatic high frequency (HF) radio communication systems. During the research programme two new frequency management tools were developed; a chirpsounder monitoring tool to provide accuracy enhancement information for propagation prediction programs and an algorithm designed to allow optimisation of signal formats, so that in-band interference is avoided and the overall system throughput rate is increased. Two new HF communication system architectures are presented, which use system design and programming methodologies derived from the fields of artificial intelligence and computer networks.The characteristics of the HF band are presented from a communicator's viewpoint, rather than the generalised, technical approach normally associated with such reviews. The methods employed by current HF communication systems to overcome the inherent time and frequency variability of HF channels are presented in the form of reviews of propagation, natural noise and co-channel interference prediction methods, embedded real-time channel evaluation algorithms and HF communications system architectures. The inadequacies of these current techniques are analysed. The eradication of their shortcomings is the main objective of the work described in the thesis.The short-term inaccuracies associated with current propagation analysis procedures can limit the performance of automatic HF communication systems. An accuracy enhancement methodology is proposed which makes use of measurements made on oblique chirpsounder transmitters. In order to provide accuracy enhancement data, a chirpsounder-based, propagation monitor was constructed. Its implementation and trials are described and methods of using its output to enhance prediction model accuracy are discussed. Ways in which its performance may be improved are detailed.The theory of a technique, termed "template correlation", which provides automatic HF communication systems with signal format adaptation data in order to enable them to avoid in-band interference, is presented. The objective of this work is to enhance the error-free capacity of a channel via adaptation of the signal. The results of computer simulations and laboratory bench trials of template correlation are presented. Enhancements of the technique in the light of the trials results are included.Two proposed design methodologies for automatic HF communication systems are described. The first uses many of the frequency management tools associated with current automatic systems and it combines the information from these using a blackboard-based expert system architecture. The second proposed design is more conceptual than the first. An inductive expert system is employed to produce rules describing the ways in which an automatic HF system should respond to certain path conditions. Examples of how such a system might function are given.The single, most important factor which has enabled the techniques described in this thesis to be feasible is the availability of cheap but powerful microprocessors. Thus the overall philosophy of the work is to improve the performance of automatic HF communication systems via the incorporation of processing power and "intelligent software" into the communication system's terminals

Using supervised machine learning to code policy issues: Can classifiers generalize across contexts?

Content analysis of political communication usually covers large amounts of material and makes the study of dynamics in issue salience a costly enterprise. In this article, we present a supervised machine learning approach for the automatic coding of policy issues, which we apply to news articles and parliamentary questions. Comparing computer-based annotations with human annotations shows that our method approaches the performance of human coders. Furthermore, we investigate the capability of an automatic coding tool, which is based on supervised machine learning, to generalize across contexts. We conclude by highlighting implications for methodological advances and empirical theory testing