Affective games:a multimodal classification system

Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Автоматическое распознавание паралингвистических характеристик говорящего: способы улучшения качества классификации

The ability of artificial systems to recognize paralinguistic signals, such as emotions, depression, or openness, is useful in various applications. However, the performance of such recognizers is not yet perfect. In this study we consider several directions which can significantly improve the performance of such systems. Firstly, we propose building speaker- or gender-specific emotion models. Thus, an emotion recognition (ER) procedure is followed by a gender- or speaker-identifier. Speaker- or gender-specific information is used either for including into the feature vector directly, or for creating separate emotion recognition models for each gender or speaker. Secondly, a feature selection procedure is an important part of any classification problem; therefore, we proposed using a feature selection technique, based on a genetic algorithm or an information gain approach. Both methods result in higher performance than baseline methods without any feature selection algorithms. Finally, we suggest analysing not only audio signals, but also combined audio-visual cues. The early fusion method (or feature-based fusion) has been used in our investigations to combine different modalities into a multimodal approach. The results obtained show that the multimodal approach outperforms single modalities on the considered corpora. The suggested methods have been evaluated on a number of emotional databases of three languages (English, German and Japanese), in both acted and non-acted settings. The results of numerical experiments are also shown in the studyСпособность искусственных систем распознавать паралингвистические характеристики говоря- щего, такие как эмоциональное состояние, наличие и степень депрессии, открытость человека, является полезной для широкого круга приложений. Однако производительность таких систем далека от идеальных значений. В этой статье мы предлагаем подходы, применение которых позволяет существенно улучшить производительность систем распознавания. В работе описы- вается метод построения адаптивных эмоциональных моделей, позволяющих использовать ха- рактеристики конкретного человека для построения точных моделей. В статье представлены алгоритмы выявления наиболее значимых характеристик речевых сигналов, позволяющие одно- временно максимизировать точность решения поставленной задачи и минимизировать количе- ство используемых характеристик сигнала. Наконец, предлагается использовать комбинирован- ные аудио визуальные сигналы в качестве входов для алгоритма машинного обучения. Указанные подходы были реализованы и проверены на 9 эмоциональных речевых корпусах. Результаты прове- денных экспериментов позволяют утверждать, что предложенные в статье подходы улучшают качество решения поставленных задач с точки зрения выбранных критерие

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

Ant Colony Optimization Based Subset Feature Selection in Speech Processing: Constructing Graphs with Degree Sequences

Feature selection or the process of selecting the most discriminating feature subset is an essential practice in speech processing that significantly affects the performance of classification. However, the volume of features that presents in speech processing makes the feature selection perplexing. Moreover, finding the optimal feature subset is a NP-hard problem (2n). Thus, a good searching strategy is required to avoid evaluating large number of combinations in the whole feature subsets. As a result, in recent years, many heuristic based search algorithms are developed to address this NP-hard problem. One of the several meta heuristic algorithms that is applied in many application domains to solve feature selection problem is Ant Colony Optimization (ACO) based algorithms.  ACO based algorithms are nature-inspired from the foraging behavior of actual ants. The success of an ACO based feature selection algorithm depends on the choice of the construction graph with respect to runtime behavior. While most ACO based feature selection algorithms use fully connected graphs, this paper proposes ACO based algorithm that uses graphs with prescribed degree sequences. In this method, the degree of the graph representing the search space will be predicted and the construction graph that satisfies the predicted degree will be generated. This research direction on graph representation for ACO algorithms may offer possibilities to reduce computation complexity from O(n2) to O(nm) in which m is the number of edges. This paper outlines some popular optimization based feature selection algorithms in the field of speech processing applications and overviewed ACO algorithm and its main variants. In addition to that, ACO based feature selection is explained and its application in various speech processing tasks is reviewed. Finally, a degree based graph construction for ACO algorithms is proposed