In this paper, we propose an effective method for emergent leader detection in meeting environments which is based on nonverbal visual features. Identifying emergent leader is an important issue for organizations. It is also a wellinvestigated topic in social psychology while a relatively new problem in social signal processing (SSP). The effectiveness of nonverbal features have been shown by many previous SSP studies. In general, the nonverbal video-based features were not more effective compared to audio-based features although, their fusion generally improved the overall performance. However, in absence of audio sensors, the accurate detection of social interactions is still crucial. Motivating from that, we propose novel, automatically extracted, nonverbal features to identify the emergent leadership. The extracted nonverbal features were based on automatically estimated visual focus of attention which is based on head pose. The evaluation of the proposed method and the defined features were realized using a new dataset which is firstly introduced in this paper including its design, collection and annotation. The effectiveness of the features and the method were also compared with many state of the art features and methods.

We present a research tool that supports marine ecologists' research by allowing analysis of long-term and continuous fish monitoring video content. The analysis can be used for instance to discover ecological phenomena such as changes in fish abundance and species composition over time and area. Two characteristics set our system apart from traditional ecological data collecting and processing methods. First, the continuous video recording results in enormous data volumes of monitoring data. Currently around a year of video recordings (containing over the 4 million fish observations) have been processed. Second, different from traditional manual recording and analysing the ecological data, the whole recording, analysing and presentation of results is automated in this system. On one hand, it saves the effort of manually examining every video, which is infeasible. On the other hand, no automatic video analysis method is perfect, so the user interface provides marine ecologists with multiple options to verify the data. Marine ecologists can examine the underlying videos, check results of automatic video analysis at different certainty levels computed by our system, and compare results generated by multiple versions of automatic video analysis software to verify the data in our system. This research tool enables marine ecologists for the first time to analyse long-term and continuous underwater video records.

Classification of data is difficult if the data is imbalanced and classes are overlapping. In recent years, more research has started to focus on classification of imbalanced data since real world data is often skewed. Traditional methods are more successful with classifying the class that has the most samples (majority class) compared to the other classes (minority classes). For the classification of imbalanced data sets, different methods are available, although each has some advantages and shortcomings. In this study, we propose a new hierarchical decomposition method for imbalanced data sets which is different from previously proposed solutions to the class imbalance problem. Additionally, it does not require any data pre-processing step as many other solutions need. The new method is based on clustering and outlier detection. The hierarchy is constructed using the similarity of labeled data subsets at each level of the hierarchy with different levels being built by different data and feature subsets. Clustering is used to partition the data while outlier detection is utilized to detect minority class samples. The comparison of the proposed method with state of art the methods using 20 public imbalanced data sets and 181 synthetic data sets showed that the proposed method's classification performance is better than the state of art methods. It is especially successful if the minority class is sparser than the majority class. It has accurate performance even when classes have sub-varieties and minority and majority classes are overlapping. Moreover, its performance is also good when the class imbalance ratio is low, i.e. classes are more imbalanced.

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In this paper, an effective framework for detection of emergent leaders in small group is presented. In this scope, the combination of different types of nonverbal visual features; the visual focus of attention, head activity and body activity based features are utilized. Using them together ensued significant results. For the first time, multiple kernel learning (MKL) was applied for the identification of the most and the least emergent leaders. Taking the advantage of MKL's capability to use different kernels which corresponds to different feature subsets having different notions of similarity, significantly improved results compared to the state of the art methods were obtained. Additionally, high correlations between the majority of the features and the social psychology questionnaires which are designed to estimate the leadership or dominance were demonstrated.

Machine learning, a subfield of artificial intelligence, offers various methods that can be applied in marine science. It supports data-driven learning, which can result in automated decision making of de novo data. It has significant advantages compared with manual analyses that are labour intensive and require considerable time. Machine learning approaches have great potential to improve the quality and extent of marine research by identifying latent patterns and hidden trends, particularly in large datasets that are intractable using other approaches. New sensor technology supports collection of large amounts of data from the marine environment. The rapidly developing machine learning subfield known as deep learning—which applies algorithms (artificial neural networks) inspired by the structure and function of the brain—is able to solve very complex problems by processing big datasets in a short time, sometimes achieving better performance than human experts. Given the opportunities that machine learning can provide, its integration into marine science and marine resource management is inevitable. The purpose of this themed set of articles is to provide as wide a selection as possible of case studies that demonstrate the applications, utility, and promise of machine learning in marine science. We also provide a forward-look by envisioning a marine science of the future into which machine learning has been fully incorporated.

Recent research on the relationship between coral reef water temperature and fish swimming activity has stated that swimming speed is inversely correlated with temperature above a species' optimum temperature (Johansen, J. L., and Jones, G. P. 2011. Increasing ocean temperature reduces the metabolic performance and swimming ability of coral reef damselfishes. Global Change Biology, 17: 2971–2979; Johansen, J. L., Messmer,V., Coker, D. J., Hoey, A. S., and Pratchett, M. S. 2014. Increasing ocean temperatures reduce activity patterns of a large commercially important coral reef fish. Global Change Biology, 20: 1067–1074). For tropical coral reefs, one anticipated consequence of global warming is an increase of ≥3°C in average water temperature in addition to greater thermal fluctuations [IPCC (Intergovernmental Panel on Climate Change). 2007. Summary for policymakers. In Climate Change 2007: The Physical Science Basis. Contribution of Working, Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Ed. by S. Solomon, D. Qin, and M. Manning et al. Cambridge University Press, Cambridge, UK; Lough, J. 2007. Climate and climate change on the Great Barrier Reef. In Climate Change and the Great Barrier Reef. Ed. by J. Johnson and P. A. Marshall, pp. 15–50. Great Barrier Reef Marine Park Authority and Australian Greenhouse Office, Townsville, Qld, Australia; Johansen and Jones, 2011]. Evaluating the behaviour of coral reef associated fish species at different temperatures can help to assess their sensitivity to climate change. In this study, the speed of freely swimming fish in a natural setting is investigated as a function of seasonal changes in water temperature, as contrasted with systematic temperature increases in a fish tank. We show that Dascyllus reticulatus swim faster as a function of increased water temperature over the range 20.9–30.3°C. The experiments were carried out using ∼3.6 million fish trajectories observed at the Kenting National Park in Taiwan. Fish speed was computed by detecting and tracking the fish through consecutive video frames, then converting image speeds to scene speeds. Temperatures were grouped into 10 intervals. The data reveal an ∼2 mm s−1 increase in average speed per additional temperature degree over the range of 20.9–30.3°C. The Mann–Kendall test using the mean and median speed for each interval revealed that there is a speed increase trend as temperature increases at the 0.05 significance level, rather than a random increase. Our results complement previous studies that investigated the effect of temperature on the swimming performance of different fish species in the laboratory (Johansen and Jones, 2011; Myrick, C. A. and Cech, J. J. 2000. Swimming performance of four California stream fishes: temperature effects. Environmental Biology of Fishes, 58: 289–295; Ojanguren, A. F. and Braña, F. 2000. Thermal dependence of swimming endurance in juvenile brown trout. Journal of Fish Biology, 56: 1342–1347; Lough 2007; Johansen et al., 2014).