A Survey on Unusual Event Detection in Videos

As the usage of CCTV cameras in outdoor and indoor locations has increased significantly, one needs to design a system to detect the unusual events, at the time of its occurrence. Computer vision is used for Human Action recognition, which has been widely implemented in the systems, but unusual event detection is lately entering into the limelight. In order to detect the unusual events, supervised techniques, semi-supervised techniques and unsupervised techniques have been adopted. Social force model (SFM) and Force field are used to model the interaction among crowds. Only normal events training samples is not sufficient for detection of unusual events. Double sparse representation has been used as a solution to this, which includes normal and abnormal training data. To develop an intelligent video surveillance system, behavioural representation and behavioural modelling techniques are used. Various machine learning techniques to identify unusual events include: Graph modelling and matching, object trajectory based, object silhouettes based and pixel based approaches. Kullback–Leibler (KL) divergence, Quaternion Discrete Cosine Transformation (QDCT) analysis, hidden Markov model (HMM) and histogram of oriented contextual gradient (HOCG) descriptor are some of the models used are used for detecting unusual events. This paper briefly discusses the above mentioned strategies and pay attention on their pros and cons

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem becoming overwhelming. Moreover, it will aid in better decision-making to reduce wasted energy and promote sustainable and energy efficient behavior. In this regard, this paper is an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence. Specifically, an extensive survey is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. To the best of the authors' knowledge, this is the first review article that discusses anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumption, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, (iv) platforms for reproducibility and (v) privacy-preservation. Following, insights about current research trends are discussed to widen the applications and effectiveness of the anomaly detection technology before deriving future directions attracting significant attention. This article serves as a comprehensive reference to understand the current technological progress in anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table

Flow-3D CFD model of bifurcated open channel flow: setup and validation

Bifurcation is a morphological feature present in most of fluvial systems; where a river splits into two channels, each bearing a portion of the flow and sediments. Extensive theoretical studies of river bifurcations were performed to understand the nature of flow patterns at such diversions. Nevertheless, the complexity of the flow structure in the bifurcated channel has resulted in various constraints on physical experimentation, so computational modelling is required to investigate the phenomenon. The advantages of computational modelling compared with experimental research (e.g. simple variable control, reduced cost, optimize design condition etc.) are widely known. The great advancement of computer technologies and the exponential increase in power, memory storage and affordability of high-speed machines in the early 20th century led to evolution and wide application of numerical fluid flow simulations, generally referred to as Computational Fluid Dynamics {CFD). In this study, the open-channel flume with a lateral channel established by Momplot et al (2017) is modelled in Flow-3D. The original investigation on divided flow of equal widths as simulated in ANSYS Fluent and validated with velocity measurements

Soil Microbial Diversity Across Different Agroecological Zones in New South Wales

A synergistic relationship between soil diversity (pedodiversity) and soil microbial diversity (biodiversity) seems axiomatic. Soil microbes contribute with biogeochemical cycles on which rely soil services (e.g. food production) and; vice-versa the soil matrix provides the living conditions that structure its microbial communities. A better insight would enable us to quantify/qualify and so sustain, protect, and improve those processes underpinned by soil microorganisms. We hypothesize that the structural patterns of soil microbes rely on multivariate soil units and gradients (e.g. soil horizons) instead of single discrete ‘factors’ (soil pH) and that the microbial patterns can become a well-defined property of determined soils. We began exploring this biotic-abiotic dynamic by modeling soil microbial α-diversity using two orthogonal transects (~900 km each) across NSW. Soils were sampled from paired conserved and disturbed ecosystems. Soil biophysicochemistry was characterized using 16SrDNA/ITS metabarcoding and pedometric approaches. Soil microbial patterns and physicochemical attributes were assessed using linear and non-linear relationships (bootstraped regression trees models) whose output enabled the microbial mapping at 1km across NSW. These maps showed a higher diversity of soil microbes in western than eastern NSW. Despite this gradient, fungi and archaea were respectively lower and higher in Vertosols, whereas bacteria distribution was less clear. Our results suggested that microbial structural patterns relate to most pedological attributes and, the extent of this relationship varies according to the structural parameters analyzed (taxa composition, abundance, diversity metric). Therefore, microbial patterns are more consistent with grouped features defining soil gradients (soil types) rather than on individual soil properties. These conclusions will be supported by analyzing microbial and pedological dissimilarities (β-diversity) in a further research

Diversity and structure of Metrosideros polymorpha canopy arthropod communities across space and time

Global biodiversity is under pressure from climate change, habitat fragmentation and other anthropogenic change, and our ability to predict biodiversity responses to change requires a better understanding of the processes that drive diversity and structure local communities. However, quantifying these processes has proven to be challenging for multiple reasons; diversity is multidimensional, and both diversity and the processes that generate it vary across scale. In this dissertation, I examine temporal and spatial patterns in community structure to test hypotheses about the drivers of local diversity and composition in communities of varying age, focusing on arthropod communities associated with the native tree Metrosideros polymorpha on the Hawaiian Islands. Analysis of Hemiptera (true bug) communities reveals a temporal pattern in community structure, where young substrate communities were variable in species composition and beta dispersion decreased with substrate age, indicating convergence. However, substrate age did not correlate with community dissimilarity in a directional way. Similarly, geographic distance did not correlate with compositional dissimilarity, suggesting a lack of dispersal limitation. I confirmed this result by examining connections between arthropod communities in a historically fragmented ‘kīpuka’ landscape, using species-area relationships and graph theory analyses. Finally, if canopy arthropods are dispersive and differences in species composition across sites are not driven by substrate age, local habitat characteristics may influence species composition. I determined the role of local beta diversity and identified habitat characteristics regarding forest structure and host leaf traits that are strong drivers of beta diversity and species composition. Then, to further explore local habitat drivers I examined forests with high intraspecific variation in co-occurring Metrosideros. In this hybrid zone, insect life history traits shape species’ response to intraspecific variation in host plant characteristics, highlighting the importance of including dimensions of biodiversity beyond taxonomic diversity. Together, these results demonstrate the importance of local habitat conditions for canopy arthropods, suggest that canopy arthropod communities are highly connected and that substrate age plays a limited role in determining local arthropod communities. Such insights into biodiversity and plant-insect interactions across temporal and spatial scale are integral to understanding and conserving our natural world

Secondary use of electronic medical records for early identification of raised condition likelihoods in individuals: a machine learning approach

With many symptoms being common to multiple diseases, there is a challenge in producing an initial diagnosis or recommendation for diagnostic tests from a set of symptoms that could have been produced by a number of diseases. Often the initial choice of diagnosis or testing is based on a clinician’s impression of the likelihood of that condition in a general population; however the opportunity may exist for modification of these likelihoods based on individuals’ recorded medical histories. This data-driven approach utilises existing data and is thus cheap and non-invasive. A method is proposed by which an individual’s likelihoods of having specified medical conditions are modified by the similarity of that individual’s medical history to the medical histories of other individuals, comparing the prevalence of conditions in those other individuals’ records who are similar to the individual of interest versus the prevalence of the conditions in those individuals who are dissimilar. In order to maximise the number of records available for analysis, a process was developed for the merging of data from disparate sources that used different clinical coding systems, including extensive development of a technique for semi automatically mapping clinical events coded in ICD9-CM to Clinical Terms Version 3 (CTV3), for which no existing mapping table was found. Semantically similar fields in the source code sets were identified and retained in the combined data set. ‘Codelists’ comprising multiple CTV3 codes for a variety of conditions were built that defined the presence of those conditions within individual records. The hierarchical structure of the CTV3 code table was utilised as a method of identifying codes that differed in structure but had clinically similar or related meaning. The optimum degree of granularity of the coded data to use in identifying similar records was investigated and used in subsequent analysis. Two methods were used for discovering groups of similar and dissimilar individuals: the ‘nearest neighbours’ method and the grouping of records using a clustering process. Altered likelihoods for a range of conditions were investigated and results for the nearest-neighbours approach compared to the clustering approach. Results for adjusted condition likelihoods for 18 conditions are reported, together with a discussion of possible reasons for a change, or otherwise, in the condition likelihood, and a discussion of the clinical significance and potential use of information about such a change. logistic regressions performed on a selection of conditions KNN performed better than logistic regression when judged by F-score (or sensitivity and specificity separately), however situation more nuanced when looking at likelihood ratios: Logistic regression produced higher (better) positive likelihood ratios, but KNN produced lower (better) negative likelihood ratios. Logistic regression produced higher odds ratios

11th International Coral Reef Symposium Abstracts

https://nsuworks.nova.edu/occ_icrs/1001/thumbnail.jp

Dipterocarps protected by Jering local wisdom in Jering Menduyung Nature Recreational Park, Bangka Island, Indonesia

Apart of the oil palm plantation expansion, the Jering Menduyung Nature Recreational Park has relatively diverse plants. The 3,538 ha park is located at the north west of Bangka Island, Indonesia. The minimum species-area curve was 0.82 ha which is just below Dalil conservation forest that is 1.2 ha, but it is much higher than measurements of several secondary forests in the Island that are 0.2 ha. The plot is inhabited by more than 50 plant species. Of 22 tree species, there are 40 individual poles with the average diameter of 15.3 cm, and 64 individual trees with the average diameter of 48.9 cm. The density of Dipterocarpus grandiflorus (Blanco) Blanco or kruing, is 20.7 individual/ha with the diameter ranges of 12.1 – 212.7 cm or with the average diameter of 69.0 cm. The relatively intact park is supported by the local wisdom of Jering tribe, one of indigenous tribes in the island. People has regulated in cutting trees especially in the cape. The conservation agency designates the park as one of the kruing propagules sources in the province. The growing oil palm plantation and the less adoption of local wisdom among the youth is a challenge to forest conservation in the province where tin mining activities have been the economic driver for decades. More socialization from the conservation agency and the involvement of university students in raising environmental awareness is important to be done