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With an increase in the population of older adults, the number of cases with dementia also increases. People living with dementia (PLwD) exhibit various behavioral and psychological experiences; agitation and aggression being the most common. Aggressive patients with dementia can harm themselves, other patients and the staff. In the past, researchers have used actigraphy to detect incidences of agitation...
Virtual research environments (VRE) allow to collaboratively work on research projects. Typically, they provide a web-based interface to share documents and data for analysis. However, currently there is no support for earlier stages of research projects in the context of behaviour monitoring such as data collection. With the rise of solutions based on information and communication technologies (ICT),...
The standard dementia screening tool Mini Mental State Examination (MMSE) and the standard dementia staging tool Clinical Dementia Rating Scale (CDR) are prominent methods for answering questions whether a person might have dementia and about the dementia severity respectively. These methods are time consuming and require well-educated personnel to administer. Conversely, cognitive tests, such as...
As average life expectancy continuously rises, assisting the elderly population with living independently is of great importance. Detecting abnormal behaviour of the elderly living at home is one way to assist the eldercare systems with the increase of the elderly population. In this study, we perform an initial investigation to identify abnormal behaviour of household residents using energy consumption...
We propose a new variant of the Correlation-based Feature Selection (CFS) method for coping with longitudinal data – where variables are repeatedly measured across different time points. The proposed CFS variant is evaluated on ten datasets created using data from the English Longitudinal Study of Ageing (ELSA), with different age-related diseases used as the class variables to be predicted. The results...
In this article we address the problem of expanding the set of papers that researchers encounter when conducting bibliographic research on their scientific work. Using classical search engines or recommender systems in digital libraries, some interesting and relevant articles could be missed if they do not contain the same search key-phrases that the researcher is aware of. We propose a novel model...
It is very crucial for news aggregator websites which are recent in the market to actively engage its existing users. A recommendation system would help to tackle such a problem. However, due to the lack of sufficient amount of data, most of the state-of-the-art methods perform poorly in terms of recommending relevant news items to the users. In this paper, we propose a novel approach for Item-based...
Deep neural networks have yielded immense success in speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks for content based recommendation has received a relatively less amount of inspection. Also, different recommendation scenarios have their own issues which creates the need for different approaches for recommendation. One of the problems...
One of the most important and challenging problems in recommendation systems is that of modeling temporal behavior. Typically, modeling temporal behavior increases the cost of parameter inference and estimation. Along with it, it also poses the constraint of requiring a large amount of data for reliably learning the parameters of the model. Therefore, it is often difficult to model temporal behavior...
Attributes, such as metadata and profile, carry useful information which in principle can help improve accuracy in recommender systems. However, existing approaches have difficulty in fully leveraging attribute information due to practical challenges such as heterogeneity and sparseness. These approaches also fail to combine recurrent neural networks which have recently shown effectiveness in item...
Recently, heterogeneous information network(HIN) analysis has attracted a lot of attentions. One of the HIN application is recommendation. Due to HIN containing multiple different objects and links and rich semantic meanings, it is promising to generate better recommendation. Previous studies on movie recommendation have combined the single implicit feedback information with heterogeneous information...
Banks and financial institutions around the world must comply with several policies for the prevention of money laundering and in order to combat the financing of terrorism. Nowadays, there is a raise in the popularity of novel financial technologies such as digital currencies, social trading platforms and distributed ledger payments, but there is a lack of approaches to enforce the aforementioned...
Security is one of the top concerns of any enterprise. Most security practitioners in enterprises rely on correlation rules to detect potential threats. While the rules are intuitive to design, each rule is independently defined per log source, unable to collectively address heterogeneity of data from a myriad of enterprise networking and security logs. Furthermore, correlation rules do not look for...
Detecting security threats from compromised account or malicious insider by leveraging enterprise traffic logs is the goal of user behavior-based analytics. For its ease of interpretation, a common analytic indicator used in the industry for user behavior analytics is whether a user accesses a network entity, such as a machine or process, for the first time. While this popular indicator does correlate...
Recurrent neural network has been widely used as auto-regressive model for time series. The most commonly used training method for recurrent neural network is back propagation. However, recurrent neural networks trained with back propagation can get trapped at local minima and saddle points. In these cases, auto-regressive models cannot effectively model time series patterns. In order to address these...
Trading strategies basing on both financial analysis and machine learning techniques are becoming increasingly popular due to their ability to capture micro market price movements and leverage big data. An important class of works are focusing on exploiting the structural relationships between companies for accurate stock price prediction. In this paper we develop an algorithm for learning the parameters...
Feature selection, as a fundamental component of building robust models, plays an important role in many machine learning and data mining tasks. Since acquiring labeled data is particularly expensive in both time and effort, unsupervised feature selection on unlabeled data has recently gained considerable attention. Without label information, unsupervised feature selection needs alternative criteria...
We consider the problem of anomaly localization in a sensor network for multivariate time-series data by computing anomaly scores for each variable separately. To estimate the sparse Gaussian graphical models (GGMs) learned from different sliding windows of the dataset, we propose a new model wherein we constrain sparsity directly through L0 constraint and apply an additional L2 regularization in...
Feature selection is the process of selecting a subset of relevant features from the larger set of collected features. As the amount of available data grows with technology, feature selection becomes a more important part of the system-design process. In real-world applications, there are several costs associated with the collection, processing, and storage of data. Given that these costs can vary...
Support vector data description (SVDD) is a popular technique for detecting anomalies. The SVDD classifier partitions the whole space into an inlier region, which consists of the region near the training data, and an outlier region, which consists of points away from the training data. The computation of the SVDD classifier requires a kernel function, and the Gaussian kernel is a common choice for...
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