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Human mobility has been studied extensively in various biomedical contexts with applications in clinical rehabilitation, disease diagnosis, health risk prognosis, and general performance assessments. In this paper, we present ATOMHP (Analytical Technologies to Objectively Measure Human Performance) Kinect: a system to objectively quantify human performance using the Microsoft Kinect as a single camera...
Crime prediction plays a crucial role in addressing crime, violence, conflict and insecurity in cities to promote good governance, appropriate urban planning and management. Plenty efforts have been made on developing crime prediction models by leveraging demographic data, but they failed to capture the dynamic nature of crimes in urban. Recently, with the development of new techniques for collecting...
Predictive Complex Event Processing (CEP) constitutes the next phase of CEP evolution and provides future predictive states of the partially matched complex sequences. In this paper, we demonstrate our novel predictive CEP system and show that this problem can be solved while leveraging existing data modelling, query execution and optimisation frameworks. We model the predictive detection of events...
Cyber-physical systems - systems that incorporate physical devices with cyber components - are appearing in diverse applications, and due to advances in data acquisition, are accompanied with large amounts of data. The interplay between the cyber and the physical components leaves such systems vulnerable to faults and intrusions, motivating the development of a general model that can efficiently and...
Cloud users have little visibility into the performance characteristics and utilization of the physical machines underpinning the virtualized cloud resources they use. This uncertainty forces users and researchers to reverse engineer the inner workings of cloud systems in order to understand and optimize the conditions their applications operate. At Massachusetts Open Cloud (MOC), as a public cloud...
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...
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...
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...
In recent years, companies and non-profit organizations are increasingly leveraging crowdsourcing platforms to seek novel solutions to data-driven problems. Crowdsourcing innovation problems to the data science community frequently take the form of public contests. Existing literature studied the mechanism and design of open innovation contests. However, the data science contests have a number of...
The most significant trend in human creativity is the shift from individual to teams. Great achievements across academic disciplines and industries are increasingly teamwork. Motivated by this, we aim to uncover teamwork in networks, including predicting teams' performance, optimizing teams' compositions and explaining the prediction results and optimization actions.
Overweight and obesity are ubiquitous around the world, and to make matters worse, many fatal diseases are related to obesity. Consequently, an increasing effort have been made to help people lose weight. Due to the cost-effective, convenient and wide-reaching features of mobile applications, smart phone weight loss applications have gained great public attention. Such weight loss applications typically...
In this paper, we propose a novel multilevel NER framework, for addressing the challenges of clinical name entity recognition, based on different machine learning and text mining algorithms. The proposed framework, with multiple levels, allows models for increasingly complex NER tasks to be built. The experimental evaluation on two different publicly available datasets, corresponding to different...
Exponential growth in electronic health record (EHR) data has resulted in new opportunities and urgent needs to discover meaningful data-driven representations and patterns of diseases, i.e., computational phenotyping. Recent success and development of deep learning provides promising solutions to the problem of prediction and feature discovery tasks, while lots of challenges still remain and prevent...
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...
In the literature, a number of methods have been proposed for semi-supervised learning. Recently, graph-based methods of semi-supervised learning have become popular because of their capability of handling large amounts of unlabeled data. However, the existing graph based semi-supervised learning algorithms do not optimize the process of selecting better labeled data. We have developed a new selective...
This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Significant challenges are the large size and high dimensionality of commercial data at...
Social surveys have been used by researchers and policy makers as an essential tool for understanding social and political activities in society. Social media has introduced a new way of capturing data from large numbers of people. Unlike surveys, social media deliver data more rapidly and cheaply. In this paper, we aim to rapidly identify socio-political activity in South Africa using proxy data...
The large adoption of Twitter during electioneering has created a valuable opportunity to monitor political deliberation nationwide. Recent work has analyzed online attention to forecast elections results addressing some limitations of opinion polling. However, the reproducibility of such methods remains a challenge given that most of them rely on the number of political parties or candidates mentions...
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...
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