Efficient Kernel-Based Subsequence Search for Enabling Health Monitoring Services in IoT-Based Home Setting

This paper presents an efficient approach for subsequence search in data streams. The problem consists of identifying coherent repetitions of a given reference time-series, also in the multivariate case, within a longer data stream. The most widely adopted metric to address this problem is Dynamic Time Warping (DTW), but its computational complexity is a well-known issue. In this paper, we present an approach aimed at learning a kernel approximating DTW for efficiently analyzing streaming data collected from wearable sensors, while reducing the burden of DTW computation. Contrary to kernel, DTW allows for comparing two time-series with different length. To enable the use of kernel for comparing two time-series with different length, a feature embedding is required in order to obtain a fixed length vector representation. Each vector component is the DTW between the given time-series and a set of "basis" series, randomly chosen. The approach has been validated on two benchmark datasets and on a real-life application for supporting self-rehabilitation in elderly subjects has been addressed. A comparison with traditional DTW implementations and other state-of-the-art algorithms is provided: results show a slight decrease in accuracy, which is counterbalanced by a significant reduction in computational costs

a multi view sentiment corpus

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Named Entity Recognition: Resource Constrained Maximum Path

Information Extraction (IE) is a process focused on automatic extraction of structured information from unstructured text sources. One open research field of IE relates to Named Entity Recognition (NER), aimed at identifying and associating atomic elements in a given text to a predefined category such as names of persons, organizations, locations and so on. This problem can be formalized as the assignment of a finite sequence of semantic labels to a set of interdependent variables associated with text fragments, and can modelled through a stochastic process involving both hidden variables (semantic labels) and observed variables (textual cues). In this work we investigate one of the most promising model for NER based on Conditional Random Fields (CRFs). CRFs are enhanced in a two stages approach to include in the decision process logic rules that can be either extracted from data or defined by domain experts. The problem is defined as a Resource Constrained Maximum Path Problem (RCMPP) associating a resource with each logic rule. Proper resource Extension Functions (REFs) and upper bound on the resource consumptions are defined in order to model the logic rules as knapsack-like constraints. A well-tailored dynamic programming procedure is defined to address the RCMPP

The Internet of Responsibilities-Connecting Human Responsibilities using Big Data and Blockchain

Accountability in the workplace is critically important and remains a challenging problem, especially with respect to workplace safety management. In this paper, we introduce a novel notion, the Internet of Responsibilities, for accountability management. Our method sorts through the list of responsibilities with respect to hazardous positions. The positions are interconnected using directed acyclic graphs (DAGs) indicating the hierarchy of responsibilities in the organization. In addition, the system detects and collects responsibilities, and represents risk areas in terms of the positions of the responsibility nodes. Finally, an automatic reminder and assignment system is used to enforce a strict responsibility control without human intervention. Using blockchain technology, we further extend our system with the capability to store, recover and encrypt responsibility data. We show that through the application of the Internet of Responsibility network model driven by Big Data, enterprise and government agencies can attain a highly secured and safe workplace. Therefore, our model offers a combination of interconnected responsibilities, accountability, monitoring, and safety which is crucial for the protection of employees and the success of organizations

twine a real time system for tweet analysis via information extraction

In the recent years, the amount of user generated contents shared on the Web has significantly increased, especially in social media environment, e.g. Twitter, Facebook, Google+. This large quantity of data has generated the need of reactive and sophisticated systems for capturing and understanding the underlying information enclosed in them. In this paper we present TWINE, a real-time system for the big data analysis and exploration of information extracted from Twitter streams. The proposed system based on a Named Entity Recognition and Linking pipeline and a multi-dimensional spatial geo-localization is managed by a scalable and flexible architecture for an interactive visualization of micropost streams insights. The demo is available at http://twine-mind.cloudapp.net/streamin

Risk of classical Kaposi sarcoma by plasma levels of Epstein-Barr virus antibodies, sCD26, sCD23 and sCD30

<p>Abstract</p> <p>Background</p> <p>To clarify the immunological alterations leading to classical Kaposi sarcoma (cKS) among people infected with KS-associated herpesvirus (KSHV).</p> <p>Methods</p> <p>In a population-based study of 119 cKS cases, 105 KSHV-seropositive controls, and 155 KSHV-seronegative controls, we quantified plasma soluble cluster of differentiation (sCD) levels and antibodies against Epstein-Barr virus nuclear antigen-1 (anti-EBNA-1) and viral capsid antigen (anti-VCA). Differences between groups in prevalence of low-tertile anti-EBNA-1 and high-tertile anti-VCA were compared by logistic regression. Continuous levels between groups and by presence of cKS co-factors among controls were compared by linear regression and Mann-Whitney-Wilcoxon methods.</p> <p>Results</p> <p>Comparisons of cKS cases to seropositive controls and of seropositive to seronegative controls revealed no significant differences. However, controls with known cKS cofactors (male sex, nonsmoking, diabetes and cortisone use) had significantly lower levels of anti-EBNA (<it>P </it>= 0.0001 - 0.07) and anti-VCA (<it>P </it>= 0.0001 - 0.03). Levels of sCD26 were significantly lower for male and non-smoking controls (<it>P</it>_adj≤ 0.03), and they were marginally lower with older age and cortisone use (<it>P</it>_adj≤ 0.09).</p> <p>Conclusions</p> <p>Anti-EBV and sCD26 levels were associated with cofactors for cKS, but they did not differ between cKS cases and matched controls. Novel approaches and broader panels of assays are needed to investigate immunological contributions to cKS.</p

A telerehabilitation platform for cognitive, physical and behavioural rehabilitation in elderly patients affected by dementia

Dementia is one of the main causes of disability in elderly people and its treatment becomes, year after year, an increasingly compelling priority for the public health system. In the last years, home assistance and telemedicine have paved the way to decrease the treatments’ costs and to improve the patients and caregivers quality of life quality. In this framework, the aim of ABILITY project is to design, develop and validate an integrated platform of services aimed at supporting and enhancing the rehabilitation process for patients with dementia at their homes. ABILITY platform allows the clinician to assign rehabilitation plans with a strong compliance monitoring, enabled by the technological solutions integrated, and the holistic approach to rehabilitation, as the plan includes physical, cognitive and behavioral therapies/exercises. The ABILITY platform will be assessed through a set of validation activities, involving a small group of pilot patients, and a Randomized Control Trial. In conclusion, the ABILITY project generates a series of assistive services inside a modular and flexible platform, adaptable to the single patient and his/her needs, increasing the treatment efficiency and efficacy with respect to the state of the art