/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

Autism spectrum disorder (ASD) research has yet to leverage “big data” on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as well as inform and guide researchers interested in expanding the body of clinically, computationally, and statistically sound approaches for mining ASD data.

/pdf/applications-of-supervised-machine-learning-in-autism-3sgp5hbt9y.pdf

Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review

Data mining techniques in social media

An overview of smartphone technology for citizen-centered, real-time and scalable civil infrastructure monitoring

Semantic access to streaming and static data at Siemens

In this demonstration, we are presenting SmartMonitor, a distributed Structural Health Monitoring (SHM) system consisting of smart devices. Over the last few years, the vast majority of smart devices is equipped with accelerometers that can be utilized towards building SHM systems with hundreds of nodes. We describe a scalable, fault-tolerant communication protocol, that performs best-effort time synchronization of the nodes and is used to implement a decentralized version of the popular peak-picking SHM method. The implemented interactive system can be easily installed in any accelerometer-equipped Android device and the user has a number of options for configuring the system or analyzing the collected data and computed outcomes.

/pdf/smartmonitor-using-smart-devices-to-perform-structural-4podwc2rpz.pdf

SmartMonitor: using smart devices to perform structural health monitoring

Many modern applications require the evaluation of analytical queries on large amounts of data. Such queries entail joins and heavy aggregations that often include user-defined functions (UDFs). The most efficient way to process these specific type of queries is using tree execution plans. In this work, we develop an engine for analytical query processing and a suite of specialized techniques that collectively take advantage of the tree form of such plans. The engine executes these tree plans in an elastic IaaS cloud infrastructure and dynamically adapts by allocating and releasing pertinent resources based on the query workload monitored over a sliding time window. The engine offers its services for a fee according to service-level agreements (SLAs) associated with the incoming queries; its management of cloud resources aims at maximizing the profit after removing the costs of using these resources. We have fully implemented our algorithms in the Exareme dataflow processing system. We present an extensive evaluation that demonstrates that our approach is very efficient (exhibiting fast response times), elastic (successfully adjusting the cloud resources it uses as the engine continually adapts to query workload changes), and profitable (approximating very well the maximum difference between SLA-based income and cloud-based expenses).

Elastic Processing of Analytical Query Workloads on IaaS Clouds

Users in social networks utilize hashtags for a variety of reasons. In many cases, hashtags serve retrieval purposes by labeling the content they accompany. More often than not, hashtags are used to promote content, ideas, or conversations producing viral memes. This paper addresses a specific case of hashtag classification: meme-filtering. We argue that hashtags that are correlated with memes may hinder many valuable social media algorithms like trend detection and event identification. We propose and evaluate a set of language-agnostic features that aid the separation of these two classes: meme-hashtags and event-hashtags. The proposed approach is evaluated on two large datasets of Twitter messages written in English and German. A proof-of-concept application of the meme-filtering approach to the problem of event detection is presented.

/pdf/language-agnostic-meme-filtering-for-hashtag-based-social-3neq6nbkus.pdf

Language agnostic meme-filtering for hashtag-based social network analysis

Users in social networks use hashtags for various reasons, some of them being serving search purposes, gaining attention or popularity or starting new conversation - thus, creating viral memes. In this paper we address the problem of classifying these hashtags in different categories, based on whether they represent a real life event or a social network generated meme. We compute a set of language-agnostic features to aid the classification of hashtags into events and memes and we provide an extensive study of the behavior that characterizes memes and events. We focus on Twitter social network, we apply our methods on a big dataset and reveal interesting characteristics of the two classes of hashtags.

#tag: meme or event?

Over the last few years, the vast majority of smart devices has been equipped with a variety of sensors, including accelation, light and gravity sensors, magnetometers etc. Moreover, mobile smart devices possess high computational power, storage in the order of gigabytes, whereas high battery capacity and high bandwidth are available. The biggest advantage of the wide presence of mobile smart devices is that all this distributed computing power is already at hands of people, being idle for the most time. This fact presents a chance of utilizing this distributed computational infrastructure with the goal of building participatory sensing systems with various applications for enviromental support, like health or structure monitoring. In this paper we are presenting a generic distributed framework consisting only of mobile smart devices and operating only in the network. We utilize a scalable, fault-tolerant communication protocol, that performs best-effort time synchronization of the nodes and present a first approach in an example application of distributed structural health monitoring (SHM).

Using smart mobile devices for monitoring in assistive environments

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