Transportation mode recognition fusing wearable motion, sound and vision sensors

We present the first work that investigates the potential of improving the performance of transportation mode recognition through fusing multimodal data from wearable sensors: motion, sound and vision. We first train three independent deep neural network (DNN) classifiers, which work with the three types of sensors, respectively. We then propose two schemes that fuse the classification results from the three mono-modal classifiers. The first scheme makes an ensemble decision with fixed rules including Sum, Product, Majority Voting, and Borda Count. The second scheme is an adaptive fuser built as another classifier (including Naive Bayes, Decision Tree, Random Forest and Neural Network) that learns enhanced predictions by combining the outputs from the three mono-modal classifiers. We verify the advantage of the proposed method with the state-of-the-art Sussex-Huawei Locomotion and Transportation (SHL) dataset recognizing the eight transportation activities: Still, Walk, Run, Bike, Bus, Car, Train and Subway. We achieve F1 scores of 79.4%, 82.1% and 72.8% with the mono-modal motion, sound and vision classifiers, respectively. The F1 score is remarkably improved to 94.5% and 95.5% by the two data fusion schemes, respectively. The recognition performance can be further improved with a post-processing scheme that exploits the temporal continuity of transportation. When assessing generalization of the model to unseen data, we show that while performance is reduced - as expected - for each individual classifier, the benefits of fusion are retained with performance improved by 15 percentage points. Besides the actual performance increase, this work, most importantly, opens up the possibility for dynamically fusing modalities to achieve distinct power-performance trade-off at run time

BlogForever: D2.5 Weblog Spam Filtering Report and Associated Methodology

This report is written as a first attempt to define the BlogForever spam detection strategy. It comprises a survey of weblog spam technology and approaches to their detection. While the report was written to help identify possible approaches to spam detection as a component within the BlogForver software, the discussion has been extended to include observations related to the historical, social and practical value of spam, and proposals of other ways of dealing with spam within the repository without necessarily removing them. It contains a general overview of spam types, ready-made anti-spam APIs available for weblogs, possible methods that have been suggested for preventing the introduction of spam into a blog, and research related to spam focusing on those that appear in the weblog context, concluding in a proposal for a spam detection workflow that might form the basis for the spam detection component of the BlogForever software

Towards Data-Driven Autonomics in Data Centers

Continued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, and at that point, the intervention of humans will be limited to setting high-level goals and policies rather than performing low-level operations. Data-driven autonomics, where management and control are based on holistic predictive models that are built and updated using generated data, opens one possible path towards limiting the role of operators in data centers. In this paper, we present a data-science study of a public Google dataset collected in a 12K-node cluster with the goal of building and evaluating a predictive model for node failures. We use BigQuery, the big data SQL platform from the Google Cloud suite, to process massive amounts of data and generate a rich feature set characterizing machine state over time. We describe how an ensemble classifier can be built out of many Random Forest classifiers each trained on these features, to predict if machines will fail in a future 24-hour window. Our evaluation reveals that if we limit false positive rates to 5%, we can achieve true positive rates between 27% and 88% with precision varying between 50% and 72%. We discuss the practicality of including our predictive model as the central component of a data-driven autonomic manager and operating it on-line with live data streams (rather than off-line on data logs). All of the scripts used for BigQuery and classification analyses are publicly available from the authors' website.Comment: 12 pages, 6 figure

Towards Operator-less Data Centers Through Data-Driven, Predictive, Proactive Autonomics

Continued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, and at that point, the intervention of humans will be limited to setting high-level goals and policies rather than performing low-level operations. Data-driven autonomics, where management and control are based on holistic predictive models that are built and updated using live data, opens one possible path towards limiting the role of operators in data centers. In this paper, we present a data-science study of a public Google dataset collected in a 12K-node cluster with the goal of building and evaluating predictive models for node failures. Our results support the practicality of a data-driven approach by showing the effectiveness of predictive models based on data found in typical data center logs. We use BigQuery, the big data SQL platform from the Google Cloud suite, to process massive amounts of data and generate a rich feature set characterizing node state over time. We describe how an ensemble classifier can be built out of many Random Forest classifiers each trained on these features, to predict if nodes will fail in a future 24-hour window. Our evaluation reveals that if we limit false positive rates to 5%, we can achieve true positive rates between 27% and 88% with precision varying between 50% and 72%.This level of performance allows us to recover large fraction of jobs' executions (by redirecting them to other nodes when a failure of the present node is predicted) that would otherwise have been wasted due to failures. [...

Passport: Enabling Accurate Country-Level Router Geolocation using Inaccurate Sources

When does Internet traffic cross international borders? This question has major geopolitical, legal and social implications and is surprisingly difficult to answer. A critical stumbling block is a dearth of tools that accurately map routers traversed by Internet traffic to the countries in which they are located. This paper presents Passport: a new approach for efficient, accurate country-level router geolocation and a system that implements it. Passport provides location predictions with limited active measurements, using machine learning to combine information from IP geolocation databases, router hostnames, whois records, and ping measurements. We show that Passport substantially outperforms existing techniques, and identify cases where paths traverse countries with implications for security, privacy, and performance

Passport: enabling accurate country-level router geolocation using inaccurate sources

When does Internet traffic cross international borders? This question has major geopolitical, legal and social implications and is surprisingly difficult to answer. A critical stumbling block is a dearth of tools that accurately map routers traversed by Internet traffic to the countries in which they are located. This paper presents Passport: a new approach for efficient, accurate country-level router geolocation and a system that implements it. Passport provides location predictions with limited active measurements, using machine learning to combine information from IP geolocation databases, router hostnames, whois records, and ping measurements. We show that Passport substantially outperforms existing techniques, and identify cases where paths traverse countries with implications for security, privacy, and performance.First author draf