49 research outputs found
EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
In recent years, advances in deep learning have resulted in unprecedented
leaps in diverse tasks spanning from speech and object recognition to context
awareness and health monitoring. As a result, an increasing number of
AI-enabled applications are being developed targeting ubiquitous and mobile
devices. While deep neural networks (DNNs) are getting bigger and more complex,
they also impose a heavy computational and energy burden on the host devices,
which has led to the integration of various specialized processors in commodity
devices. Given the broad range of competing DNN architectures and the
heterogeneity of the target hardware, there is an emerging need to understand
the compatibility between DNN-platform pairs and the expected performance
benefits on each platform. This work attempts to demystify this landscape by
systematically evaluating a collection of state-of-the-art DNNs on a wide
variety of commodity devices. In this respect, we identify potential
bottlenecks in each architecture and provide important guidelines that can
assist the community in the co-design of more efficient DNNs and accelerators.Comment: Accepted at MobiSys 2019: 3rd International Workshop on Embedded and
Mobile Deep Learning (EMDL), 201
Applications of Temporal Graph Metrics to Real-World Networks
Real world networks exhibit rich temporal information: friends are added and
removed over time in online social networks; the seasons dictate the
predator-prey relationship in food webs; and the propagation of a virus depends
on the network of human contacts throughout the day. Recent studies have
demonstrated that static network analysis is perhaps unsuitable in the study of
real world network since static paths ignore time order, which, in turn,
results in static shortest paths overestimating available links and
underestimating their true corresponding lengths. Temporal extensions to
centrality and efficiency metrics based on temporal shortest paths have also
been proposed. Firstly, we analyse the roles of key individuals of a corporate
network ranked according to temporal centrality within the context of a
bankruptcy scandal; secondly, we present how such temporal metrics can be used
to study the robustness of temporal networks in presence of random errors and
intelligent attacks; thirdly, we study containment schemes for mobile phone
malware which can spread via short range radio, similar to biological viruses;
finally, we study how the temporal network structure of human interactions can
be exploited to effectively immunise human populations. Through these
applications we demonstrate that temporal metrics provide a more accurate and
effective analysis of real-world networks compared to their static
counterparts.Comment: 25 page
Detecting network performance anomalies with contextual anomaly detection
Network performance anomalies can be defined as abnormal and significant variations in a network's traffic levels. Being able to detect anomalies is critical for both network operators and end users. However, the accurate detection without raising false alarms can become a challenging task when there is high variance in the traffic. To address this problem, we present in this paper a novel methodology for detecting performance anomalies based on contextual information. The proposed method is compared with the state of the art and is evaluated with high accuracy on both synthetic and real network traffic.Peer ReviewedPostprint (author's final draft
Large scale crowdsourcing and characterization of Twitter abusive behavior
In recent years online social networks have suffered an increase in sexism, racism, and other types of aggressive and cyberbullying behavior, often manifesting itself through offensive, abusive, or hateful language. Past scientific work focused on studying these forms of abusive activity in popular online social networks, such as Facebook and Twitter. Building on such work, we present an eight month study of the various forms of abusive behavior on Twitter, in a holistic fashion. Departing from past work, we examine a wide variety of labeling schemes, which cover different forms of abusive behavior. We propose an incremental and iterative methodology that leverages the power of crowdsourcing to annotate a large collection of tweets with a set of abuse-related labels.By applying our methodology and performing statistical analysis for label merging or elimination, we identify a reduced but robust set of labels to characterize abuse-related tweets. Finally, we offer a characterization of our annotated dataset
of 80 thousand tweets, which we make publicly available for further scientific exploration.Accepted manuscrip