Thermopolitics of data: cloud infrastructures and energy futures

We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of videos, while our system detects more than 98% of them.

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Realized Volatility and Correlation in Energy Futures Markets

SSRN Electronic Journal ◽

10.2139/ssrn.1032322 ◽

2007 ◽

Author(s):

Tao Wang ◽

Jingtao Wu ◽

Jian Yang

Keyword(s):

Futures Markets ◽

Realized Volatility ◽

Energy Futures ◽

Energy Futures Markets

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Jump Spillover in Energy Futures Markets: The Bayesian Viewpoint

SSRN Electronic Journal ◽

10.2139/ssrn.1460492 ◽

2009 ◽

Author(s):

Qingfu Liu ◽

Anthony Tu

Keyword(s):

Futures Markets ◽

Energy Futures ◽

Energy Futures Markets

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Joint DOE/NRCan study of North American transportation energy futures: Discussion of the study, presentation of Phase 2 results

10.2172/1216571 ◽

2003 ◽

Author(s):

Philip Patterson ◽

David Greene ◽

Elyse Steiner ◽

Steve Plotkin ◽

Margaret Singh ◽

...

Keyword(s):

North American ◽

Phase 2 ◽

Transportation Energy ◽

Energy Futures

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GPU-Enabled Serverless Workflows for Efficient Multimedia Processing

Applied Sciences ◽

10.3390/app11041438 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1438

Author(s):

Sebastián Risco ◽

Germán Moltó

Keyword(s):

Video Processing ◽

Cost Effective ◽

Multimedia Processing ◽

Computing Services ◽

Video Frames ◽

Open Source Framework ◽

Cloud Infrastructures ◽

Aws Lambda ◽

Runtime Environments

Serverless computing has introduced scalable event-driven processing in Cloud infrastructures. However, it is not trivial for multimedia processing to benefit from the elastic capabilities featured by serverless applications. To this aim, this paper introduces the evolution of a framework to support the execution of customized runtime environments in AWS Lambda in order to accommodate workloads that do not satisfy its strict computational requirements: increased execution times and the ability to use GPU-based resources. This has been achieved through the integration of AWS Batch, a managed service to deploy virtual elastic clusters for the execution of containerized jobs. In addition, a Functions Definition Language (FDL) is introduced for the description of data-driven workflows of functions. These workflows can simultaneously leverage both AWS Lambda for the highly-scalable execution of short jobs and AWS Batch, for the execution of compute-intensive jobs that can profit from GPU-based computing. To assess the developed open-source framework, we executed a case study for efficient serverless video processing. The workflow automatically generates subtitles based on the audio and applies GPU-based object recognition to the video frames, thus simultaneously harnessing different computing services. This allows for the creation of cost-effective highly-parallel scale-to-zero serverless workflows in AWS.

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Imagining the smart city through smart grids? Urban energy futures between technological experimentation and the imagined low-carbon city

Urban Studies ◽

10.1177/00420980211005946 ◽

2021 ◽

pp. 004209802110059

Author(s):

Leslie Quitzow ◽

Friederike Rohde

Keyword(s):

Smart Grid ◽

Urban Development ◽

Smart City ◽

Smart Grids ◽

Urban Policy ◽

Radical Change ◽

Low Carbon ◽

Energy Futures ◽

Urban Energy ◽

The City

Current imaginaries of urban smart grid technologies are painting attractive pictures of the kinds of energy futures that are desirable and attainable in cities. Making claims about the future city, the socio-technical imaginaries related to smart grid developments unfold the power to guide urban energy policymaking and implementation practices. This paper analyses how urban smart grid futures are being imagined and co-produced in the city of Berlin, Germany. It explores these imaginaries to show how the politics of Berlin’s urban energy transition are being driven by techno-optimistic visions of the city’s digital modernisation and its ambitions to become a ‘smart city’. The analysis is based on a discourse analysis of relevant urban policy and other documents, as well as interviews with key stakeholders from Berlin’s energy, ICT and urban development sectors, including key experts from three urban laboratories for smart grid development and implementation in the city. It identifies three dominant imaginaries that depict urban smart grid technologies as (a) environmental solution, (b) economic imperative and (c) exciting experimental challenge. The paper concludes that dominant imaginaries of smart grid technologies in the city are grounded in a techno-optimistic approach to urban development that are foreclosing more subtle alternatives or perhaps more radical change towards low-carbon energy systems.

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Cost-Performance Evaluation of a Recognition Service of Livestock Activity Using Aerial Images

Remote Sensing ◽

10.3390/rs13122318 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2318

Author(s):

Darío G. Lema ◽

Oscar D. Pedrayes ◽

Rubén Usamentiaga ◽

Daniel F. García ◽

Ángela Alonso

Keyword(s):

Performance Evaluation ◽

Local Solution ◽

Aerial Images ◽

Cost Ratio ◽

Activity Detection ◽

Average Precision ◽

Cost Performance ◽

Detection Algorithms ◽

Cloud Infrastructures ◽

Animal Detection

The recognition of livestock activity is essential to be eligible for subsides, to automatically supervise critical activities and to locate stray animals. In recent decades, research has been carried out into animal detection, but this paper also analyzes the detection of other key elements that can be used to verify the presence of livestock activity in a given terrain: manure piles, feeders, silage balls, silage storage areas, and slurry pits. In recent years, the trend is to apply Convolutional Neuronal Networks (CNN) as they offer significantly better results than those obtained by traditional techniques. To implement a livestock activity detection service, the following object detection algorithms have been evaluated: YOLOv2, YOLOv4, YOLOv5, SSD, and Azure Custom Vision. Since YOLOv5 offers the best results, producing a mean average precision (mAP) of 0.94, this detector is selected for the creation of a livestock activity recognition service. In order to deploy the service in the best infrastructure, the performance/cost ratio of various Azure cloud infrastructures are analyzed and compared with a local solution. The result is an efficient and accurate service that can help to identify the presence of livestock activity in a specified terrain.

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