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Texas Chainsaw (Blu-ray disc): Alexandra Daddario, Tania Raymonde, Scott Eastwood, Bill Moseley, Richard Riehle, Shaun Sipos,... Texas Chainsaw (Blu-ray disc)
Alexandra Daddario, Tania Raymonde, Scott Eastwood, Bill Moseley, Richard Riehle, … 1
R137 Discovery Miles 1 370 In stock

The seventh outing in the classic slasher horror franchise sees the return of chainsaw-wielding killer Leatherface. Alexandra Daddario stars as Heather, a young woman who travels to Texas with her boyfriend Ryan (Trey Songz) and two other friends to collect a family inheritance. On arrival, the friends realise to their horror that Heather's legacy includes the unwanted attentions of crazed murderer Leatherface (Dan Yeager) and his cannibalistic clan. The film features a cameo appearance from Gunnar Hansen, who played Leatherface in the original 1974 film.

Texas Chainsaw (DVD): Alexandra Daddario, Tania Raymonde, Scott Eastwood, Bill Moseley, Richard Riehle, Shaun Sipos, Thom... Texas Chainsaw (DVD)
Alexandra Daddario, Tania Raymonde, Scott Eastwood, Bill Moseley, Richard Riehle, … 1
R101 Discovery Miles 1 010 Ships in 10 - 15 working days

The seventh outing in the classic slasher horror franchise sees the return of chainsaw-wielding killer Leatherface. Alexandra Daddario stars as Heather, a young woman who travels to Texas with her boyfriend Ryan (Trey Songz) and two other friends to collect a family inheritance. On arrival, the friends realise to their horror that Heather's legacy includes the unwanted attentions of crazed murderer Leatherface (Dan Yeager) and his cannibalistic clan. The film features a cameo appearance from Gunnar Hansen, who played Leatherface in the original 1974 film.

Crowdsourced Data Management - Industry and Academic Perspectives (Paperback): Adam Marcus, Aditya Parameswaran Crowdsourced Data Management - Industry and Academic Perspectives (Paperback)
Adam Marcus, Aditya Parameswaran
R2,336 Discovery Miles 23 360 Ships in 7 - 11 working days

Crowdsourcing and human computation enable organizations to accomplish tasks that are currently not possible for fully automated techniques to complete, or require more flexibility and scalability than traditional employment relationships can facilitate. In the area of data processing, companies have benefited from crowd workers on platforms such as Amazon's Mechanical Turk or Upwork to complete tasks as varied as content moderation, web content extraction, entity resolution, and video/audio/image processing. Several academic researchers from diverse areas, ranging from the social sciences to computer science, have embraced crowdsourcing as a research area, resulting in algorithms and systems that improve crowd work quality, latency, and cost. Despite the relative nascence of the field, the academic and the practitioner communities have largely operated independently of each other for the past decade, rarely exchanging techniques and experiences. Crowdsourced Data Management aims to narrow the gap between academics and practitioners. On the academic side, it summarizes the state of the art in crowd-powered algorithms and system design tailored to large-scale data processing. On the industry side, it surveys 13 industry users - such as Google, Facebook, and Microsoft - and four marketplace providers of crowd work - such as CrowdFlower and Upwork - to identify how hundreds of engineers and tens of million dollars are invested in various crowdsourcing solutions. It simultaneously introduces academics to real problems that practitioners encounter every day, and provides a survey of the state of the art for practitioners to incorporate into their designs. Through the surveys, it also highlights the fact that crowdpowered data processing is a large and growing field. Over the next decade, most technical organizations are likely to benefit in some way from crowd work, and this monograph can help guide the effective adoption of crowdsourcing across these organizations.

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