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Hackers expose the frailty of robots

In 2017, Lucas Apa and Cesar Cerrudo, security researchers at the consulting firm IOActive, showed that the version 2.5.5 of Pepper could be pirated via its software because of the vulnerabilities discovered when connecting to a network. They demonstrated that the robot could be controlled remotely, its manipulated members and its cameras used to spy on users. Yet, more than a year later, SoftBank did not correct the software, according to an analysis of its change logs by Mr. Apa. He told the FT that the Japanese conglomerate had told him he could not solve the problem. He said: “We were very disappointed with this response, but we understand that with any new technology, it is very difficult for manufacturers to get the attention or investment they need.” Hackers expose the frailty of robots SoftBank says that users have been asked to maintain the security of the Wi-Fi network and to correctly set the robot’s passwords. “We will continue to improve our security measures on
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5 Steps For Securing Big Data Infrastructure

Enterprises need to know what they design about big data environments. For IT executives responsible for modernizing big data infrastructure and embracing cloud storage, the weekly rhythm of security news has become a recurring nightmare. For example, the genealogy website “MyHeritage” acknowledged that a security breach had led to the leak of email addresses and hashed passwords of more than 92 million users. In addition to self-inflicted breaches, big data leaders are increasingly concerned about regulatory compliance. The European Union’s General Data Protection Regulation (GDPR) is in effect, enterprises can face fines as much as 4 percent of their annual sales if they violate the data security regulations. Securing Big Data Infrastructure The task of modernizing big data storage and deploying new cloud-based solutions has never seemed more perilous. The fallout from a security breach or data leak can embroil a company legally and financially and burn its reputation with cu
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Global Market Analysis & Forecast 2013-2023

Global big data analytics in the healthcare market is gaining interest with the introduction of personalized healthcare systems and demand for high-quality healthcare services. With the adoption of big data, healthcare payers and suppliers increased their capabilities. They study the patient behavior in a particular treatment and the diagnostic patterns. This provides them customized and highly cost-effective services. Categorization of Healthcare Market: Based on the deployment type, big data analytics in the healthcare market is categorized into on-demand and on-premise. Of the two, the on-demand deployment generates a larger revenue compared to the on-premise software deployment. Based on the hardware, big data analytics within the healthcare market is categorized into data storage, routers, firewalls, virtual private network, e-mail servers, and others; others include data centers hardware and on-premise and on-cloud hardware. Data storage accounted for the biggest revenue
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artificial intelligence job market

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Financial analytics sealed the biggest market share in 2017. It provides healthcare organizations with greater and better visibility into several factors. It drives revenues, reduces operational costs and manages shareholder value. The application uses sophisticated software technology such as specialized billing process and knowledge, to keep a track of various claims processed, and the amount of revenue collected from different segments.  artificial intelligence job market The major players are developing new solutions for healthcare data analytics. Key players operating in the big data analytics in healthcare market are McKesson Corporation, Cognizant Technology Solutions Corporation, Epic System Corporation, Cerner Corporation, Dell Inc., GE Healthcare, Siemens AG, Koninklijke Philips N.V., and Xerox Corporation. Most of the main vendors within the big data analytics in healthcare market are actively focusing on enhancing their offerings. This also includes software develop
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bigdata appplication in health care

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Geographical Big Data Analytics: Geographically, North America has been the largest big data analytics in the healthcare market. Asia-Pacific is expected as the fastest growth in the forecast period. The common factors include advancement in big data technologies, growing digitalization, expanding data analytics software industry, and increasing adoption of big data analytics services by health care providers in the region. Due to various initiatives were undertaken by the government, the market in the region is expected to grow significantly in the coming years. This makes more sophisticated and cheap healthcare services available to people. Factors driving the growth of big data analytics in healthcare market include  bigdata appplication in health care A major increase in demand for financial analytics in healthcare. Demand for exploring structured and unstructured data present in the healthcare industry. Decrease the prices and handiness of big data software system and se
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benefits of big data in healthcare

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Global big data analytics in the healthcare market is gaining interest with the introduction of personalized healthcare systems and demand for high-quality healthcare services. With the adoption of big data, healthcare payers and suppliers increased their capabilities. They study the patient behavior in a particular treatment and the diagnostic patterns. This provides them customized and highly cost-effective services bigdata appplication in health care Categorization of Healthcare Market: Based on the deployment type, big data analytics in the healthcare market is categorized into on-demand and on-premise. Of the two, the on-demand deployment generates a larger revenue compared to the on-premise software deployment. Based on the hardware, big data analytics within the healthcare market is categorized into data storage, routers, firewalls, virtual private network, e-mail servers, and others; others include data centers hardware and on-premise and on-cloud hardware. Data
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