Return-Path: Delivered-To: diana@transocean.com Received: from vps.transocean.com by vps.transocean.com (Dovecot) with LMTP id 4Wt6JWyXWlilfQAAInt2oQ for ; Wed, 21 Dec 2016 06:53:32 -0800 Return-path: Envelope-to: diana@transocean.com Delivery-date: Wed, 21 Dec 2016 06:53:32 -0800 Received: from [69.195.148.54] (port=39263 helo=doghdpicture.com) by vps.transocean.com with esmtp (Exim 4.87) (envelope-from ) id 1cJiGo-0008LA-RL for diana@transocean.com; Wed, 21 Dec 2016 06:53:32 -0800 Date: Wed, 21 Dec 2016 09:48:56 -0500 Reply-To: "Glasses To Your Door" Message-Id: Mime-Version: 1 From: "Michael Barnett" Content-Type: multipart/alternative; boundary="Mz7535367880efcab8c324536e0c838e3bw" To: Spec-ID: 0000003.99034552.3581670-881991 Subject: Hate your old glasses? 1/2 off Holiday Sale. Pick, we ship, you wear, you love. X-Spam-Status: No, score=3.8 X-Spam-Score: 38 X-Spam-Bar: +++ X-Ham-Report: Spam detection software, running on the system "vps.transocean.com", has NOT identified this incoming email as spam. The original message has been attached to this so you can view it or label similar future email. If you have any questions, see root\@localhost for details. Content preview: https://www.media.mit.edu/wearables/mithril/memory-glasses.html http://www.doghdpicture.com/g/ Don't want to receive this type of email? end them here (http://cap.doghdpicture.com) . MEDDG Coding Updates 4523 E King Ave Phoenix AZ 85032 [...] Content analysis details: (3.8 points, 5.0 required) pts rule name description ---- ---------------------- -------------------------------------------------- 0.0 URIBL_BLOCKED ADMINISTRATOR NOTICE: The query to URIBL was blocked. See http://wiki.apache.org/spamassassin/DnsBlocklists#dnsbl-block for more information. [URIs: doghdpicture.com] 1.2 URIBL_ABUSE_SURBL Contains an URL listed in the ABUSE SURBL blocklist [URIs: doghdpicture.com] -0.0 SPF_PASS SPF: sender matches SPF record -0.0 SPF_HELO_PASS SPF: HELO matches SPF record 0.0 HTML_MESSAGE BODY: HTML included in message -0.0 BAYES_40 BODY: Bayes spam probability is 20 to 40% [score: 0.2785] 0.6 HTML_IMAGE_RATIO_04 BODY: HTML has a low ratio of text to image area 2.0 RDNS_NONE Delivered to internal network by a host with no rDNS 0.0 T_REMOTE_IMAGE Message contains an external image X-Spam-Flag: NO --Mz7535367880efcab8c324536e0c838e3bw Content-type: text/plain; charset=UTF-8 Content-transfer-encoding: 8bit https://www.media.mit.edu/wearables/mithril/memory-glasses.html http://www.doghdpicture.com/g/ Don't want to receive this type of email? end them here (http://cap.doghdpicture.com) . MEDDG Coding Updates 4523 E King Ave Phoenix AZ 85032 *-* Open Questions ------------------------------------------------------------ There are a number of open questions to be addressed in the construction of a proactive, context aware reminder system. These questions may be broken down as follows: * Defining Useful Context What is useful context for proactive reminders? Assuming that we cannot know everything about the wearer's state and actions, what are the most important things to know? Of these, which are technologically feasible to sense and classify? Time and location are obviously useful. In addition, it may be useful to know socially or logistically important features of the wearer's activity state, such as "in a conversation with person X" or "driving to work." * Context Sensing and Classification How do we sense and classify useful context? What types of sensors, signal processing, and inference techniques are necessary? What are the bandwidth and computational requirements for this classification? Time and location are relatively easy to sense and classify. Aspects of the wearer's activity state which are independent of time and location are harder. High-bandwidth, computationally expensive computer perception techniques may be used to provide infrastructure-free sensing and classification, and low-bandwidth low-computing power tag readers and tags may be used in cases where tagging infrastructure, people, and objects is feasible. Increasingly, we are seeing the value of medium-bandwidth sensing and real-time classification of signals such as accelerometer data, as described in the MIThril Real-Time Context Engine page (https://www.media.mit.edu/wearables/context/index.html) --Mz7535367880efcab8c324536e0c838e3bw Content-Type: text/html; charset=UTF-8 Content-transfer-encoding: 8bit
https://www.media.mit.edu/wearables/mithril/memory-glasses.html
 
 

Glasses-USA - 50%-Off - See all styles and frames...
 
 
 
 


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MEDDG Coding Updates
4523 E King Ave Phoenix AZ 85032


Open Questions

There are a number of open questions to be addressed in the construction of a proactive, context aware reminder system. These questions may be broken down as follows:
    • Defining Useful Context??What is useful context for proactive reminders? Assuming that we cannot know everything about the wearer's state and actions, what are the most important things to know? Of these, which are technologically feasible to sense and classify?

      Time and location are obviously useful. In addition, it may be useful to know socially or logistically important features of the wearer's activity state, such as "in a conversation with person X" or "driving to work."

    • Context Sensing and Classification??How do we sense and classify useful context? What types of sensors, signal processing, and inference techniques are necessary? What are the bandwidth and computational requirements for this classification?

      Time and location are relatively easy to sense and classify. Aspects of the wearer's activity state which are independent of time and location are harder. High-bandwidth, computationally expensive computer perception techniques may be used to provide infrastructure-free sensing and classification, and low-bandwidth low-computing power tag readers and tags may be used in cases where tagging infrastructure, people, and objects is feasible. Increasingly, we are seeing the value of medium-bandwidth sensing and real-time classification of signals such as accelerometer data, as described in the??MIThril Real-Time Context Engine page


 
 
 
 
 
 
 
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