I have a new follower on Twitter

Bonnie Thornton
Нello ;) I'm lоoking for a sex. Cаll me, сhеck http://ift.tt/2yJsnLS


Following: 98 - Followers: 29

October 21, 2017 at 05:00PM via Twitter http://twitter.com/Gabrielle1222

Anonymous make-out - m4w

Here's the scenario. We meet in a public place. Decide if we are into each other. If so, we find a more private spot and make-out, then go our separate ...

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Anonymous of Tokyo

She had the life of 115 years 196 days and She known for the oldest Japanese person and oldest anonymous .Since the death of. her Nabi Tajima ...

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Maybe It's Time to Do Away with Anonymous Reviews

Maybe It's Time to Do Away with Anonymous Reviews. The latest Kirkus fiasco might not have been such a big deal if reviewers signed their names.

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New Rapidly-Growing IoT Botnet Threatens to Take Down the Internet

Just a year after Mirai—biggest IoT-based malware that caused vast Internet outages by launching massive DDoS attacks—completed its first anniversary, security researchers are now warning of a brand new rapidly growing IoT botnet. Dubbed 'IoT_reaper,' first spotted in September by researchers at firm Qihoo 360, the new malware no longer depends on cracking weak passwords; instead, it exploits

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Incogneato Anonymous Box

Description. This plugin allows you to quickly add your existing Incogneato anonymous suggestion box to your WordPress site. Incogneato is simple ...

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Accountant

Accountant in Accounting and public practice, Accountant with Anonymous. Apply Today.

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Working At Anonymous Production - Ask a Question

Post questions about Anonymous Production's Company Culture, answered by Employees at Anonymous Production. See the 1 total questions asked ...

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Lynds Dark Nebula 183

Beverly Lynds Dark Nebula 183 lies a mere 325 light-years away, drifting high above the plane of our Milky Way Galaxy. Obscuring the starlight behind it when viewed at optical wavelengths, the dark, dusty molecular cloud itself seems starless. But far infrared explorations reveal dense clumps within, likely stars in the early stages of formation as enhanced regions of the cloud undergo gravitational collapse. One of the closest molecular clouds, it is seen toward the constellation Serpens Caput. This sharp cosmic cloud portrait spans about half a degree on the sky. That's about 3 light-years at the estimated distance of Lynds Dark Nebula 183. via NASA http://ift.tt/2x9BMbW

Alcoholics Anonymous – Di and Stu

Growing up in a family that drank and as an angry and rebellious girl, Di started drinking with friends in her early teens. She continued drinking and ...

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Node author always anonymous

Hi, I just started using this great module. When I create a node using a Flexiform, the author is always anonymous. Even when logged in. It is worth ...

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I have a new follower on Twitter

Soren S Hansen
Host of Tribe Of Entrepreneurs podcast. Interview with entrepreneurs to help you take action https://t.co/GGbU46jxp9 To get featured https://t.co/dla880FSyT
Copenhagen, Denmark
https://t.co/OHdoXKjr2b
Following: 6111 - Followers: 7084

October 20, 2017 at 03:47PM via Twitter http://twitter.com/sorenshansen

Flix Anonymous 45 - CKNW Original Podcasts

This week Trev and Steve riff about all the negative reviews The Snowman is getting. There is a great new TV series on Netflix called Mindhunterand a ...

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[FD] SSD Advisory – Endian Firewall Stored From XSS to Remote Command Execution

SSD Advisory – Endian Firewall Stored From XSS to Remote Command Execution Full report: http://ift.tt/2xNVJot Twitter: @SecuriTeam_SSD Weibo: SecuriTeam_SSD Vulnerability Summary The following advisory describes a stored cross site scripting that can be used to trigger remote code execution in Endian Firewall version 5.0.3. Endian Firewall is a “turnkey Linux security distribution, which is an independent, unified security management operating system. The Endian Firewall is based on a hardened Linux operating system.” Credit An independent security researcher has reported this vulnerability to Beyond Security’s SecuriTeam Secure Disclosure program. Vendor response Endian has released patches to address this vulnerability. For more information: http://ift.tt/2xUHLGb

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[FD] [RCE] TP-Link Remote Code Execution CVE-2017-13772

** Advisory Information Title: TP-Link Remote Code Execution Blog URL: http://ift.tt/2yCaPRM Vendor: TP-Link Date Published: 19/10/2017 CVE: CVE-2017-13772 ** Vulnerability Summary Numerous remote code execution paths were discovered in TP-Link's WR940N home WiFi router. Valid credentials are required for this attack path. It is possible for an authenticated attacker to obtain a remote shell with root privileges. ** Details There were multiple occurrences of strcpy being used in an unsafe manner, resulting in a trivial buffer overflow condition. It is also possible to cause a Denial of Service on the web service. Using the ‘Diagnostic’ page, an attacker could utilise the built in ‘ping’ feature of the router to cause either; a Denial of Service attack to crash the web server or exploit a buffer overflow condition to obtain a remote root shell. ** Vendor Response TP-Link have released a new version of the firmware thus mitigating exploitation of this issue. ** Report Timeline * Disclosed to vendor – 11/8/2017 * Response from vendor, request for initial advisory – 14/8/2017 * Initial advisory sent – 14/8/2017 * Beta patch sent for testing by vendor – 17/8/2017 * Patch confirmed to work, however other vulnerable locations were identified, a second exploit was written to demonstrate this. Sent to vendor – 17/8/2017 * Response by vendor, will look into the other vulnerable locations – 18/8/2017 * Second patch sent for testing by vendor – 25/8/17 * Patch confirmed to mitigate vulnerabilities (500+ calls to strcpy removed) – 29/8/2017 * Patch released – 28/9/2017 (Only HW V5 US) ** Credit This vulnerability was discovered by Tim Carrington, part of the Fidus Information Security research team. ** References http://ift.tt/2yCaPRM ** Disclaimer This advisory is licensed under a Creative Commons Attribution Non-Commercial Share-Alike 3.0 License: http://ift.tt/e9zF8v

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I have a new follower on Twitter

Veille Social Media
EMarketing - Veille quotidienne des réseaux sociaux : #FacebookExpert #TwitterExpert #CM #SocialMedia #Ereputation #Com #Mkg
Paris

Following: 15187 - Followers: 30715

October 20, 2017 at 01:07PM via Twitter http://twitter.com/Marie_Veille

[FD] CVE-2017-12579 Local root privesc in Hashicorp vagrant-vmware-fusion 4.0.24

I have previously disclosed a couple of bugs in Hashicorp's vagrant-vmware-fusion plugin for vagrant. Unfortunately the 4.0.23 release which was supposed to fix the previous bug I reported didn't address the issue, so Hashicorp quickly put out another release - 4.0.24 - after that (but didn't update the public changelog on github). Unfortunately 4.0.24 is still vulnerable, largely due to a fundamental design flaw in the way the plugin is written combined with the need to elevate privileges for certain functions within Fusion. Because Hashicorp need users to be able to update the plugin as the local non-root user the encrypted ruby code that the plugin is comprised of must remain owned by the non-root user. This means there is a huge attack surface that we can exploit to manipulate the execution of the program and still get root on 4.0.24. I wrote this exploit before Fusion 10 was released and on the surface 4.0.24 is not compatible with Fusion 10. Curiously though it can be fairly easily tricked into working (at least partially) with Fusion 10 simply by patching out the version check and creating a symlink. I discovered this while trying to get the 4.0.24 exploit working with Fusion 10 installed - we can simply monkey-patch the version check out of the code, create a symlink for a binary that VMWare moved in v10 and then we're away. I was able to vagrant up and ssh into the running vm without any issues. It also means I was able to update the exploit so that it works on Fusion 8.x and Fusion 10. This seems to be (finally!) fixed properly in 4.0.25 by replacing the suid helper binary with a new go binary that contains all the required elevated operations and doesn't call back to the vulnerable ruby code. ref: http://ift.tt/2gxo3IO

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ISS Daily Summary Report – 10/19/2017

Circadian Rhythms:  Yesterday a crewmember instrumented themselves with Thermolab Double Sensors and mounted the Thermolab Unit to their belt, which began 36 hours of monitoring for the Circadian Rhythm investigation.  Circadian Rhythms investigates the role of synchronized circadian rhythms, or the “biological clock,” and how it changes during long-duration spaceflight. Researchers hypothesize that a non-24-hour cycle of light and dark affects crew members’ circadian clocks. The investigation also addresses the effects of reduced physical activity, microgravity and an artificially controlled environment. Changes in body composition and body temperature, which also occur in microgravity, can affect crew members’ circadian rhythms as well. Understanding how these phenomena affect the biological clock will improve performance and health for future crew members. Fine Motor Skills (FMS): Today a 51S crewmember completed a Flight Day 85 FMS session which was executed on a touchscreen tablet, where the subject performs a series of interactive tasks. The investigation studies how fine motor skills are affected by long-term microgravity exposure, different phases of microgravity adaptation, and sensorimotor recovery after returning to Earth gravity. The goal of FMS is to answer how fine motor performance in microgravity trend/vary over the duration of a six-month and year-long space mission; how fine motor performance on orbit compares with that of a closely matched participant on Earth; and how performance trends/varies before and after gravitational transitions, including the periods of early flight adaptation, and very early/near immediate post-flight periods.  Space Headaches:  The crew completed the weekly questionnaire for the ESA Space Headaches investigation. The Space Headaches investigation collects information that may help in the development of methods to alleviate associated symptoms and improvement in the well-being and performance of crewmembers in space. Headaches during space flight can negatively affect mental and physical capacities of crewmembers that can influence performance during a space mission.  USOS Extra-Vehicular Activity (EVA) #46 Preparations:  Today the crew conducted a review of EVA procedures and a conference with ground teams.  They also inspected tethers, configured tools, printed cuff checklists, and prepared the equipment lock in preparation for tomorrow’s EVA. The goals of USOS EVA #46 include Removal and Replacement (R&R) of the Latching End Effector (LEE) A Camera Lens Assembly (CLA). Tablet Updates:  As part of the ongoing efforts to update the operating system on the onboard tablets, the crew performed steps to update and configure those tablets. Remote Power Control Module (RPCM) P12B-B R&R:  Wednesday morning, ground teams completed the robotic activities to swap RPCM P12B-B with P13A-G.  On Monday night RPCM P12B-B was extracted and installed into empty truss slot P11A-D and the replacement RPCM was removed from slot P13A-G, however it could not be inserted into the P12B-B at that time.  Robotics activities resumed on Tuesday afternoon and teams were able to successfully install the replacement RPCM in the P12B-B slot after 47 wiggle and push attempts.  The new P12B-B RPCM powered up nominally.  Ground teams then removed the failed RPCM from the P11A-D truss slot and relocated it to the P13A-B slot successfully.  The power up of the P13A-G RPCM was also performed nominally.  This completes the RPCM swap activities. JEM Airlock (A/L) Reconfiguration:  Today, the crew removed the MBSU from the JEM A/L where it had been stowed after the I-Level maintenance was performed in early September.  The Kaber plate and adapter plate were then installed on the A/L slide table in preparation for installation of the KE2M satellite on Monday.  After KE2M and SIMPL, another JEM A/L-deployed satellite, are deployed next week, the MBSU will be returned to the JEM A/L for transfer and installation externally at External Stowage Platform 2 (ESP2).  External Robotics Operations:  Overnight, robotics ground controllers stowed the Special Purpose Dexterous Manipulator (SPDM) and configured the SSRMS to be in position for the EVA tomorrow.  The SSRMS is now based on LEE B, at Mobile Base System (MBS) Power Data Grapple Fixture 4 (PDGF), Worksite 6. LEE A is ready to receive a new LEE CLA. Today’s Planned Activities All activities were completed as scheduled unless otherwise noted. Extravehicular Activity (EVA) Reminder for EVA In-Suit Light Exercise (ISLE) Preparation US Extravehicular Activity (EVA) Tether Inspection Fine Motor Skills Experiment Test – Subject ISS crew and ГОГУ (RSA Flight Control Management Team) weekly conference(S-band) MICROVIR. Retrieval of Cassette-M No.3-3 and No.3-4 from ТБУ-В No.05 and setup on interior panel. Photography of upper cells. Tagup with specialists Hardware Gather for Kestrel Eye 2M (KE2M) Installation on JEM Airlock Slide Table iPad Air 2 Install Part 1 Installation of protective guard on ПН28-120 voltage converter on the RS. Note 8 iPad Air 2 Food Intake Tracker (FIT) Update Extravehicular Mobility Unit (EMU) Cuff Checklist Print MICROVIR. Photography of Cassette-M lower cells No.3-1, No.3-2 and stowage in ТБУ-В No.05 at +4°С XF305 Camcorder Setup JEM Airlock Slide Table (ST) Extension to JPM Side PRODUTSENT. Removal from ТБУ-В No.02 (+29 °С) and Transfer to ТБУ-В No.05 at +4° C In Flight Maintenance (IFM) Main Bus Switching Unit (MBSU) Cover Build PRODUTSENT. Deactivation and Removal of ТБУ-В S/N 02 Main Bus Switching Unit (MBSU) Removal from JEM Airlock Slide Table Miscellaneous Hardware Cleanup Progress 437 (DC1) Priority and US Cargo Items Transfers and IMS Ops JEM ORU Transfer Interface (JOTI) from the JEM Airlock Slide Table BIOPLENKA. Removal of samples from thermostat, fixation of Constanta cassette No.4-6 biofilm. Tagup with specialists Collect SM and FGB air samples using АК-1М sampler Extravehicular Activity (EVA) Tool Configuring JEM Airlock Kaber Plate and Adapter Plate Installation ИПД Air Sample Collection for Ammonia in SM SM Air Sampling for FREON Using АК-1М Sampler JEM Airlock Slide Table (ST) Retraction from JPM Side MATRYOSHKA-R. Deployment of Tritel P/L. Configure electrical connections. P/L activation. Extravehicular Activity (EVA) Tool Audit. ESA Weekly crew conference ISS HAM Service Module Pass Environmental Health System (EHS) – Coliform Water Sample Analysis 44 +/- 4 hours post processing Crew Medical Officer (CMO) On Board Training (OBT) Replacement of СО filter unit in gas analyzer ГЛ2106 CONSTANTA-2. Cassette No.3-1 retrieval from ТБУ-В and setup on panel  for […]

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Unpatched Microsoft Word DDE Exploit Being Used In Widespread Malware Attacks

A newly discovered unpatched attacking method that exploits a built-in feature of Microsoft Office is currently being used in various widespread malware attack campaigns. Last week we reported how hackers could leveraging an old Microsoft Office feature called Dynamic Data Exchange (DDE), to perform malicious code execution on the targeted device without requiring Macros enabled or memory

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Anonymous Feedback Survey

I used the quiz function to do an anonymous survey of my students asking 3 questions about how the course was going. I would like to gather all the.

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Anonymous user a25ec2

Name, Anonymous user a25ec2. User since, August 24, 2016. Number of add-ons developed, 0 add-ons. Average rating of developer's add-ons, Not ...

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Get maximum number of outputs from anonymous function

Get maximum number of outputs from anonymous... Learn more about anonymous function, varargout.

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Kobie Marketing

Management doesn't care for employees career advancement because of favoritism. Their mickey mouse technology needs vast improvement The ...

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Anonymous user d84c04

Name, Anonymous user d84c04. User since, February 20, 2017. Number of add-ons developed, 0 add-ons. Average rating of developer's add-ons ...

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Google Play Store Launches Bug Bounty Program to Protect Popular Android Apps

Better late than never. Google has finally launched a bug bounty program for Android apps on Google Play Store, inviting security researchers to find and report vulnerabilities in some of the most popular Android apps. Dubbed "Google Play Security Reward," the bug bounty program offers security researchers to work directly with Android app developers to find and fix vulnerabilities in their

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Architect at waa (we architech anonymous)

There is an opportunity for an architect to join waa (we architech anonymous) at its office in Beijing.

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Implications for Campus Racial Climate

Anonymous social media platforms, such as Whisper and Yik Yak, have elicited controversy within the field of education over the past few years.

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Ravens: Mike Wallace (back) not practicing Thursday; Jeremy Maclin (back) wearing red, no-contact jersey (ESPN)

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I have a new follower on Twitter

Brian Connors
Co-Founder @AllSearch1 National Sales #Recruiting, Inc 5000. #entrepreneur #jobsearch Family. Outdoors. Techie.
York, PA
https://t.co/WFP8X4EXZ6
Following: 1304 - Followers: 1206

October 19, 2017 at 01:08PM via Twitter http://twitter.com/AllSearchBrian

Geisel Productions and Anonymous Content Win Best of Show for

Geisel Productions and Anonymous Content's branded short "BMW Films: The Escape” took home Best of Show at last night's One Screen Film ...

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Anonymous Friend

WPPED CREAM 2017, In recognition of the most outstanding WPP work of the year.

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Alcoholics anonymous code of ethics

Last Updated on Thu, 07 Sep 2017 | Medical Ethics Founded in 1935 by Bill Wilson, an alcoholic stockbroker, Alcoholics Anonymous (AA) borrowed ...

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Sales Administrator

View details and apply for this sales administrator job in Bristol, Avon with Anonymous on totaljobs. Sales Administrators are responsible for handling ...

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Live For Each Moon and DJ Anonymous (Total Stasis) – Library of Sounds Past Present and Future ...

Thomas is part founder and resident of Music For Dancers, a Los Angeles staple in the underground dance community. His other projects include, but ...

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fastq-anonymous 1.0.0

fastq-anonymous-1.0.0.tar.gz. Change the sequence of a fastq file to enable sharing of confidential information, for troubleshooting of tools. Change ...

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Hulu anonymous proxy fix

May 6, 2009 Control Freaks: Hulu Now Blocks Anonymous Proxies Too which the content providers behind Hulu saw as a problem because the ...

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I have a new follower on Twitter

Bitcoin Auto Trader
Learn the 📟🎧 Secret to Earning $1000's💸🎡💷🎉weekly in #Bitcoin on🌴🏆 Autopilot🏕 with #autotrading🖍📥Sign up Free!🖎👇

https://t.co/1MV4T5lItX
Following: 1806 - Followers: 1073

October 19, 2017 at 03:37AM via Twitter http://twitter.com/BitcoinAutoCash

M51: The Whirlpool Galaxy

Find the Big Dipper and follow the handle away from the dipper's bowl until you get to the last bright star. Then, just slide your telescope a little south and west and you'll come upon this stunning pair of interacting galaxies, the 51st entry in Charles Messier's famous catalog. Perhaps the original spiral nebula, the large galaxy with well defined spiral structure is also cataloged as NGC 5194. Its spiral arms and dust lanes clearly sweep in front of its companion galaxy (bottom), NGC 5195. The pair are about 31 million light-years distant and officially lie within the angular boundaries of the small constellation Canes Venatici. Though M51 looks faint and fuzzy to the eye, deep images like this one can reveal striking colors and the faint tidal debris around the smaller galaxy via NASA http://ift.tt/2yQWhyI

Download Link hesitates download for anonymous users

PDFs and other media documents using the "Download Link" display hesitate for anonymous users. I've confirmed that all the Download permissions ...

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Anonymous function expressions aren't covered for 'infer from usage'

Anonymous function expressions aren't covered for 'infer from usage' #19313. Open. DanielRosenwasser opened this Issue 3 minutes ago · 0 ...

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Anonymous user e3de79

Name, Anonymous user e3de79. User since, April 24, 2017. Number of add-ons developed, 0 add-ons. Average rating of developer's add-ons, Not yet ...

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Anonymous Feedback in Front Page

Re: Anonymous Feedback in Front Page - Password Authentication. Andrew C. Tuesday, 17 October 2017, 8:31 PM. Thanks Emma. I'm not sure how ...

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Anonymous

I've lived in Columbia for a few years now. I've watched as Mizzou went through it's low enrollment repercussion from it's racial inequality debacle.

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Ravens: Brandon Williams (foot) will practice Wednesday; missed last 4 games (ESPN)

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ISS Daily Summary Report – 10/17/2017

Miniature Exercise Device (MED-2):  Today crewmembers set up cameras in Node 3 to capture video from multiple views of the Advanced Resistive Exercise Device (ARED) and MED-2 hardware.  They also applied body markers, and performed exercises before transferring the video for downlink.  The ISS’s exercise equipment is large and bulky, while the MED-2 aims to demonstrate small robotic actuators can provide motion and resistance for crew workout sessions, reducing the size and weight of exercise equipment for long-duration space missions. The MED-2 investigation is a system to test key technologies needed to develop space based exercise equipment that may provide appropriate countermeasures to the adverse effects of microgravity. This technology is critical for the initial design and development of second and third generation Counter Measure Systems (CMS) hardware that is an order of magnitude lighter and smaller than existing ISS class of CMS hardware and that has significantly greater reliability.  Fluids Integrated Rack (FIR) Light Microscopy Module (LMM) Hardware Configuration: The crew continued to conduct the LMM configuration activities that began last week, by removing the Constrained Vapor Bubble (CVB) control box and LMM Monochrome camera, before installing the Fluids and Combustion Facility (FCF) high rate data link, the LMM confocal unit and brackets, and the LMM confocal and wide-field cameras. The LMM hardware configuration will support upcoming ACE-T6 operations. The Light Microscopy Module (LMM) is a modified commercial, highly flexible, state-of-the-art light imaging microscope facility that provides researchers with powerful diagnostic hardware and software onboard the International Space Station (ISS).  Meteor Grating Configuration: The crew removed and replaced gratings in the Meteor camera located in the Window Observational Research Facility (WORF) payload volume. The Meteor payload is a visible spectroscopy instrument with the primary purpose of observing meteors in Earth orbit. Meteor uses image analysis to provide information on the physical and chemical properties of the meteoroid dust, such as size, density, and chemical composition. Since the parent comets or asteroids for most of the meteor showers are identified, the study of the meteoroid dust on orbit provides information about the parent comets and asteroids.    TangoLab-2 Card Troubleshooting: In August, during installation of a payload card into the TangoLab-2 slot, the card did not seat properly.  Today as part of the troubleshooting efforts, the crew took a different payload card from TangoLab-1 and then installed it into the slot in TangoLab-2. The crew photographed the inside of TangoLab-2 specifically where connectors are located for downlink.  Veg-03 Operations: The crew photo-documented the status of the plants in the Veggie facility. The Veg-03 investigation uses the Veggie plant growth facility to cultivate a type of cabbage, lettuce and Mizuna which are harvested on-orbit with samples returned to Earth for testing.  Organisms grow differently in space, from single-celled bacteria to plants and humans. Future long-duration space missions will require crew members to grow their own food, so understanding how plants respond to microgravity is an important step toward that goal. Veg-03 uses the Veggie plant growth facility to cultivate a type of cabbage, lettuce and Mizuna which are harvested on-orbit with samples returned to Earth for testing. Earth Imagery from ISS Target Operations: The crew setup and configured the RED camera in the Cupola to capture video footage of New York City and the Nile Delta. The Earth Imagery from ISS investigation creates a series of videos, showcasing Earth from space. These videos will be taken with cameras on the International Space Station in 6K hi-resolution, then integrated into videos for screensavers for public enjoyment, exploration, and engagement. USOS Extra-Vehicular Activity (EVA) #46 Preparations:  Today the crew set up cameras and reviewed robotics procedures/tools in preparation for this Friday’s EVA. The goals of USOS EVA #46 include Removal and Replacement (R&R) of the Latching End Effector (LEE) A Camera Lens Assembly (CLA). Remote Power Controller Module (RPCM) P12B-B R&R:  Today, robotics ground controllers removed a degraded RPCM from external slot P12B-B and attempted to replace it with a functional unit from P13A-G. The installation of the new RPCM into P12B_B slot was unsuccessful this morning. Ground teams have backed off to a park position. Teams are discussing a forward plan. This RPC provides power to the S-band String 2 Transponder.  Today’s Planned Activities All activities are on schedule unless otherwise noted. BIOPLENKA. Activation of Cryogem-03 at +37°С setting KONYUGATSIYA. ТБУ-В No.2 Moding to +29 deg С Virus Definition File Update on Auxiliary Computer System (ВКС) Laptops Miniature Exercise Device Operations Session Onboard Training (OBT) Robotics On-board Trainer (ROBoT) Setup Regeneration of Micropurification Unit (БМП) Ф2 Cartridge (start) Review of Robotics procedures for US Extravehicular Activity (EVA) 46 Filling (separation) of ЕДВ [КОВ] for Elektron or ЕДВ-СВ (ЕДВ1 – No.1300 (00071692R, СМ1ПрК), ЕДВ2 – No.1210 (00067598R, ФГБ1ПГО_1_110), ЕДВ3 – No.1242 (00069551R, ФГБ1ПГО_1_111,3.0 CTB for ЕДВ (00053480R)) Meteor On Board Training METEOR Grating Configuration PRODUTSENT. Removal of from ТБУ-В No.05 (+4 deg C) and transfer to ТБУ-В No.02 (+29 deg C) СОЖ Maintenance Meteor Hard Disk Drive and Antivirus Update PRODUTSENT. Photography of Bioekologiya case setup in ТБУ-В No.02 KONYUGATSIYA. Removal of Recomb-K from thermostat (+4 deg C) and Insertion into thermostat at +29 deg C. Tagup with specialists Environmental Health System (EHS) Potable Water Dispenser (PWD) Sample Collect MPEP BRACKET INSTALLATION Crew Medical Officer (CMO) On Board Training (OBT) BIOPLENKA. Fixation of Constanta No.4-1 cassette biofilm and setup in ТБУ-В No.05. Insertion of Constanta kit for incubation into Cryogem-03. Tagup with specialists Photo TV GoPro Setup Bringing ODF up to date using Progress 437 delivered files KONYUGATSIYA. Process activation, setup in ТБУ-В at +29°С. Tagup with specialists Water Drainage from SM Rodnik H2O Tank 1 to ЕДВ. SM H2O Tank 1 shell compression Portable Onboard Computers (POC) Dynamic Onboard Ubiquitous Graphics (DOUG) Setup On-board Training (OBT) EVA Robotics Onboard Trainer (ROBoT) Session Inspect ЕДВ cover 1308. Tagup with specialists IMS Update KONYUGATSIYA. Completion of activation process, setup in thermostat (+29 deg C). Tagup with specialists KONYUGATSIYA. Photography of deactivation. Tagup with specialists VEG-03 Plant Photo Environmental Health System (EHS) Coliform Water Processing ENERGY Big Picture […]

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Enable Google's New "Advanced Protection" If You Don't Want to Get Hacked

It is good to be paranoid when it comes to cybersecurity. Google already provides various advanced features such as login alerts and two-factor authentication to keep your Google account secure. However, if you are extra paranoid, Google has just introduced its strongest ever security feature, called "Advanced Protection," which makes it easier for users, who are usually at high risk of

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[FD] SEC Consult SA-20171018-1 :: Multiple vulnerabilities in Linksys E-series products

SEC Consult Vulnerability Lab Security Advisory < 20171018-1 > ======================================================================= title: Multiple vulnerabilities product: Linksys E series, see "Vulnerable / tested versions" vulnerable version: see "Vulnerable / tested versions" fixed version: no public fix, see solution/timeline CVE number: - impact: high homepage: http://www.linksys.com/ found: 2017-06-26 by: T. Weber (Office Vienna) SEC Consult Vulnerability Lab An integrated part of SEC Consult Bangkok - Berlin - Linz - Luxembourg - Montreal - Moscow Kuala Lumpur - Singapore - Vienna (HQ) - Vilnius - Zurich http://ift.tt/1mGHMNR ======================================================================= Vendor description:

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Best anonymous image board

Best anonymous image board. Visit anon-ib.co now to see the best up-to-date Anon IB content for United States and also check out these interesting ...

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Anonymous - Sous Chef

A regionally and nationally acclaimed restaurant is looking to add a sous chef to their team. Management experience at a fine dining or progressive ...

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Anonymous user 65b2e5

Name, Anonymous user 65b2e5. User since, October 10, 2017. Number of add-ons developed, 0 add-ons. Average rating of developer's add-ons, Not ...

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jQuery (or any core scripts)

There are a ton of hits on this, but no solutions. As an anonymous user, my custom script is being loaded properly, but the dependency (jquery) is not.

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Take Stock in Children Receives $75000 Gift from Anonymous Donor

The South Florida State College Foundation has received a gift of stock valued at $75,000 from an anonymous donor to bolster the Take Stock in ...

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Wisconsin is the most anonymous CFP contender

CBS Writer Barrett Sallee discusses why he thinks Wisconsin is the most anonymous College Football Playoff contender of 2017.

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Stuck in Captive Portal with Anonymous Authentication

Stuck in Captive Portal with Anonymous Authentication. 20m ago. Hello,. We are trying to set up a captive portal where a user has to click "I Accept" to ...

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Anonymous user f9da84

Name, Anonymous user f9da84. User since, October 17, 2017. Number of add-ons developed, 0 add-ons. Average rating of developer's add-ons, Not ...

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Facebook buys anonymous teen compliment app TBH

NEW YORK — Facebook has bought TBH, a teen-focused app that lets people give anonymous compliments.

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[FD] SSD Advisory – Linux Kernel AF_PACKET Use-After-Free

SSD Advisory – Linux Kernel AF_PACKET Use-After-Free Full report: http://ift.tt/2zv0WmQ Twitter: @SecuriTeam_SSD Weibo: SecuriTeam_SSD Vulnerabilities summary The following advisory describes a use-after-free vulnerability found in Linux Kernel’s implementation of AF_PACKET that can lead to privilege escalation. AF_PACKET sockets “allow users to send or receive packets on the device driver level. This for example lets them to implement their own protocol on top of the physical layer or to sniff packets including Ethernet and higher levels protocol headers” Credit The vulnerability was discovered by an independent security researcher which reported this vulnerabilities to Beyond Security’s SecuriTeam Secure Disclosure program. Vendor response “It is quite likely that this is already fixed by: packet: hold bind lock when rebinding to fanout hook – http://ift.tt/2yv4Hun Also relevant, but not yet merged is packet: in packet_do_bind, test fanout with bind_lock held – http://ift.tt/2xLUmX5 We verified that this does not trigger on v4.14-rc2, but does trigger when reverting that first mentioned commit (008ba2a13f2d).”

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[FD] SSD Advisory – Ikraus Anti Virus Remote Code Execution

SSD Advisory – Ikraus Anti Virus Remote Code Execution Full report: http://ift.tt/2yrucg5 Twitter: @SecuriTeam_SSD Weibo: SecuriTeam_SSD Vulnerability summary The following advisory describes an remote code execution found in Ikraus Anti Virus version 2.16.7. KARUS anti.virus “secures your personal data and PC from all kinds of malware. Additionally, the Anti-SPAM module protects you from SPAM and malware from e-mails. Prevent intrusion and protect yourself against cyber-criminals by choosing IKARUS anti.virus, powered by the award-winning IKARUS scan.engine. It is among the best in the world, detecting new and existing threats every day. ” Credit An independent security researcher has reported this vulnerability to Beyond Security’s SecuriTeam Secure Disclosure program Vendor Response The vendor has released patches to address these vulnerabilities. For more information: http://ift.tt/2gobsru

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[FD] [CVE-2017-14322] Interspire Email Marketer - Remote Admin Authentication Bypass

Please disclose, thanks.

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[FD] SSD Advisory – FiberHome Directory Traversal

SSD Advisory – FiberHome Directory Traversal Full report: http://ift.tt/2hE3c34 Twitter: @SecuriTeam_SSD Weibo: SecuriTeam_SSD Vulnerability Summary The following advisory describes a directory traversal vulnerability found in FiberHome routers. FiberHome Technologies Group “was established in 1974. After continuous and intensive development for over 40 years, its business has been extended to R&D, manufacturing, marketing & sales, engineering service, in 4 major areas: fiber-optic communications, data networking communications, wireless communication, and intelligentizing applications. In particular, it has been providing end-to- end solutions integrated with opto-electronic devices, opticpreforms, fiber & cables, and optical communication systems to many countries around the world.” Credit An independent security researcher has reported this vulnerability to Beyond Security’s SecuriTeam Secure Disclosure program. Vendor response We tried to contact FiberHome since September 6 2017, repeated attempts to establish contact went unanswered. At this time there is no solution or workaround for the vulnerability.

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📉 Ravens slide down four spots to No. 22 in Week 7 Power Rankings (ESPN)

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Anybody remember the anonymous comments

No, but I remember the article by Barftknecht in mid-August suggesting that this season would be horrible and essentially that we suck, at least that ...

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I have a new follower on Twitter

Lee Gilbert
Father, author, economist, startup mentor, CEO LG Advisors. Insatiably curious. #Blockchain #Cryptocurrency
Chicago, IL

Following: 70070 - Followers: 109731

October 17, 2017 at 11:46AM via Twitter http://twitter.com/leetheonlyone

Templum Antiquum Ad Fontem Aegerium (Sant'Urbano alla Caffarella, Rome)

Perspectival depiction of a Roman temple with Corinthian columns, transformed into the church of Sant'Urbano alla Caffarelle in the 10th century AD.

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ISS Daily Summary Report – 10/16/2017

68 Progress (68P) docking:  Saturday morning, 68P launched from Baikonur after a two day delay.  This morning, 68P docked successfully to DC1 nadir, bringing supplies to the ISS. JAXA Protein Crystal Growth (PCG) #13 Installation:  Following the docking of 68P,  a crewmember stowed two canister bags containing protein samples in the JAXA Freezer-Refrigerator Of STirling cycle 2 (J-FROST2). This experiment will run from today through removal for return on Soyuz 51S.  The canisters contain protein samples prepared by Japanese and Russian researchers from universities, national research institutes, and the private sector. The purpose of this activity is to obtain high quality protein crystals in the microgravity environment at 20 degrees C for about 9 weeks. The results obtained by JAXA PCG #13 contribute to the development of drugs for multidrug-resistant bacteria, Alzheimer’s disease, muscular dystrophy and periodontitis. They will also aid in the development of a blood substitute and biosensor.  Advanced Nano Step Microgravity Measurements Apparatus (MMA) Cable Connection: After successfully adjusting the position of the cell stage to resolve movement issues during sample observations last week; today the MMA cable connection to the medical laptop was completed to measure MMA data during the Advanced Nano Step experiment run.  JAXA’s Advanced Nano Step experiment investigates the relationship between impurity incorporation mechanisms and the quality of obtained protein crystals should be clarified for the progress to an “advanced” stage of the space utilization for structure-based drug design. In this mission, we observe the protein crystal growth surfaces of glucose isomerase crystals in space in the presence of various impurities with the use of a Michelson interferometer. In addition, we also clarify the crystal surface morphology on the molecular step level by using the laser confocal microscope. We evaluate the crystal quality of the returned crystals. Human Research Program (HRP) Collections (Biochemical Profile, Repository): Today a 52S crewmember completed Flight Day (FD) 30 urine sample collections that began yesterday and blood sample collections in support of the Biochemical Profile and Repository experiments. The samples were placed in the Minus Eighty Degree Celsius Laboratory Freezer for ISS (MELFI). The Biochemical Profile experiment tests blood and urine samples are obtained from astronauts before, during, and after spaceflight. Specific proteins and chemicals in the samples are used as biomarkers, or indicators of health. Post-flight analysis yields a database of samples and test results, which scientists can use to study the effects of spaceflight on the body. Repository is a storage bank used to maintain biological specimens over extended periods of time and under well-controlled conditions. This repository supports scientific discovery that contributes to our fundamental knowledge in the area of human physiological changes and adaptation to a microgravity environment and provides unique opportunities to study longitudinal changes in human physiology spanning many missions. Fine Motor Skills (FMS): A 51S crewmember completed a Flight Day 80 FMS session, where the subject performs a series of interactive tasks on a touchscreen tablet. The investigation studies how fine motor skills are affected by long-term microgravity exposure, different phases of microgravity adaptation, and sensorimotor recovery after returning to Earth gravity. The goal of FMS is to answer how fine motor performance in microgravity trend/vary over the duration of a six-month and year-long space mission; how fine motor performance on orbit compare with that of a closely matched participant on Earth; and how performance trend/vary before and after gravitational transitions, including the periods of early flight adaptation, and very early/near immediate post-flight periods. USOS Extra-Vehicular Activity (EVA) #46 Preparations:  Today the crew configured tools, reviewed EVA procedures, and conducted a conference with ground specialists in preparation for next week’s EVA. The goals of USOS EVA #46 include Removal and Replacement (R&R) of the degraded CP13 camera and R&R of the Latching End Effector (LEE) A Camera Lens Assembly (CLA). Today’s Planned Activities All activities were completed unless otherwise noted. HRF Generic Urine Collection Male – Subject МО-8. H/W set up Body Mass Measurement – BMMD ISS HAM Radio Power Down USOS Window Shutter Close МО-8. Closeout Ops HRF Generic Frozen Blood Collection EML ON-Board Training HRF Generic Refrigerated Centrifuge Configuration EML Gas Valves Opening Photo TV Battery Charge Initiation Check Closure of SM window shutters 6,8,9,12,13,14 Note 5 HRF Generic Refrigerated Centrifuge Spin Conclude MDLT MMA Cable Connect Charging EVA Camera D4 Battery Environmental Health System (EHS) Total Organic Carbon Analyzer (TOCA) Water Recovery System (WRS) Sample Analysis HRF Generic Sample MELFI Insertion Auxiliary Laptop Computer System Virus Definition File Update Fine Motor Skills Experiment Test – Subject СОЖ maintenance MPEG2 Multicast Test via Ku-band (Activation/Deactivation of TV data and MPEG2 Multicast controls). Regenerative Environmental Control and Life Support System (RGN) Wastewater Storage Tank Assembly (WSTA) Fill BIOPLENKA. Activation of [ТБУ-В] thermostat #5 in +4C mode Preparation for Progress 437 Docking Fluids Integrated Rack Hardware Gather Progress 437 Docking to DC1 Activation of MPEG2 Multicast TV Monitoring Equipment. Tagup with specialists Extravehicular Activity (EVA) Tool Configuring Activation of mpeg2 multicast video recording mode PROGRESS 437 DOCKING TO DC1, Comm Reconfig for Nominal Ops after Docking Camera Light Assembly Retrieval Photo TV Battery Charge Swap Closing Applications and Downlink of MPEG2 Multicast video via OCA Miniature Exercise Device Surface Pro 3 Charge In-Flight Maintenance (IFM) Hatch Seal Inspection Z1 Dome Egress Extravehicular Activity (EVA) Camera Light Assembly Configuring Glacier  Sample Transfer to MELFI ТКГ 437 and DC1 Interface Leak Check Handover. Leak Check, Hatch Opening, Installation of Quick Release Screw Clamps (ЗВБ) Environmental Health System (EHS) Total Organic Carbon Analyzer (TOCA) Sample Data Record DC1-СУ Hatch Opening Opening of Progress-[СУ] Transfer Hatch Quick-disconnect screw clamps (БЗВ) installation Extravehicular Activity (EVA) Procedure Review WRM CWC Stowage Physical Fitness Evaluation (on the treadmill) COLLECTING AIR SAMPLES USING АК-1М SAMPLER IN PROGRESS 437 Progress 437 Deactivation, Air Duct Installation MELFI 2 Ice Brick Insert 1 Public Affairs Office (PAO) High Definition (HD) Config LAB Setup Progress 437 (DC1) Transfers of Critical, Priority and US Cargo Items, and IMS Ops Public Affairs Office (PAO) Event in High Definition (HD) – Lab KRISTALLIZATOR. PCG Transfer from […]

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Dangerous Malware Allows Anyone to Empty ATMs—And It’s On Sale!

Hacking ATM is now easier than ever before. Usually, hackers exploit hardware and software vulnerabilities to hack ATMs and force them to spit out cash, but now anyone can simply buy a malware to steal millions in cash from ATMs. Hackers are selling ready-made ATM malware on an underground hacking forum that anybody can simply buy for around $5000, researchers at Kaspersky Lab discovered

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Learn Ethical Hacking — Get 8 Online Courses (With Sample Videos) For Just $29

With the rise in cyber-crimes, ethical hacking has become a powerful strategy in the fight against online threats. In general terms, ethical hackers are authorised to break into supposedly 'secure' computer systems without malicious intent, but with the aim of discovering vulnerabilities to bring about improved protection. Ethical Hackers are now kind of becoming the alchemists of the 21st

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[FD] SEC Consult SA-20171017-0 :: Cross site scripting in Webtrekk Pixel tracking component

SEC Consult Vulnerability Lab Security Advisory < 20171017-0 > ======================================================================= title: Cross site scripting product: Webtrekk Pixel tracking vulnerable version: v3.24 to v3.40, v4.00 to v4.40, v5.00 to v5.04 fixed version: v3.41, v4.41, v5.05 impact: Medium homepage: http://ift.tt/1E9i6Ws found: 2017-08-29 by: Malte Batram for SEC Consult Vulnerability Lab An integrated part of SEC Consult Bangkok - Berlin - Linz - Luxembourg - Montreal - Moscow Kuala Lumpur - Singapore - Vienna (HQ) - Vilnius - Zurich http://ift.tt/1mGHMNR ======================================================================= Vendor description:

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Microsoft Kept Secret That Its Bug-Tracking Database Was Hacked In 2013

It was not just Yahoo among "Fortune 500" companies who tried to keep a major data breach incident secret. Reportedly, Microsoft had also suffered a data breach four and a half years ago (in 2013), when a "highly sophisticated hacking group" breached its bug-reporting and patch-tracking database, but the hack was never made public until today. According to five former employees of the

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Anonymous Bullying Report

... Links · Newsletters · Photo Album · Resources · School Calendar · Staff Directory · Staff Only · State Required Postings · Anonymous Bullying Report.

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University of Oregon Receives $50 Million From Anonymous Donor

The University of Oregon has announced a $50 million gift from an anonymous donor in support of research and discovery, student success, and ...

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I have a new follower on Twitter

Andy Cline
ATTN: do-gooders. Solar Marketing, cleantech at https://t.co/R0Ae93Ccxw . Co-venture network for NGOs TBA at https://t.co/AC151TLFY0.

https://t.co/R0Ae93kB8W
Following: 4217 - Followers: 4516

October 17, 2017 at 06:31AM via Twitter http://twitter.com/apuforlife1

The Relaxed Satanist

The Relaxed Satanist. Beautiful Stories From Anonymous People #83 October 17, 2017. Gethard has a meandering chit chat with a photographer and ...

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Serious Crypto-Flaw Lets Hackers Recover Private RSA Keys Used in Billion of Devices

If you think KRACK attack for WiFi is the worst vulnerability of this year, then hold on… ...we have got another one for you which is even worse. Microsoft, Google, Lenovo, HP and Fujitsu are warning their customers of a potentially serious vulnerability in widely used RSA cryptographic library produced by German semiconductor manufacturer Infineon Technologies. It's noteworthy that this

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Haumea of the Outer Solar System

One of the strangest objects in the outer Solar System has recently been found to have a ring. The object, named Haumea, is the fifth designated dwarf planet after Pluto, Ceres, Eris, and Makemake. Haumea's oblong shape makes it quite unusual. Along one direction, Haumea is significantly longer than Pluto, while in another direction Haumea has an extent very similar to Pluto, while in the third direction is much smaller. Haumea's orbit sometimes brings it closer to the Sun than Pluto, but usually Haumea is further away. Illustrated above, an artist visualizes Haumea as a cratered ellipsoid surrounded by a uniform ring. Originally discovered in 2003 and given the temporary designation of 2003 EL61, Haumea was renamed in 2008 by the IAU for a Hawaiian goddess. Besides the ring discovered this year, Haumea has two small moons discovered in 2005, named Hi'iaka and Namaka for daughters of the goddess. via NASA http://ift.tt/2gKDBWu

Deleted account still shows in the comments instead of Anonymous

Some time ago I deleted my Drupal account, in most places my account name gets changed to 'Anonymous' like expected but in comments it doesn't ...

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anonymous's post

hey anyone if you want some help building something give me a friend request and ill help im good at redstone and building (at least i think so :|...).

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Facebook acquires anonymous polling app targeted at teens

Facebook says it has acquired tbh, a teen-focused messaging app that lets users create anonymous polls, for an undisclosed amount. The four ...

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Anonymous function with vector instead of multiple inputs

Anonymous function with vector instead of... Learn more about anonymous function, matlabfunction.

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Ravens: John Harbaugh defends OC Marty Mornhinweg and struggling offense (ESPN)

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Anonymous Advertiser Id:63409

View details and apply for this head housekeeper job in Maidenhead, Berkshire with Anonymous Advertiser Id:63409 on Caterer.com. Our client is ...

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Sales Executive

View details and apply for this sales executive job in City (EC2) with Anonymous on totaljobs. Description Cistor is a tier one Cisco Partner, with ...

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I have a new follower on Twitter

Jeff Jackson 📱🚴🏾
Online Marketing🖥, Digital Marketing, Video Content, Business Automation, Linux🐧, Web integration, Cyclist🚴🏾, Greyhounds, Scuba Diving🐙🐬🐠, Minimalist

https://t.co/o9nbubMv6P
Following: 6055 - Followers: 6395

October 16, 2017 at 02:41PM via Twitter http://twitter.com/jefjxn

Ravens drop passes and playoff hopes in loss to Bears - Jamison Hensley (ESPN)

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Anonymous (Dark)

Anonymous (Dark). by Andrew ... Anonymous Theme Works best on firefox 57+. Only with Firefox — Get Firefox Now! Add to collection ...

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Hackers Use New Flash Zero-Day Exploit to Distribute FinFisher Spyware

FinSpy—the infamous surveillance malware is back and infecting high-profile targets using a new Adobe Flash zero-day exploit delivered through Microsoft Office documents. Security researchers from Kaspersky Labs have discovered a new zero-day remote code execution vulnerability in Adobe Flash, which was being actively exploited in the wild by a group of advanced persistent threat actors,

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Yet Another Linux Kernel Privilege-Escalation Bug Discovered

Security researchers have discovered a new privilege-escalation vulnerability in Linux kernel that could allow a local attacker to execute code on the affected systems with elevated privileges. Discovered by Venustech ADLab (Active-Defense Lab) researchers, the Linux kernel vulnerability (CVE-2017-15265) is due to a use-after-free memory error in the Advanced Linux Sound Architecture (ALSA)

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ISS Daily Summary Report – 10/13/2017

Advanced Nano Step Troubleshooting: Today the crew successfully completed troubleshooting operations by retrieving the Advanced Nano Step cartridge from the Solution Crystallization Observation Facility (SCOF) to access and adjust the position of the cell stage, due to movement issues during the sample observation.  JAXA’s Advanced Nano Step experiment investigates the relationship between impurity incorporation mechanisms and the quality of obtained protein crystals should be clarified for the progress to an “advanced” stage of the space utilization for structure-based drug design. In this mission, we observe the protein crystal growth surfaces of glucose isomerase crystals in space in the presence of various impurities with the use of a Michelson interferometer. In addition, we also clarify the crystal surface morphology on the molecular step level by using the laser confocal microscope. We evaluate the crystal quality of the returned crystals.  Lighting Effects:  A 51S crewmember completed a Visual Performance Test by stowing the hardware in their crewquarters, setting the light to the correct mode, turning all other light sources in the crew quarters off, and performing a Numerical Verification Test and a Color Discrimination Test. The completed tests will be photographed and downlinked. The Lighting Effects investigation studies the impact of the change from fluorescent light bulbs to solid-state light-emitting diodes (LEDs) with adjustable intensity and color and aims to determine if the new lights can improve crew circadian rhythms, sleep, and cognitive performance. Fluids Integrated Rack (FIR) Light Microscopy Module (LMM) Hardware Configuration: To prepare for upcoming ACE-T6 operations, the crew configured the back of the optics bench for the LMM upgrades for confocal operations. The Light Microscopy Module (LMM) is a modified commercial, highly flexible, state-of-the-art light imaging microscope facility that provides researchers with powerful diagnostic hardware and software onboard the International Space Station (ISS). Combustion Integrated Rack (CIR) Configuration for Advanced Combustion via Microgravity Experiments (ACME): The crew continued to conduct ACME insertion configurations that began earlier this week by setting the CIR valve timers to limit fuel and oxygen into the CIR combustion chamber. The crew also removed and replaced a CIR manifold bottle and a CIR absorber cartridge. The ACME investigation is a set of five independent studies of gaseous flames to be conducted in the CIR. ACME’s primary goal is to improved fuel efficiency and reduced pollutant production in practical combustion on Earth. Its secondary goal is to improve spacecraft fire prevention through innovative research focused on materials flammability.  Device for the study of Critical Liquids and Crystallization) DECLIC Hose Relocation: Following last month’s hose relocation to troubleshoot the moderate temperature loop (MTL) flow issues to the DECLIC directional solidification insert (DSL), today the crew returned the DECLIC water supply and return hoses from the Lower Control Panel to the Upper Control Panel, and re-connected the GLACIER water supply and return hoses into the Lower Control Panel.  DECLIC is a multi-user facility utilized to study transparent media and their phase transitions in microgravity onboard the International Space Station (ISS). The DSI portion of the DECLIC multi-user facility experiment will study a series of benchmark experiments on transparent alloys that freeze like metals under microgravity onboard the International Space Station (ISS) using SCN (succinonitrile-a transparent organic substance in the liquid state that is used to study the phenomena related to solidification processes) based alloys. The DSI insert will be installed for the second run of the three series of DECLIC experiments. Space Headaches:  The weekly questionnaire for the ESA Space Headaches investigation was completed by the crew.  The Space Headaches investigation collects information that may help in the development of methods to alleviate associated symptoms and improvement in the well-being and performance of crewmembers in space. Headaches during space flight can negatively affect mental and physical capacities of crewmembers that can influence performance during a space mission.  USOS Extra-Vehicular Activity (EVA) #46 Preparations:  Today the crew reviewed preliminary EVA procedures, conducted a conference with ground specialists, and charged batteries in preparation for next week’s EVA. The goals of USOS EVA #46 include Removal and Replacement (R&R) of the degraded CP13 camera and R&R of the Latching End Effector (LEE) A Camera Lens Assembly (CLA). External Television Camera Group (ETVCG) Maintenance:  Today the crew performed maintenance procedures required to prep an ETVCG for future installation at Camera Port 13 (CP13). The ETVCGs provide external views of the station and provide enhanced situational awareness as well as views of the Earth from space. Extravehicular Robotic Operations: Last night the ground controllers powered up and maneuvered the Space Station Remote Manipulator System (SSRMS) to grapple the Special Purpose Dexterous Manipulator (SPDM). SPDM Arm2 was used to unstow Robot Micro Conical Tool (RMCT) #2 from the Tool Holster Assembly (THA) and SPDM Arms and Body were configured in preparation for the Remote Power Controller Module (RPCM) P12B-B Remove and Replace activity. Today’s Planned Activities All activities were completed unless otherwise noted. ESA Weekly crew conference Photo/TV Camcorder Setup Verification Kubik 6 Temperature and Centrifuge Data Copy LMM Hardware Configuration Recovery of Condensate Feed Unit (БПК) Mfr #1607008. Tagup with specialists Vacuum cleaning of ВТ7 screens on ГЖТ4 behind FGB panel 108 Advanced Nano Step trouble shooting UDOD. Experiment Ops with DYKNANIYE-1 and SPRUT-2 Sets. Tagup with specialists Cleaning FGB ЦВ1 Circulation Fan Guard Screen UDOD. Photography of the Experiment Session INTER-MAI-75. HAM Radio Hardware Activation Life On The Station Photo and Video OA8 Prep Photograph Plug-in-Plan Node 1 Audit Lighting Effects Visual Performance Tests Light Setting – Subject Plug-in-Plan Cupola Audit Combustion Integrated Rack Manifold#1 Bottle Removal Combustion Integrated Rack Manifold #2 Bottle Replacement Extravehicular Activity (EVA) Procedure Review Inspection and Photos of space behind SM panels СОЖ maintenance Combustion Integrated Rack Manifold #4 Bottle Install Combustion Integrated Rack GN2 Relief Valve Installation Extravehicular Activity (EVA) Procedure Conference Combustion Integrated Rack Adsorber Cartridge Replacement Environmental Health System (EHS) – Rad Detector Rotate Extravehicular Mobility Unit (EMU) Long Life Battery (LLB) Terminate Combustion Integrated Rack Valve Timer Set Health Maintenance System (HMS) Electrocardiogram Device Find Battery Stowage Assembly (BSA) Operation Termination […]

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How A Drive-by Download Attack Locked Down Data of this City for 4 Days

We don't really know the pain and cost of a downtime event unless we are directly touched. Be it a flood, electrical failure, ransomware attack or other broad geographic events; we don't know what it is really like to have to restore IT infrastructure unless we have had to do it ourselves. We look at other people's backup and recovery issues and hope we are smarter or clever enough to keep

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Raspberry Pi: Deep learning object detection with OpenCV

A few weeks ago I demonstrated how to perform real-time object detection using deep learning and OpenCV on a standard laptop/desktop.

After the post was published I received a number of emails from PyImageSearch readers who were curious if the Raspberry Pi could also be used for real-time object detection.

The short answer is “kind of”…

…but only if you set your expectations accordingly.

Even when applying our optimized OpenCV + Raspberry Pi install the Pi is only capable of getting up to ~0.9 frames per second when applying deep learning for object detection with Python and OpenCV.

Is that fast enough?

Well, that depends on your application.

If you’re attempting to detect objects that are quickly moving through your field of view, likely
not.

But if you’re monitoring a low traffic environment with slower moving objects, the Raspberry Pi could indeed be fast enough.

In the remainder of today’s blog post we’ll be reviewing two methods to perform deep learning-based object detection on the Raspberry Pi.

Looking for the source code to this post?
Jump right to the downloads section.

Raspberry Pi: Deep learning object detection with OpenCV

Today’s blog post is broken down into two parts.

In the first part, we’ll benchmark the Raspberry Pi for real-time object detection using OpenCV and Python. This benchmark will come from the exact code we used for our laptop/desktop deep learning object detector from a few weeks ago.

I’ll then demonstrate how to use multiprocessing to create an alternate method to object detection using the Raspberry Pi. This method may or may not be useful for your particular application, but at the very least it will give you an idea on different methods to approach the problem.

Object detection and OpenCV benchmark on the Raspberry Pi

The code we’ll discuss in this section is is identical to our previous post on Real-time object detection with deep learning and OpenCV; therefore, I will not be reviewing the code exhaustively.

For a deep dive into the code, please see the original post.

Instead, we’ll simply be using this code to benchmark the Raspberry Pi for deep learning-based object detection.

To get started, open up a new file, name it

real_time_object_detection.py

 , and insert the following code:

# import the necessary packages
from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2

We then need to parse our command line arguments:

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", required=True,
        help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True,
        help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
        help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

Followed by performing some initializations:

# initialize the list of class labels MobileNet SSD was trained to
# detect, then generate a set of bounding box colors for each class
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
        "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
        "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
        "sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

We initialize

CLASSES

, our class labels, and corresponding

COLORS

, for on-frame text and bounding boxes (Lines 22-26), followed by loading the serialized neural network model (Line 30).

Next, we’ll initialize the video stream object and frames per second counter:

# initialize the video stream, allow the camera sensor to warm up,
# and initialize the FPS counter
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
# vs = VideoStream(usePiCamera=True).start()
time.sleep(2.0)
fps = FPS().start()

Wwe initialize the video stream and allow the camera warm up for 2.0 seconds (Lines 35-37).

On Line 35 we initialize our

VideoStream

  using a USB camera If you are using the Raspberry Pi camera module you’ll want to comment out Line 35 and uncomment Line 36 (which will enable you to access the Raspberry Pi camera module via the

VideoStream

  class).

From there we start our

fps

  counter on Line 38.

We are now ready to loop over frames from our input video stream:

# loop over the frames from the video stream
while True:
        # grab the frame from the threaded video stream and resize it
        # to have a maximum width of 400 pixels
        frame = vs.read()
        frame = imutils.resize(frame, width=400)

        # grab the frame dimensions and convert it to a blob
        (h, w) = frame.shape[:2]
        blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
                0.007843, (300, 300), 127.5)

        # pass the blob through the network and obtain the detections and
        # predictions
        net.setInput(blob)
        detections = net.forward()

Lines 41-55 simply grab and resize a

frame

 , convert it to a

blob

, and pass the

blob

  through the neural network, obtaining the

detections

  and bounding box predictions.

From there we need to loop over the

detections

  to see what objects were detected in the

frame

 :

# loop over the detections
        for i in np.arange(0, detections.shape[2]):
                # extract the confidence (i.e., probability) associated with
                # the prediction
                confidence = detections[0, 0, i, 2]

                # filter out weak detections by ensuring the `confidence` is
                # greater than the minimum confidence
                if confidence > args["confidence"]:
                        # extract the index of the class label from the
                        # `detections`, then compute the (x, y)-coordinates of
                        # the bounding box for the object
                        idx = int(detections[0, 0, i, 1])
                        box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
                        (startX, startY, endX, endY) = box.astype("int")

                        # draw the prediction on the frame
                        label = "{}: {:.2f}%".format(CLASSES[idx],
                                confidence * 100)
                        cv2.rectangle(frame, (startX, startY), (endX, endY),
                                COLORS[idx], 2)
                        y = startY - 15 if startY - 15 > 15 else startY + 15
                        cv2.putText(frame, label, (startX, y),
                                cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)

On Lines 58-80, we loop over our

detections

 . For each detection we examine the

confidence

  and ensure the corresponding probability of the detection is above a predefined threshold. If it is, then we extract the class label and compute (x ,y) bounding box coordinates. These coordinates will enable us to draw a bounding box around the object in the image along with the associated class label.

From there we’ll finish out the loop and do some cleanup:

# show the output frame
        cv2.imshow("Frame", frame)
        key = cv2.waitKey(1) & 0xFF

        # if the `q` key was pressed, break from the loop
        if key == ord("q"):
                break

        # update the FPS counter
        fps.update()

# stop the timer and display FPS information
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()

Lines 82-91 close out the loop — we show each frame,

break

  if ‘q’ key is pressed, and update our

fps

  counter.

The final terminal message output and cleanup is handled on Lines 94-100.

Now that our brief explanation of

real_time_object_detection.py

  is finished, let’s examine the results of this approach to obtain a baseline.

Go ahead and use the “Downloads” section of this post to download the source code and pre-trained models.

From there, execute the following command:

$ python real_time_object_detection.py \
        --prototxt MobileNetSSD_deploy.prototxt.txt \
        --model MobileNetSSD_deploy.caffemodel
[INFO] loading model...
[INFO] starting video stream...
[INFO] elapsed time: 54.70
[INFO] approx. FPS: 0.90

As you can see from my results we are obtaining ~0.9 frames per second throughput using this method and the Raspberry Pi.

Compared to the 6-7 frames per second using our laptop/desktop we can see that the Raspberry Pi is substantially slower.

That’s not to say that the Raspberry Pi is unusable when applying deep learning object detection, but you need to set your expectations on what’s realistic (even when applying our OpenCV + Raspberry Pi optimizations).

Note: For what it’s worth, I could only obtain 0.49 FPS when NOT using our optimized OpenCV + Raspberry Pi install — that just goes to show you how much of a difference NEON and VFPV3 can make.

A different approach to object detection on the Raspberry Pi

Using the example from the previous section we see that calling

net.forward()

  is a blocking operation — the rest of the code in the

while

  loop is not allowed to complete until

net.forward()

  returns the

detections

.

So, what if

net.forwad()

  was not a blocking operation?

Would we able to obtain a faster frames per second throughput?

Well, that’s a loaded question.

No matter what, it will take approximately a little over a second for

net.forwad()

  to complete using the Raspberry Pi and this particular architecture — that cannot change.

But what we can do is create a separate process that is solely responsible for applying the deep learning object detector, thereby unblocking the main thread of execution and allow our

while

  loop to continue.

Moving the predictions to separate process will give the illusion that our Raspberry Pi object detector is running faster than it actually is, when in reality the

net.forward()

  computation is still taking a little over one second.

The only problem here is that our output object detection predictions will lag behind what is currently being displayed on our screen. If you detecting fast-moving objects you may miss the detection entirely, or at the very least, the object will be out of the frame before you obtain your detections from the neural network.

Therefore, this approach should only be used for slow-moving objects where we can tolerate lag.

To see how this multiprocessing method works, open up a new file, name it

pi_object_detection.py

 , and insert the following code:

# import the necessary packages
from imutils.video import VideoStream
from imutils.video import FPS
from multiprocessing import Process
from multiprocessing import Queue
import numpy as np
import argparse
import imutils
import time
import cv2

For the code walkthrough in this section, I’ll be pointing out and explaining the differences (there are quite a few) compared to our non-multprocessing method.

Our imports on Lines 2-10 are mostly the same, but notice the imports of

Process

  and

Queue

  from Python’s multiprocessing package.

Next, I’d like to draw your attention to a new function,

classify_frame

 :

def classify_frame(net, inputQueue, outputQueue):
        # keep looping
        while True:
                # check to see if there is a frame in our input queue
                if not inputQueue.empty():
                        # grab the frame from the input queue, resize it, and
                        # construct a blob from it
                        frame = inputQueue.get()
                        frame = cv2.resize(frame, (300, 300))
                        blob = cv2.dnn.blobFromImage(frame, 0.007843,
                                (300, 300), 127.5)

                        # set the blob as input to our deep learning object
                        # detector and obtain the detections
                        net.setInput(blob)
                        detections = net.forward()

                        # write the detections to the output queue
                        outputQueue.put(detections)

Our new

classify_frame

  function is responsible for our multiprocessing — later on we’ll set it up to run in a child process.

The

classify_frame

  function takes three parameters:

• net
  

   : the neural network object.

• inputQueue
  

   : our FIFO (first in first out) queue of frames for object detection.

• outputQueue
  

  : our FIFO queue of detections which will be processed in the main thread.

This child process will loop continuously until the parent exits and effectively terminates the child.

In the loop, if the

inputQueue

  contains a

frame

 , we grab it, and then pre-process it and create a

blob

  (Lines 16-22), just as we have done in the previous script.

From there, we send the

blob

  through the neural network (Lines 26-27) and place the

detections

  in an

outputQueue

  for processing by the parent.

Now let’s parse our command line arguments:

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", required=True,
        help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True,
        help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
        help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

There is no difference here — we are simply parsing the same command line arguments on Lines 33-40.

Next we initialize some variables just as in our previous script:

# initialize the list of class labels MobileNet SSD was trained to
# detect, then generate a set of bounding box colors for each class
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
        "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
        "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
        "sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

This code is the same — we initialize class labels, colors, and load our model.

Here’s where things get different:

# initialize the input queue (frames), output queue (detections),
# and the list of actual detections returned by the child process
inputQueue = Queue(maxsize=1)
outputQueue = Queue(maxsize=1)
detections = None

On Lines 56-58 we initialize an

inputQueue

  of frames, an

outputQueue

  of detections, and a

detections

  list.

Our

inputQueue

  will be populated by the parent and processed by the child — it is the input to the child process.  Our

outputQueue

  will be populated by the child, and processed by the parent — it is output from the child process. Both of these queues trivially have a size of one as our neural network will only be applying object detections to one frame at a time.

Let’s initialize and start the child process:

# construct a child process *indepedent* from our main process of
# execution
print("[INFO] starting process...")
p = Process(target=classify_frame, args=(net, inputQueue,
        outputQueue,))
p.daemon = True
p.start()

It is very easy to construct a child process with Python’s multiprocessing module — simply specify the

target

  function and

args

  to the function as we have done on Lines 63 and 64.

Line 65 specifies that

p

  is a daemon process, and Line 66 kicks the process off.

From there we’ll see some more familiar code:

# initialize the video stream, allow the cammera sensor to warmup,
# and initialize the FPS counter
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
# vs = VideoStream(usePiCamera=True).start()
time.sleep(2.0)
fps = FPS().start()

Don’t forget to change your video stream object to use the PiCamera if you desire by switching which line is commented (Lines 71 and 72).

Once our

vs

  object and

fps

  counters are initialized, we can loop over the video frames:

# loop over the frames from the video stream
while True:
        # grab the frame from the threaded video stream, resize it, and
        # grab its dimensions
        frame = vs.read()
        frame = imutils.resize(frame, width=400)
        (fH, fW) = frame.shape[:2]

On Lines 80-82, we read a frame, resize it, and extract the width and height.

Next, we’ll work our our queues into the flow:

# if the input queue *is* empty, give the current frame to
        # classify
        if inputQueue.empty():
                inputQueue.put(frame)

        # if the output queue *is not* empty, grab the detections
        if not outputQueue.empty():
                detections = outputQueue.get()

First we check if the

inputQueue

  is empty — if it is empty, we put a frame in the

inputQueue

  for processing by the child (Lines 86 and 87). Remember, the child process is running in an infinite loop, so it will be processing the

inputQueue

  in the background.

Then we check if the

outputQueue

  is not empty — if it is not empty (something is in it), we grab the

detections

  for processing here in the parent (Lines 90 and 91). When we call

get()

  on the

outputQueue

 , the detections are returned and the

outputQueue

  is now momentarily empty.

If you are unfamiliar with Queues or if you want a refresher, see this documentation.

Let’s process our detections:

# check to see if our detectios are not None (and if so, we'll
        # draw the detections on the frame)
        if detections is not None:
                # loop over the detections
                for i in np.arange(0, detections.shape[2]):
                        # extract the confidence (i.e., probability) associated
                        # with the prediction
                        confidence = detections[0, 0, i, 2]

                        # filter out weak detections by ensuring the `confidence`
                        # is greater than the minimum confidence
                        if confidence < args["confidence"]:
                                continue

                        # otherwise, extract the index of the class label from
                        # the `detections`, then compute the (x, y)-coordinates
                        # of the bounding box for the object
                        idx = int(detections[0, 0, i, 1])
                        dims = np.array([fW, fH, fW, fH])
                        box = detections[0, 0, i, 3:7] * dims
                        (startX, startY, endX, endY) = box.astype("int")

                        # draw the prediction on the frame
                        label = "{}: {:.2f}%".format(CLASSES[idx],
                                confidence * 100)
                        cv2.rectangle(frame, (startX, startY), (endX, endY),
                                COLORS[idx], 2)
                        y = startY - 15 if startY - 15 > 15 else startY + 15
                        cv2.putText(frame, label, (startX, y),
                                cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)

If our

detections

  list is populated (it is not

None

 ), we loop over the detections as we have done in the previous section’s code.

In the loop, we extract and check the

confidence

  against the threshold (Lines 100-105),  extract the class label index (Line 110), and draw a box and label on the frame (Lines 111-122).

From there in the while loop we’ll complete a few remaining steps, followed by printing some statistics to the terminal, and performing cleanup:

# show the output frame
        cv2.imshow("Frame", frame)
        key = cv2.waitKey(1) & 0xFF

        # if the `q` key was pressed, break from the loop
        if key == ord("q"):
                break

        # update the FPS counter
        fps.update()

# stop the timer and display FPS information
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()

In the remainder of the loop, we display the frame to the screen (Line 125) and capture a key press and check if it is the quit key at which point we break out of the loop (Lines 126-130). We also update our

fps

  counter.

To finish out, we stop the

fps

  counter, print our time/FPS statistics, and finally close windows and stop the video stream (Lines 136-142).

Now that we’re done walking through our new multiprocessing code, let’s compare the method to the single thread approach from the previous section.

Be sure to use the “Downloads” section of this blog post to download the source code + pre-trained MobileNet SSD neural network. From there, execute the following command:

$ python pi_object_detection.py \
        --prototxt MobileNetSSD_deploy.prototxt.txt \
        --model MobileNetSSD_deploy.caffemodel
[INFO] loading model...
[INFO] starting process...
[INFO] starting video stream...
[INFO] elapsed time: 48.55
[INFO] approx. FPS: 27.83

Here you can see that our

while

  loop is capable of processing 27 frames per second. However, this throughput rate is an illusion — the neural network running in the background is still only capable of processing 0.9 frames per second.

Note: I also tested this code on the Raspberry Pi camera module and was able to obtain 60.92 frames per second over 35 elapsed seconds.

The difference here is that we can obtain real-time throughput by displaying each new input frame in real-time and then drawing any previous

detections

  on the current frame.

Once we have a new set of

detections

  we then draw the new ones on the frame.

This process repeats until we exit the script. The downside is that we see substantial lag. There are clips in the above video where we can see that all objects have clearly left the field of view…

…however, our script still reports the objects as being present.

Therefore, you should consider only using this approach when:

1. Objects are slow moving and the previous detections can be used as an approximation to the new location.
2. Displaying the actual frames themselves in real-time is paramount to user experience.

Summary

In today’s blog post we examined using the Raspberry Pi for object detection using deep learning, OpenCV, and Python.

As our results demonstrated we were able to get up to 0.9 frames per second, which is not fast enough to constitute real-time detection. That said, given the limited processing power of the Pi, 0.9 frames per second is still reasonable for some applications.

We then wrapped up this blog post by examining an alternate method to deep learning object detection on the Raspberry Pi by using multiprocessing. Whether or not this second approach is suitable for you is again highly dependent on your application.

If your use case involves low traffic object detection where the objects are slow moving through the frame, then you can certainly consider using the Raspberry Pi for deep learning object detection. However, if you are developing an application that involves many objects that are fast moving, you should instead consider faster hardware.

Thanks for reading and enjoy!

And if you’re interested in studying deep learning in more depth, be sure to take a look at my new book, Deep Learning for Computer Vision with Python. Whether this is the first time you’ve worked with machine learning and neural networks or you’re already a seasoned deep learning practitioner, my new book is engineered from the ground up to help you reach expert status.

Just click here to start your journey to deep learning mastery.

Downloads:

If you would like to download the code and images used in this post, please enter your email address in the form below. Not only will you get a .zip of the code, I’ll also send you a FREE 11-page Resource Guide on Computer Vision and Image Search Engines, including exclusive techniques that I don’t post on this blog! Sound good? If so, enter your email address and I’ll send you the code immediately!

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