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2008 Mercedes-Benz CL-Class V12 2dr Coupe 5.5L
Rear Wheel Drive, 2 Door Coupe, Compact
Recently Viewed Cars 2008 Mercedes-Benz CL-Class V12 2dr Coupe 5.5L
Mercedes-Benz CL500 4MATIC, For Those Ski Trips In The Swiss Alps [Geneva Motor Show] – No, 4MATIC does not mean that the CL500 has a 4-speed slushbox; it actually has Mercedes' 7-speed automatic. 4MATIC is Mercedes' all-wheel-drive system, which makes this the first time ever that the big CL coupe has been fitted with such a drivetrain Source: feeds.gawker.com
Mercedes-Benz C63 AMG Set To Launch As The Official Safety Car Of The DTM – When the second DTM race weekend of the season kicks off in Oschersleben tomorrow (April 19th), taking the reins of the official Safety Car duties will be none other than the Mercedes-Benz C63 AMG. This marks the eighth continuous year Mercedes has p Source: www.emercedesbenz.com
The Mercedes-Benz CL-Class Vs. The BMW 6-Series Vs. The Maybach Exelero – As automotive enthusiasts, we've all been there before: a new model makes its debut, and the first thing you think is 'Wow, I love the new ___________________ (fill in the blank with your attribute of choice).' Maybe it's the wheels; maybe its the li Source: www.emercedesbenz.com
Mercedes-Benz Review Roundup: SL550, CL63 AMG And SLK-Class – After a lengthy round of Friday press releases, wrapping up the day comes an early-weekend Review Roundup installment looking at a total of three new Mercedes models. The 2009 SL550, the CL63 AMG and the SLK-Class - this is the superb triumvirate scr Source: www.emercedesbenz.com
Mercedes-Benz Review Roundup: The CL63 AMG, CLS63 AMG And SL350 – The Mercedes-Benz CL63 AMG, CLS63 AMG and SL350 - all are covetable models, and in this, our newest Mercedes Review Roundup installment, all managed to earn at least four stars (out of a possible five) from those that tested them. An impressive feat Source: www.emercedesbenz.com
Benz World – Mercedes Benz Discussion Forum. Source: www.benzworld.org
Tuning The Mercedes-Benz CL-Class: The Kicherer CL 60 Coupe – The Mercedes-Benz CL600 is an immaculate creature, offering an incredible amount of luxury along with an equally awe-inspiring level of power. But for those looking to add an additional dose of thrust to the model's 517-horsepower twin-turbo V12, Kic Source: www.emercedesbenz.com
Carlsson unleashes CK65 RS Eau Rouge Dark Edition – Filed under: Aftermarket, Tuners, Coupes, Mercedes-BenzClick above for a high-res gallery of the Aigner CK65 RS Eau Rouge Dark Edition Carlsson is continuing its line of two-tone Aigner-themed vehicles with the CK65 RS Eau Rouge Dark Edition. Previou Source: feedproxy.google.com
Mercedes-Benz Review Roundup: The SL65 AMG Black Series, GLK-Class, CLS550 And ML320 BlueTEC – Kicking things off this morning we've got a healthy dose of brand spanking new Mercedes reviews. In this, our latest Mercedes Review Roundup installment, a total of four models – the Mercedes-Benz SL65 AMG Black Series, the GLK-Class, the CLS550 and Source: www.emercedesbenz.com
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redpajama_set_name: RedPajamaCommonCrawl


Jayanti "Jonty" Rajagopalan (95PMIR) has started her own company - Detours India currently focused on Hyderabad in particular.
Detours India offers Historical, Slice of Life, Culinary and Arts & Crafts tours of the city.
The Culinary tours that she organizes was covered on BBC Worlds's India Business Report.
Watch the clip on youtube.


Pop the top and the fun won't stop with our sweet and salty Caramel Popcorn lip balm. Forget the calories, sugar and carbs next time you are at the movies just pop on your caramel popcorn balm without guilt.
This lip balm makes you think about fall festivities!!! I love this one too it smells great and makes your lips look great too!!!


I've been keeping an eye on our new wireless (SonicPoints!), and noticed something interesting… This 60-minute image was captured at 10:17. Can you guess what time people started coming into the sanctuary?
I'm guessing that it's probably a combination of bluetooth from all the cell phones in the room along with different RF dispersion patterns because the room filled up with a lot of very wet objects (humans) which absorbs 2.4GHz quite readily.


Postman's knock create beautiful, unique wedding and celebration stationery and decorations with a vintage feel that can be completely personalised to your requirements.
As well as personalised wedding invitations they also make handmade save the dates, place cards, guestbooks, orders of service, thank you cards and table plans.
The team will work with you every step of the way to create something personal and perfect to match your event or special occasion.
Contact Postman's Knock Drop us an email below or call us on 01270 748270 .


The Superintendent Of Documents policy for Dissemination and Distribution (SOD 301) explicitly limits what GPO will deposit into depository libraries to so-called "tangible" products. This policy can be judged a success only by looking backwards and evaluating it in terms of how well it adheres to old methods — continuing to send physical objects to depository libraries. By Looking Backwards and focusing on methods, depositing floppy disks or DVDs (a tiny improvement) seems like progress.
But the policy is a failure if we evaluate its outcomes. To evaluate the policy by Looking Forward we would ask if the outcomes of the policy match the long-term goals of the FDLP — not if the methods have remained unchanged. We would ask if the policy ensures the long-term preservation of digital government information and ensures that it can be accessible and usable in the future (in fact, this question should be asked of every policy decision!). Sadly, we know from experience that this policy has resulted in undesirable, counter-productive outcomes. It has complicated, inhibited, and in some cases prevented preservation and long-term access.
Digitizing Backwards is digitizing by comparing the digital objects we create to their paper originals. Instead of this, we should Look Forward when we digitize and create digital objects that stand up to current and future expectations of our user communities. Digitizing Backwards has produced digital objects that already fall short of users' expectations in many ways.
Designated Community: An identified group of potential Consumers who should be able to understand a particular set of information. The Designated Community may be composed of multiple user communities. A Designated Community is defined by the Archive and this definition may change over time.
Another way of expressing James' "Looking Forwards" is as taking the needs of the Designated Community into account in designing preservation processes.
Go read the rest of James' post.


9-161 (Stuart, 48.4 ov), 10-166 (Thompson, 49.3 ov).
DNB: GJ Rowell, PW Jackson.
7-104 (Seccombe, 34.3 ov), 8-159 (Bichel, 47.3 ov).
Team Pages: New South Wales, Queensland.
New South Wales Player Pages: KJ Geyer, R Chee Quee, MG Bevan, S Lee, CJ Richards, KJ Roberts, PA Emery, BE McNamara, SM Thompson, AM Stuart, DA Freedman, SR Clark.
Queensland Player Pages: ML Hayden, JP Maher, ML Love, SG Law, A Symonds, GI Foley, SA Prestwidge, WA Seccombe, AJ Bichel, BN Creevey, GJ Rowell, PW Jackson.
Match Officials' Pages: SJ Davis, DJ Harper, SJA Taufel.


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Sumner County Real Estate Investing Lunch
with... Gina Miller
Join us for networking at Brixx in Hendersonville, Everyone Welcome!
Wednesday, June 28, 2023 1:00 PM - 2:00 PM
300 Indian Lake Blvd Hendersonville, TN 37075
Led by REIN member Gina Miller
Open to new investors, experienced investors, and anyone interested in investing in the area. Curious about REIN? Stop by and meet us, you don't need to be a member to attend! This group meets the last Wednesday of every month.
Gina Miller is a sponsor of REIN and owner of 5 Points Realty
Date: Wednesday, June 28, 2023
300 Indian Lake Blvd


The most rumored smartphone from ONEPLUS, OnePlus 7 and 7 pro has surfaced online just before it's formal launch. OnePlus 7 sport a waterdrop-style notch, and OnePlus 7 Pro will sport a Popup selfie camera setup. Accoring to rumored renders, The OnePlus 7 will feature dual rear camera setup and OnePlus 7 Pro will feature a triple rear camera setup. It is expected that OnePlus can launch their new smartphone OnePlus 7 and & 7 Pro on May 14.
Courtesy OnLeaks and PriceBaba leaks suggests us that the design of OnePlus 7 could be similar to OnePlus 6T. if talk about OnePlus 7 Pro than it's design can be different from OnePlus 7, as OnePlus 7 doesn't has any notch at screen, instead of a notch it will have a popup selfie camera setup and OnePlus 7 leaked images shows us that it features a waterdrop-style notch at the top of screen.
The ports and button placement on the smartphone OnePlus 7 are also identical to OnePlus 6T, there is an alert slider along with a power button on the right, while at left side has a sim tray and volume buttons. At the bottom it's appear that it will have a USB Type-C port. It is also claimed that the smartphone OnePlus 7 will have 157.7×74.8×8.1mm in dimensions and will 9.5mm of thickness.
If we talk about OnePlus 7 Pro, than it will have a new design and sports a popup selfie camera setup and triple rear camera setup. According to surrounded rumors, the OnePlus 7 Pro will have a dual-edge display panel.
Also Read: Top five smartphone under 10,000 inr.
According to the renders surrounded, the tipster @OnLeaks Twitter account, has leaked the key specs of the OnePlus 7 and 7 Pro. The OnePlus 7 is claimed to sport a 6.4 inch flat display along with a waterdrop-style display notch and claimed that it will have dual rear camera setup with 48MP primary sensor.
It is said that OnePlus 7 Pro will sport a 6.64 inch curved display with Popup front camera setup with triple rear camera setup with 48MP primary sensor. It is also claimed that rear camera setup include with telephoto and ultra-wide angle lenses.


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Andres Noormets (nascido em 1 de outubro de 1963, em Paide) é um actor, director e pedagogo teatral da Estónia.

Em 1988 formou-se no Conservatório Estatal de Tallinn pelo Departamento de Artes Cénicas.

Além de ter desempenhado papeis no teatro, ele também actuou em vários filmes e séries de televisão (por exemplo Wikmani poisid, de 1995).

Em 2018 foi premiado com a Ordem da Estrela Branca, IV classe.

Diretores de teatro da Estônia
Nascidos em 1963
Pessoas vivas
Atores da Estônia

redpajama_set_name: RedPajamaWikipedia


Increased use of pesticides is killing off the nation's bees and putting pollination at risk, according to scientists at The University of Reading. The university has published a report, The Decline of England's Bees, as part of a national campaign by.
Get a free car insurance quote and discover great savings and benefits Texas drivers receive with the AARP Auto Insurance Program from The Hartford.
Found a better deal on auto coverage? Follow this plan for switching car insurance smoothly.
The Motor Ombudsman is the UK's government-backed, self-regulatory body for the motor industry. Find a garage you can rely on and resolve motoring disputes.
Save money on your car insurance. Saving you money, without compromising on cover. We listen to your needs and find the policy that's right.
Should You Purchase Rental Car insurance? You might already be covered for your summer road trip.
Sgt Alex Willis, from the Whitley neighbourhood team, said: "From information we have received it would appear these motorbikes are unlikely to be roadworthy or covered by either tax or insurance. "We have also heard the riders are riding around with.


IPSNP's HYDRA presented at Canadian AI in Industry, Challenges in Real Life Products | IPSNP Computing Inc.
HYDRA is a semantic data federation engine designed for subject matter experts with minimal technical expertise. Using a graphical query canvas consultants and business analysts can construct complex queries using a series of simple keywords or key-phrases. Completed graphical queries are translated to SPARQL and issued to HYDRA. Employing a registry of SADI semantic web services the HYDRA is able to automatically discover services, plan and execute complex workflows according to an analyst's ad-hoc query needs. In this demonstration consultants use HYDRA to select crop varieties, dynamically compute profit margins, and determine appropriate pesticides for each crop variety.
IPSNP invited to present at the 2017 G20 (MACS-G20) Annual Meeting of the Agricultural Chief Scientists of the G20 (MACS-G20).


Information about CBC News, how we operate and how to contact us.
Founded in 1941, CBC News is Canada's publicly owned news and information service. We are rooted in every region of the country and report on Canada and the world to provide a Canadian perspective on news and current affairs.
Our mission is to inform, to reveal, to contribute to the understanding of issues of public interest and to encourage citizens to participate in our free and democratic society.
We have journalists stationed in over 40 cities across Canada. We also have bureaus in London, Jerusalem, Beijing, Washington, New York City, Los Angeles and Moscow.
CBC News uses pop-up bureaus as well, with reporters who fly in when a story occurs beyond our existing bureaus.
CBC News is committed to accurately reflecting the range of experiences and points of view of all citizens. All Canadians, of whatever origins, perspectives and beliefs, should feel that our news and current affairs coverage is relevant to them and lives up to our principles.
We have a special responsibility to reflect regional and cultural diversity, as well as fostering respect and understanding across regions.
In a world where the line of what constitutes journalism appears to be increasingly blurred, CBC/Radio-Canada holds itself to the highest standards of journalistic practices.
All employees of CBC News, as well as the content they create, must respect the principles of accuracy, fairness, balance, impartiality and integrity as expressed through the Journalistic Standards and Practices.
We are aware of the impact of our work and are honest with our audiences. We do not hesitate to correct any mistake when necessary nor to follow up a story when a situation changes significantly. We do not plagiarize.
You can find links to report a correction or typo at the bottom of any CBC News story.
The office of the ombudsman reviews our practices against the standards set out in our Journalistic Standards and Practices. We openly provide the public with the means to judge and hold us to account by sharing with it how we measure the quality and standards of our journalism.
If you wish to make a complaint or raise a concern regarding News or Current Affairs content, you may write to or email our independent ombudsman, who is responsible for ensuring compliance to the CBC/Radio-Canada Journalistic Standards and Practices.
CBC News is a member of the Trust Project, an international consortium of news organizations collaborating to use transparency as a way to educate readers and promote news that lives up to fairness and accuracy.
The Trust Project is developing tools that help users differentiate high-quality news online. A system of 'trust Indicators' are designed to tell audiences and news distribution platforms that the editorial is ethically produced, accurate news.
CBC News uses labels as signals to help readers identify the status of different kinds of stories. Labels are used to flag stories that are breaking news or contain live elements such as video.
CBC News also uses labels to distinguish clearly between analysis and opinion.
Analysis — Here, reporters may make observations and draw conclusions based on facts as well as their own experience and expertise.
Opinion — On an exceptional basis, we may choose to appoint certain journalists as columnists. They have licence to express their opinions. To protect the integrity of CBC's journalism, we will restrict the role of such columnists to opinion and commentary. Their work will be clearly identified as opinion.
When presenting content where a single opinion or point of view is featured, we ensure that a diversity of perspective is provided across a network or platform and in an appropriate time frame.
CBC News stories based on original reporting carry reporter bylines, which include information about the author such as expertise, location, biographical and contact information. Bylines link to author pages that include other articles by the writer.
Some CBC News stories are combined from a variety of sources, including one or more news agencies and CBC News staff. They are written and edited by CBC News but carry a generic CBC News byline.
CBC News also publishes stories from different news agencies, including The Canadian Press, The Associated Press and Reuters. These stories may be edited by CBC News to add information or change grammar and spelling to our style. They are published under agency bylines, indicating when significant material has been added by CBC News.
All stories published under the CBC News banner are held to our journalistic standards and practices.
The CBC News contacts page has details on how to reach the CBC newsrooms across Canada, contact a CBC News personality, pitch an idea or give feedback on something you heard over the air or read on our website.


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Why Girls Aren't Geeks
On: November 7, 2002
There may be more male geeks than women because women don't have the genetic programming for "geekiness." Women are programmed to be more adept in social situations because genes on the female X chromosome seem to give them better social skills. Men have only a single X chromosome, while women have two.
"Having two X chromosomes may be protective against whatever predisposes someone to not being able to make sense of the social world," says Dr. Ruth Campbell. "It makes sense for women (because)?their survival, and that of their babies, is particularly dependent on reading social situations accurately."
A study of women with Turner's Syndrome, a female genetic condition caused by a missing or defective X-chromosome, shows that women with Turner?s have difficulty with social interactions and find it hard to read body language, despite having normal intelligence. These women were shown pictures of two faces, one looking directly forward and the other with a slightly averted gaze. The researchers found that women with Turner's Syndrome couldn?t tell if they were being looked at directly.
Campbell says men aren?t necessarily bad at social interaction, but if you isolate a group of people who are socially inadequate, you?re more likely to find men there than women. Autism and autism-like conditions such as Asperger's syndrome are far more common in men, but scientists don?t know why. David Potter of the National Autistic Society thinks it could be because of their lack of a 2nd X chromosome. He says, "It's another small piece of the jigsaw but it's not going to explain all cases of autism."
Geeks, note this: Scientists studying people who go to clubs and discos have discovered you get up to 400% more attention if you take off your glasses.
London psychologists told 38 volunteers, aged 18 to 26, to try to pick up a member of the opposite sex at a London bar. The male and female volunteers were split into three groups. One group was told to wear their glasses, the second to change from contact lenses to glasses and the third to change from glasses to contact lenses. "Changing methods of eyesight correction proved to have a far-reaching effect on the volunteers' feelings of self-confidence?85% of those that had switched to contact lenses reported increased self-confidence," says Dr. June McNicholas. "By comparison, not one of those that had switched to glasses said the same. On the contrary, 75% of them complained of feeling less confident?80% of those who wore glasses on the night felt less able to attract a mate." But McNicholas says, ?In other contests, such as a job interview, glasses are an advantage."
There?s no need to leave your glasses at home if you learn the ?10 Secrets for Success and Inner Peace? from Wayne Dyer,click here. Now on sale for Christmas!
To learn more,click here and here.
Tags: geeks
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Lumines LARGO M2 is one of the mounting profiles designed for LARGO surface profile. It is an assembly power supply profile for LARGO profile. For the aesthetic hiding of the power supply in surface assembly.


Peter, altogether there are seven covenants, God's biblical promises to mankind. Here are brief explanations of three of them.
Biblical promises are God's covenants with mankind.Genesis 9:11 is God's promise to Noah and his family to never again use a flood to destroy the Earth. His covenant with Abraham was unilateral (by God alone) and unconditional (nothing mankind did would annul God's covenant). This covenant was threefold. The promise of land, descendants, and salvation and redemption (Christ Jesus is Abraham's descendant), Genesis 12:1-3.
Finally there is God's new Covenant of Grace established at Jesus' death on the cross and His resurrection which defeated sin and death forever and is our promise of eternal life in Him for all who believe. God's Grace Covenant is understood in His mercy, forgiving us while we were still sinners, His grace, the gift of salvation we could never earn on our own and, His eternal and infinite love which is the reason for His mercy and grace. God truly is love.
The principles of Scripture are different in scope and application. Old timers called it 'rightly dividing' Scripture. Here's a short explanation.I'll provide a link at the end.
First, all Scripture is God-breathed, 2 Timothy 3:16-17 meaning you can believe what the Bible says. Secondly, the entire Bible is written for you but not all of it is written directly to you. Using hermeneutics and exegesis, which go hand-in-glove, are the way we come to the correct interpretation and application of the Bible. The name for that is Christian Apologetics and there are volumes written on that subject.
An example is the change from Mosaic Law (the 10 commandments et al) to our present covenant of grace. We are no longer bound to obey the OT laws because they were fulfilled in Jesus' death and resurrection and don't apply, 2 Corinthians 5:16-21.
I hope this helps to answer your question. Let eBible know if this wasn't what you wanted by commenting below.


Report says, Chris Ngige, minister of labour and employment, received the report on Wednesday.
"There were several transfers of funds in bank accounts without authorisation and approvals. The sum of N15, 737,757,697.91. was transferred from one account to another.


text:
Lingle Guide
Power to protect
Crystal R. Albers/Lingle Guide Hogan Hoffman and mother, Darby, watch as he receives his flu shot Wednesday at Goshen County Public Health in Torrington. National Infant Immunization Week begins Saturday, April 27 and runs through Saturday, May 4.
By: Crystal R. Albers - Updated: 2 months ago
GOSHEN COUNTY – Beginning Saturday, April 27, and running through Saturday, May 4, the U.S. will observe National Infant Immunization Week – an event designed to highlight the importance of protecting infants from vaccine-preventable diseases and celebrate the achievements of immunization programs in promoting healthy communities, according to the Centers for Disease Control and Prevention.
National Infant Immunization Week is now in its 25th year, and the CDC estimates routine immunization of children born between 1994 and 2018 will prevent an estimated 419 million illnesses, 26.8 million hospitalizations, and 936,000 early deaths over the course of their lifetimes, at a net savings of $406 billion in direct costs and $1.88 trillion in total societal costs.
"As a parent, you want to protect your little one from harm," Goshen County Public Health Director Kelly Beard said. "Vaccinating your baby according to the recommended immunization schedule gives him the best protection against 14 serious childhood illnesses – like measles and whooping cough – before he is two years old."
National Infant Immunization Week social media events and activities include:
Tuesday, April 30: #ivax2protect Twitter Storm
ν Join CDC, the American Academy of Pediatrics, and What to Expect and share why you vaccinate during the #ivax2protect Twitter Storm on Tuesday, April 30, from 5 to 7 p.m.
ν Encourage your followers to share why they vaccinate on time and why they support immunization.
ν Continue the conversation and use the #ivax2protect hashtag throughout the week.
Thursday, May 2: Instagram Story Q&A
ν Parents of young children may join CDC for an Instagram live event answering parent's vaccination questions on Thursday, May 2, 10 to 12 p.m.
ν Submit your questions during the event on the @CDCgov Instagram page.
The CDC website cdc.gov, states 2019 also marks the 25th anniversary of Vaccines for Children, a program in which Goshen County Public Health participates. VFC is a federally funded program that provides vaccines at no cost to children who might not otherwise be vaccinated because of their family's inability to pay. The VFC program helps children get their vaccines according to the recommended immunization schedule, and has helped increase childhood immunization coverage levels, making a significant contribution to the elimination of disparities in vaccination coverage among young children.
"However, we are seeing an increase in the number of children under two years old who are receiving no vaccines," the CDC said. "Data suggest(s) that many of these parents do want to vaccinate their children, but they may not be able to get vaccines for them. They may face hurdles, like not having a healthcare professional nearby, not having time to get their children to a doctor, and/or thinking they cannot afford vaccines."
Beard also emphasized the importance of the whooping cough vaccine during pregnancy, and the measles, or MMR, vaccine.
"Protecting babies from whooping cough begins before a baby is even born," Beard relayed from the CDC. "All pregnant women are recommended to receive the whooping cough vaccine (Tdap) during each pregnancy. The recommended time to get the shot is during the 27th through 36th week of pregnancy, preferably during the earlier part of this time period. This will help protect babies from whooping cough until they can receive their first whooping cough vaccine at two months." Learn more about the CDC's Born With Protection campaign at www.cdc.gov/pertussis/pregnant. Learn more about maternal vaccination at www.cdc.gov/vaccines/pregnancy.
In addition, despite being eliminated in the U.S., public health officials still report measles cases and outbreaks to the CDC. For example, the U.S. has already experienced outbreaks of measles during 2019. For measles resources and information on vaccination recommendations, including for children traveling internationally, visit www.cdc.gov/measles.
"Vaccination is a shared responsibility. Families, healthcare professionals, and public health officials must work together to help protect the entire community."
Call Goshen County Public Health a (307) 532-4069 to schedule an immunization appointment.
Doggers of the Quarter named
Spring concert …
WMCI names officer, employee of the year
Wyoming Reads …
Beginning Saturday, April 27, and running through Saturday, May 4, the U.S. will observe National Infant Immunization Week – an event designed to highlight the importance of protecting infants from vaccine-preventable diseases and celebrate the achievements of immunization programs in promoting healthy communities, according to the Centers for Disease Control and Prevention.
228 Main St Lingle WY 82223


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EMS Communications Interoperability
National Health Service England accelerates roll out of Motorola Solutions body-worn cameras
Thousands of ambulance staff across England to wear body-worn cameras following successful trials to enhance safety
CHICAGO – Motorola Solutions today announced that the National Health Service (NHS) England has selected its VB400 body-worn video solution to increase transparency and safety for its frontline workers and citizens across the country.
Successful trials with London Ambulance Service and North East Ambulance Service proved the rugged VB400 body-worn cameras offer greater transparency for staff and citizens to assist in de-escalating situations, leading to the accelerated roll out of the cameras across all trusts in England – three years ahead of the NHS Long Term Plan target. The foundation of safety that the cameras provide allows ambulance workers to remain focused on making life-saving decisions and delivering patient care.
"If we are unable to protect our staff, we are unable to provide a service that's fit for purpose for the public we serve", said Darren Green, clinical service manager at North East Ambulance Service. "The availability of body-worn cameras for our staff is something that we have championed for a long time and we are delighted to have led the trial to help implement them nationally."
"Looking after our staff and volunteers and keeping them safe is a top priority. These cameras should act as a deterrent and will also help provide evidence", said Dr John Martin, Chief Paramedic and Quality Officer at London Ambulance Service.
The cameras are easily activated by the wearer, securely assigned to a frontline worker using their ID badge and offer an extended battery life that lasts beyond the shift with up to 12 hours of recording. The ambulance trusts will deploy the body-worn cameras with VideoManager evidence management software to seamlessly upload and manage the recorded video as part of their workflow.
"The adoption of VB400 body-worn cameras across all ambulance trusts in England is a significant endorsement for how they help to ensure the safety and well-being of members of the public and ambulance crews", said Fergus Mayne, country manager and head of U.K. sales at Motorola Solutions. "With greater reassurance in their safety, ambulance workers can focus on what they do best - providing life-saving emergency care to the community."
About Motorola Solutions
Motorola Solutions is a global leader in mission-critical communications and analytics. Our technologies in land mobile radio mission-critical communications, video security & analytics and command centre software, bolstered by managed & support services, make communities safer and help businesses stay productive and secure. At Motorola Solutions, we are ushering in a new era in public safety and security. Learn more at www.motorolasolutions.com.
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Motorola Solutions acquires Rave Mobile Safety, a leader in mass notification and incident management
Reasons why you should spend your ARP funding now (infographic)
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Automatic Driver Identification from In-Vehicle Network Logs
by Mina Remeli, et al.
CrySyS Lab
Data generated by cars is growing at an unprecedented scale. As cars gradually become part of the Internet of Things (IoT) ecosystem, several stakeholders discover the value of in-vehicle network logs containing the measurements of the multitude of sensors deployed within the car. This wealth of data is also expected to be exploitable by third parties for the purpose of profiling drivers in order to provide personalized, valueadded services. Although several prior works have successfully demonstrated the feasibility of driver re-identification using the in-vehicle network data captured on the vehicle's CAN (Controller Area Network) bus, they inferred the identity of the driver only from known sensor signals (such as the vehicle's speed, brake pedal position, steering wheel angle, etc.) extracted from the CAN messages. However, car manufacturers intentionally do not reveal exact signal location and semantics within CAN logs. We show that the inference of driver identity is possible even with off-the-shelf machine learning techniques without reverse-engineering the CAN protocol. We demonstrate our approach on a dataset of 33 drivers and show that a driver can be re-identified and distinguished from other drivers with an accuracy of 75-85
Mina Remeli
Szilvia Lestyan
Gergely Acs
Gergely Biczok
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Almost all vehicles in use nowadays are equipped with various on-board Electrical Control Units (ECUs), sensors and actuators measuring and controlling the vehicle's speed, acceleration, braking, fuel consumption, battery status, or tire pressure level, among others. Sensors attached to their respective ECUs, whose number ranges from 5 to a hundred per vehicle, continuously generate a vast amount of real-time data. In order to implement complex control tasks for traffic safety or passenger infotainment, these data are then transferred among ECUs and other nodes over the in-vehicle network, most commonly following the Controller Area Network (CAN bus) standard [27] . In addition to being used real-time for automotive control, data are also transferred to car manufacturers through Internet-connected devices or pre-installed modems every half to five minutes. Apart from diagnostic and forensics purposes of car companies and their primary data analytics partners, these huge datasets also become exploitable by other parties such as insurance companies, retailers, advertisers and parking operators; in general, vehicular data gradually become part of the Internet of Things (IoT) ecosystem111www.forbes.com/sites/forbestechcouncil/2018/05/08/how-well-does-your-car-know-you/. Moreover, upcoming autonomous cars produce and may share an order of magnitude larger amount of real-time data (≈ 1 GB/s) than traditional cars. Indeed, data from vehicles are predicted to create a $10 trillion market and could become five times bigger than the market for the cars themselves in the next few years222bit.ly/2V14YkK.
Unfortunately, sharing in-vehicle network data raises serious privacy concerns. Although drivers are expected to opt-in to such data sharing333www.wsj.com/articles/what-your-car-knows-about-you-1534564861, it is still unclear what exact personal information they would transfer then to third parties. For example, can a skilled data analyst infer the driver's identity using only in-vehicle network data? Despite the inherently noisy nature of this fine-grained measurement data, the feasibility of driver identification has been demonstrated in several prior works [7, 14, 24, 31] . In particular, it is well-known that drivers can be re-identified in constrained environments if they follow the same route with the same car and sensor readings are available from the captured network logs [7] . However, when following different routes, unique driving patterns are more difficult to extract due to the variable traffic conditions. Also, it is much more plausible that an adversary is able to collect CAN logs from arbitrary routes, hence we stick with this scenario throughout this paper. Moreover, car manufacturers do not disclose the exact format of CAN messages in order to protect their intellectual properties against competitors, as well as the security of drivers against malicious car hacking. But is this approach of security/privacy by obscurity effective?
A straightforward solution could be to reverse-engineer the CAN protocol by extracting different sensor signals, such as velocity, steering wheel angle, etc., from the captured CAN messages. However, this requires the manual inspection of every possible time series whether they contain a given signal with sufficient predictive power. This process is tedious and often considered to be illegal [4] .
In this paper, we show that there is no need to reverse-engineer the CAN protocol to identify drivers. In addition, re-identification remains feasible without significantly constraining the driving environment; what's more, such inference is practical with off-the-shelf machine learning techniques readily available to anybody. This is a plausible scenario when a malicious actor gains access to the car's CAN bus through an Internet-connected device such as a smartphone using a wired or wireless data collector adapter plugged into the car's OBD-II connector [27] . For the purpose of inference, we blindly consider all possible time series that could be easily extracted from CAN logs without any sort of manual inspection of their content. We use machine learning techniques to select time series with sufficient predictive power and use their combination for classification. Our solution is scalable and does not require reverse-engineering the proprietary communication protocol used on the CAN bus.
Our specific contributions are twofold:
We demonstrate the feasibility of driver re-identification even if drivers follow different routes and the exact signals (such as velocity, acceleration, RPM, etc.) cannot be extracted directly from the captured CAN log. We extract a time series from every byte of every CAN message, from which the ones with the most predictive power are identified. In particular, we build a convolution-based neural network, where features from individual time series are learnt automatically by convolutional layers, which are then combined in a mixture model in order to perform the final classification. We achieve a mean accuracy of driver classification between 75-85% for CAN traces with a length of less than 2 minutes. By contrast, most prior works
[14, 7] achieved comparable result only with fewer drivers (2-15) and when the exact signals could be extracted and are readily available for classification.
We propose a scalable technique to classify a large set of time series even if many of the individual time series are significantly noisy and hence lack sufficient predictive power individually. Our approach first classifies each time series separately, and combines the best performing individual models to classify the whole set of time series.
Ii Background
Ii-a Signal Extraction from Controller Area Network (CAN)
The Controller Area Network (CAN) is a bus system providing in-vehicle communications for ECUs and other devices [27] . The overwhelming majority of cars use one or more proprietary CAN protocols. Generally, sensor signals in CAN variants have a sampling frequency in the order of 10 ms−1, i.e., an ECU packs the measured signal samples into a CAN message and broadcasts that on the CAN bus every 10 ms.
CAN messages can be eavesdropped through the standard on-board diagnostics (OBD, OBD-II) port. Sensor signals carried by OBD have a sampling frequency in the order of 1 second. However, in certain vehicle makes and models, one or more CANs are also connected to the OBD-II port. In such cars (like the one used in this paper), also utilizing OBD over the CAN physical layer, it is possible to extract fine-grained CAN data via an OBD-II logger device [27] . Indeed, collecting in-vehicle network logs is not a privilege of car companies any more. For example, insurance companies sell a complete kit which consists of a smartphone application as well as an ODB-II adapter. This adapter is then voluntarily installed by the driver and wirelessly connected to the driver's smartphone through Bluetooth444Other malicious parties may have access to the driver's device by installing a malicious application which then may gain access to the adapter..
However, owing to the confidential message format and semantics, extracting a sensor signal from the captured CAN messages is not straightforward. Table I shows a simplified picture of a CAN message555This example shows an already stripped message, i.e., we do not discuss end of frame or check bits.. A CAN bus message has several components including the timestamp, a message identifier (CAN-ID), a Remote Transmission Request which allows ECUs to request messages from other ECUs, and the actual data field with a size of at most 8 bytes. The Data field can be broken to sensor signals which are then transformed and/or converted to a human-readable format in order to enable further analysis [22] .
For example, the position of the brake pedal may be carried by the fourth byte of the message with ID 0x02c4 in Table I. Therefore, extracting the fourth byte of every consecutive instance of this message type (ordered by their timestamp) one can reconstruct the braking signal (i.e., position of the brake pedal over time, which is shown in green in Table I). Importantly, the message ID, offset and length of a signal are not standardised and kept confidential by OEMs. Consequently, extracting a signal from recorded CAN messages would require (partly) reverse-engineering the specific CAN protocol which may be illegal under practical circumstances [4] .
CAN-ID
000 0x8 0x00 0x00 0x32 0x00 0x0e 0x32 0xfe 0x3c
0x02c4
000 0x8 0x82 0xc8 0x00 0x0f 0x03 0x00 0x92 0x3c
000 0x8 0x82 0xcc 0x00 0x0f 0x08 0x00 0x92 0x5a
0x018e
000 0x8 0x03 0x03 0x00 0x00 0x00 0x00 0x07 0x3f
0x00f1
000 0x6 0x28 0x00 0x00 0x40 0x00 0x00
000 0x8 0x82 0xd2 0x00 0x0f 0x0c 0x00 0x92 0x5d
000 0x8 0x82 0xa1 0x00 0x0f 0xa1 0x00 0x92 0x4d
TABLE I: Example of CAN messages and the extracted time series. The red, green, and blue time series are obtained by extracting the 1st, 4th and 7th byte of every time-ordered CAN message with ID 0x02c4, respectively.
Ii-B Convolutional Neural Networks for Automatic Feature Extraction
In recent years, convolutional neural networks (CNNs) have significantly progressed the fields of audio and visual pattern recognition
[21] . CNNs are capable of automatically learning complex feature representations using their convolutional layers. Specifically, CNNs produce a set of (learnable) filters that can recognize patterns in a translation-invariant way. Suppose f
is a filter (or vector) with size
n and T is a time series also represented by a vector of values. Then, the 1-dimensional discrete convolution of f and T is given by
(T∗f)[i]=n∑j=1f[n+1−j]⋅T[i+j−1]
Filters are learnt in order to extract different features from the time series using convolution. For instance, if f=[1,−1], T∗f equals the gradient (i.e., difference) between any two neighboring points. Hence, there is no need to manually craft such features since the value of T∗f (or its simple functions such as maximum or average) is used as "features" and are further fed into a classification model. By changing the number and size of filters as well as stacking multiple convolution layers, a CNN can learn to detect many different local traits of a time series. For example, if T is the velocity, then convolving T with f=[1,−1]
yields the acceleration. Therefore, by applying max pooling on the convolved signal the model can learn to distinguish drivers based on their maximum acceleration. Although, in theory, CNN could learn many different transformations of the time series such as discrete Fourier transform, such specific functions are almost never learnt in practice
[25] . It is still an open question what exact functions are learnable by CNNs.
If (T∗f) is evaluated at every point of T, then the same feature represented by f can be detected along the whole time series T
. This makes CNNs particularly suitable to detect local features. Indeed, most drivers exhibit only local distinguishing features (e.g., the short moments when they accelerate or decelerate) as the signal is globally shaped by traffic conditions rather than by the driver's behaviour.
CNNs are also more scalable than traditional fully connected neural networks (FCNNs), because the same filters are applied along the whole time series, that is, the number of learnable parameters is proportional to the number and size of filters and not to the size |T| of the whole time series. This also makes CNNs less prone to overfitting.
Ii-C Long Short Term Memory Networks
A Recurrent Neural Network (RNN) uses memory to model short-term dependencies in sequential data. When folded out in time, it can be considered as a feedforward deep neural network with indefinitely many layers. RNNs generally receive input and produce output at each time step. Long-term temporal dependencies are difficult to model with RNNs because the gradient of the loss function decays exponentially with time (the vanishing gradient problem). Another weakness of common RNNs is that they do not explicitly support multiple time scales, and any temporal hierarchy that is present in the input signal needs to be embedded implicitly in the network. However, time series often have a temporal hierarchy, where the information is scattered over multiple time scales. Long Short-Term memory (LSTM) networks
[18] , a special type of RNNs, overcome the above problems. The recurrent weights in an LSTM (learnt during the training phase) select which information they need to pass onwards vs. which they need to discard. A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate. The cell remembers values over arbitrary time intervals, while the three gates regulate the flow of information into and out of the cell.
Iii Inference model
Iii-a Overview
Our premise is that the signals represented by the time series are unknown, therefore, for the purpose of inference, we use an artificial neural network (ANN), where the features of a time series are automatically extracted and learnt by multiple convolution layers. This is advantageous as the manual feature extraction of an unknown signal would be difficult otherwise.
We propose a mixture model with two main components:
Individual time series (ITS) model: This model is specialized to the classification of a single time series. We build one ITS model per time series, hence we obtain N different ITS models altogether, where N is the number of all time series. A time series is extracted by concatenating the same byte of every CAN message with identical message ID.
Mixture model: The already trained ITS models are combined into a single fully connected decision/ouput layer which provides the final classification result.
Importantly, ITS models are trained individually and separately from the mixture model, which makes our solution scalable. In particular, each ITS model can be trained independently and in parallel, and the training of the whole mixture model does not require to fine-tune or re-train the already trained ITS models. Once ITS models are trained, their weights are frozen and not modified during the training of their mixture model.
Next, we describe the model architecture in more details. The architecture of our proposal is illustrated in Figure 1.
Iii-B Data augmentation
We first divide the time series into equally-sized segments, from here on referred to as samples. Given a single sample as input, our model attempts to classify it into its correct class (e.g., driver ID). Due to the limited amount of data, and similarly to prior works [5, 30] , we use a sliding window over the whole time series to create samples. Specifically, let T=(t1,t2,…,t|T|) denote a time series, and let Ti:j=(ti,ti+1,…,tj) be a window of T between positions i and j (1≤i≤j≤|T|). Then, the set of all n samples created from T is given by {T1,n,T1+ℓ,n+ℓ,…,T|T|−ℓ+1,|T|}, that is, all consecutive windows shifted by ℓ time slots.
Iii-C Individual Time Series (ITS) Model
We use a CNN combined with Long Short-Term Memory (LSTM) [18] . Our ITS model is inspired by [30] ; however, it also differs in several aspects as we explain in Section V.
First, the input sample is divided into equally-sized non-overlapping k segments. Each segment si of a sample is transformed into a higher-level representation/features CNN(si)
using the same CNN model. The CNN model is composed of two 1-dimensional convolutional layers (with Rectified Linear Unit activations), which are separated by a max pooling layer in order to reduce model complexity, and a fully connected layer producing the output of the CNN. The segment size should be sufficiently large in order to capture any human driving reaction (e.g., acceleration, deceleration, gear switching, etc.), but still small enough to focus on distinctive driver features.
As the ITS model is applied on each segment of the sample independently, it can only model local features of a segment except for the sequential information along the sequence of segments as well as any global features of the whole sample. For this reason, we use an LSTM layer (with tanh activation) and it is applied on the sequence of local features extracted by CNN. In particular, a single CNN model is used to extract features from every segment si. The sequence of these representations are further processed by LSTM, which computes time-dependent features by transforming the input sequence into another sequence h1,h2,…,hk composed of the hidden states of the LSTM model. Specifically, for input segment si, hi=LSTM(CNN(si),hi−1). A hidden state hi is produced for every input segment si, dependent on the current input as well as all previous input segments through hi−1. The sequence of h1,h2,…,hk is fed into an attention layer in order to select those parts of the sequence which represent distinctive driver features. In particular, a sample may contain only a few segments with clear distinctive features of the drivers such as a unique pattern of speeding, steering, or braking, while the rest may lack any unique driving pattern and hence bearing less importance in distinguishing drivers.
LSTM is ineffective to model sequential dependencies if the input sequence is too long (i.e., there are too many segments), which is alleviated by attention as follows. Several attention mechanisms have been proposed in the literature mainly for text classification and machine translation [3, 26] . In this work, we use a simple mechanism [26] which provides the best performance for our dataset. The attention mechanism computes the weighted average of all the hidden states h1,h2,…,hk produced by LSTM along the whole input sequence. More specifically, the attention mechanism is defined as
ui =tanh(w⊤hi+b)
αi =exp(ui)∑iexp(ui)
a =∑iαihi
where w∈Rk and b∈R are jointly learnt parameters, and ai is the attention vector which represents sample i. In essence, a is the weighted average of each segment's LSTM representation, where the weights are a non-linear function of these representations. This function is modeled by a shallow neural network with weights w and bias b.
Finally, the attention vector a is provided to a fully connected layer with tanh activation, whose output is then given to a final softmax (multi-class classification) or a sigmoid layer (binary classification). We apply dropout regularization on every fully connected layer of our model.
Fig. 1: Our mixture model.
Iii-D Mixture model
ITS models classify individual time series and therefore each can have very different predictive performance. Indeed, some time series contain more distinctive driver features (e.g., brake pedal position) than others (e.g., velocity). In fact, it turns out that the majority of time series often do not have any predictive power at all.
We combine ITS models into a single Mixture model by connecting all ITS models to the same fully-connected neural layer, called mixture layer, whose output provides the final prediction. We first remove the output layer of the already trained ITS models, and merge the remaining part into the mixture layer. Finally, the whole mixture model is trained by adjusting the weights of only the mixture layer while keeping all parameters of the individual models intact. In case there are too many ITS models and the training of the mixture model is not feasible due to memory constraints, only the top-K best performing ITS models are used in the mixture. Also, the trained ITS models can be compressed by distillation [17] .
In fact, our mixture model is a sort of committee machine [15] , where multiple neural networks, called as experts, are combined to provide the final classification. Indeed, each ITS model is an expert which is specialized to extract a high -level representation of a single time-series. These representations are non-linearly combined to produce the classification.
The benefit of the mixture layer is to model inter-dependencies between different time series which may improve classification accuracy. For example, the acceleration and deceleration patterns individually may not be unique to a driver but their combination is.
Iv Evaluation
In this section, we evaluate our mixture model described in Section III.
Iv-a Dataset
As CAN data logs are not widely available, we conducted a measurement campaign. For data collection in particular we connected a logging device to the OBD-II port and logged all observed messages from various ECUs. Such a device acts as a node on the CAN bus and is able to read and store all broadcast messages. Our team developed both the logging device (based on a Raspberry PI 3) and the logging software (in C).666Note that it is common that the OBD-II connector is found under the steering wheel. Also note that not all car makes and models connect the CAN serving the drive-train ECUs (or any CAN) to the OBD-II port (e.g., Volkswagen, BMW, etc.).
Most routes were driven inside or close to Budapest; approximately 15-20% was recorded on a motorway. Drivers were free to choose their way, but still conforming to three practical requirements: (1) record at least 15 minutes of driving in total, (2) do not record data when driving up and down hills, (3) do not record data in extremely heavy traffic (short runs and idling). Free driving was recorded for all 33 drivers with an Opel Astra 2018: 11 people were between the age of 20-30, 8 of 25-30, 7 of 35-40 and and 7 above 40; there were 5 women and 28 men; 12 with less experience (less than 7000 km per year on average or novice driver), 11 with average experience (8-20000 km per year), and 10 with above average experience (more than 20000 km per year). All drivers drove between 10am and 4pm on weekdays and the average trace length is 29.81 minutes with a standard deviation of 13.48, the shortest trace is 17.71 minutes long, whereas the longest trace is 85 minutes long. The data were collected with the users' explicit consent after informing them about the purpose of the study. Exact identity of drivers was not recorded except for their personal attributes described above. Traces of different drivers were linked together using random identifiers.
Gender Male 28
Age [20-25] 11
[25-30] 8
Experience Low 12
TABLE II: Dataset summary.
Iv-B Extracting time series from CAN logs
The CAN logs of all drivers contain 53 different message IDs from which we extract 72 different time series per driver. More specifically, we consider all single bytes of every message, and a single time series corresponds to the series of bytes retrieved from the same byte position of all consecutive messages with the same message ID, which is also illustrated in Table I. We dropped all time series (1) with a single constant value in all time slots, (2) that were too short, (3) contained the values of counters. We retained the time series which occur in each driver's trace. This pre-processing finally yields 72 time series per driver. Accordingly, we built 72 different ITS models, each corresponding to a single time series. The validation accuracy of each ITS model is evaluated, and the top-10 best performing ITS models are selected and used in the mixture model.
Iv-C Training and Testing data
In order to train an ITS model which is specialized for the classification of a single time series, we build two datasets T and S, which are further used to create the training and testing datasets for any classification problem as follows.
First, each time series of every driver is divided into two parts. Samples extracted from the first part of every trace, using the sliding window technique described in Section III-B, are added to T. S is created analogously from the second part of every trace. Since the size of the first part is always set to 80% of the whole trace, the training samples will roughly cover 80% of all the samples from all drivers. Training data is obtained by picking an identical number of samples per class from T, and testing data is generated from S. These guarantee that (1) the training and testing sets do not overlap, (2) training data is always balanced, and (3) the proportion of samples from a driver is identical both in the training and the testing data (i.e., there is no dataset shift).
Iv-D Hyperparameters
Sample sizes are varied among 20, 60, and 120 seconds, and consecutive samples are shifted by 0.1 second. For ITS models, samples are divided into non-overlapping segments with a size of 3 seconds, which is believed to be sufficient to capture the most important driving actions. Each convolutional layer has a kernel size corresponding to 0.5 seconds with a stride of 0.05 seconds. The first convolution layer has 20 filters while the second layer applied on the output of the first layer has 40 filters followed by batch normalization
. Max pooling is applied on the result of every 5 consecutive convolution operations of the first layer. All FCNNs are composed of 64 units, and the number of LSTM's hidden states is 16. ITS models are trained as long as validation accuracy improved: 2 epochs for ITS models and 4 epochs for the Mixture model on average. All models are trained with RMSprop
777www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf with a fixed learning rate of 0.001. The output layers use softmax activation for multi-class classification and sigmoid activation for binary classification, which are optimized against categorical and binary cross-entropy, respectively, for all models.
Iv-E Results
We use the model described in Section III for all classification tasks888
We implemented our model in Python 3.6.7 using the framework of Keras 2.2.2. Evaluation was done on commodity hardware with Intel Core i7-4770 CPU and 32 GB RAM, where the average time training of a mixture model is 294 sec, and one ITS is trained in 157 sec on average. A powerful GPU can considerably speed up training.
. To measure performance on the testing data, we define testing accuracy as all correct classifications (TP+TN, True Positive plus True Negative) divided by all classifications:
Acc=TP+TNAll
Iv-E1 1-vs-all
Table III shows the re-identification accuracy in the 1-vs-all scenario, i.e., when one driver is distinguished from all other drivers with a single binary mixture model. We show the mean value over all 33 drivers (i.e., the mean accuracy over all the 33 models), standard deviation, and the extreme values for a specific driver. It is easy to see that accuracy improves sublinearly with the length of the sample trace, the mean ranging from 75% up to 85%. Maximum accuracy values for the most correctly classified driver are between 95% and 100%, while minimum values for the hardest-to-classify driver are between 43% and 46%.
Table IV depicts the 1-vs-all re-identification accuracy depending on the personal attribute values, which include gender, age, and driving experience. Interestingly, the most experienced drivers are the easiest to distinguish from all the other drivers with a mean accuracy of 86%, which shows that, by practicing, people tend to develop more unique skill sets. This phenomenon has also been confirmed in other domains [20] .
Sample length
Std.dev.
0.758 0.017 0.949 0.432
TABLE III: Re-identification accuracy: 1-vs-all
TABLE IV: Re-id. accuracy: 1-vs-all, sample length: 60 secs
Iv-E2 Many-vs-all
Fig. 2 shows the re-identification accuracy in the many-vs-all scenario, i.e., when a group of drivers is distinguished from all other drivers with a single multi-class mixture model, further characterizing the performance of our method. In the 2-vs-all case, we randomly select 50 different groups of 2 drivers out of the possible (332), and plot the mean value and the standard deviation; we do similarly with group sizes of 4, 10 and 33 (with only 1 possible allocation in the last, all-vs-all case). Notice that all-vs-all corresponds to the case when each driver is distinguished from each other using a single model. We observe that the mean value of accuracy is higher when the group size is smaller, ranging from the highest mean of 40% (all-vs-all, trace length 60 s) up to 91% (2-vs-all, trace length 20 s). It is interesting that a larger sample length does not always translate into higher accuracy; in fact, in almost all displayed cases the highest mean accuracy comes from models built on 60 s long samples (the exception to the rule is the 4-vs-all case, where the 120 s sample length yields slightly better results). Note that even in the hardest, all-vs-all case, our approach provides a 13-fold accuracy improvement compared to the 1/33=3.03% baseline.
Fig. 2: Re-identification accuracy: many-vs-all
V Related work
In this section, we give an overview on the related work with regard to driver re-identification and neural network based time series classification.
V-a Driver re-identification
Driver characterization based on driving data has gathered significant research interest from both the automotive control and the data privacy domain. In automotive control, substantial research efforts were made in modeling and recognizing driver behavior based on CAN and other driving data with the objective of designing and improving intelligent vehicle systems and providing enhanced driver safety features [28, 23] . On the other hand, the potential re-identification of drivers (i.e., singling out) based on recorded CAN data also became a focus for data privacy research [7] . The common trait in these works is the presumed familiarity with the specific CAN protocol stack including the presentation and application layers giving the researchers access to sensor signals. This knowledge is usually gained via access to the OEM's documentations in the framework of some research cooperation. As such, researchers do not disclose such information to honor their contract and preserve confidentiality.
Miyajima et al. collected and investigated sensor signals in both a driving simulator and a real vehicle. Using car-following patterns and spectral features of pedal operation signals authors achieved an identification rate of 89.6% for the simulator (12 drivers) and 76.8% (276 drivers) for the field test. Hallac et al. [13] discovered that maneuvers during turning could fingerprint drivers using a dataset provided by Audi. Via the same dataset, Fugiglando et al. [11] showed that four behavioral traits, namely braking, turning, speeding and fuel efficiency could characterize a driver adequately well. Zhang et al. [31] developed a driver classification model based on both CAN and mobile phone sensor data. While effective for distinguishing between pairs of drivers, authors claim that their method does not scale well. Enev et al. [7] make use of mostly statistical features as an input for binary (one-vs-one) classification with regard to driving behavior. These binary classifiers are combined into a multi-class classifier, which is not a scalable approach if there is a large number of drivers. Driving the same car in a constrained parking lot setting and a longer but fixed route, authors re-identified their 15 drivers with 100% accuracy. Authors had access to all available sensor signals and their scaling and offset parameters from the manufacturer's documentation. Fugiglando et al. [10] developed a new methodology for near-real-time classification of driver behavior in an uncontrolled setting. Despite their advanced use of unsupervised machine learning techniques they conclude that clustering drivers based on their behavior remains a challenging problem.
V-B Time series classification with neural networks
The field of time series classification (TSC) has proposed a multitude of algorithms. These algorithms are either distance-based, using k-NN classifiers over distance measures between time series; or feature-based, extracting deterministic features in either the time domain or spectrum and applying traditional classification algorithms. Lately, ensemble methods using multiple classifiers have also been studied. An overview and comparative performance evaluation of these methods are presented in [2] . Using neural networks is a novel direction in TSC [5, 29, 16, 9] . In order to perform adequately, these methods require either additional pre-processing of the input signals or very large datasets. For a comparative performance evaluation we refer the reader to the work of Fawaz et al. [9] .
Our proposed classifier is most similar to the DeepSense framework [30] and the Multi-channels Deep Convolutional Neural Networks (MC-DCNN) [32]
. Both works use CNNs to extract features from multiple time series which are fed into a Multi-Layer Perceptron (MLP)
or a Gated Recurrent Unit (GRU)
[30] . Our mixture model can be considered as a synergy of these two approaches. Unlike DeepSense [30] which applies 2 dimensional convolution on multiple time series, we apply 1 dimensional convolution on individual time series which are then combined in a fully connected layer as in MC-DCNN. We found this approach to be superior especially if many of the input time series are noisy and have no predictive power at all. Our approach can also be used to localize the parts of CAN messages which indeed identify a driver. Our model is also simpler (has fewer parameters) and hence is less prone to overfitting. Unlike MC-DCNN, we use LSTM with an attention layer per time series to focus on its most distinctive parts.
Vi Conclusion and Future Work
Our study demonstrates that driver re-identification remains feasible even without the manual inspection of messages captured on the CAN bus. In other words, profiling drivers can be performed even without reverse-engineering the CAN protocol.
The empirical evaluation of our approach on a dataset of 33 drivers demonstrates that a driver can be recognized with a success probability of 85% based on a mere 2 minutes of driving in urban areas, even if drivers follow different routes. Our findings include that drivers with substantial experience are the easiest to distinguish. As features are extracted automatically using convolutional neural networks, we conjecture that our technique is also applicable to predicting various other driver attributes such as age, gender, or driving experience; however, the necessary empirical justification would require a significantly larger dataset.
Our study does not only raise the flag to car drivers, but also to companies collecting in-vehicle network logs; the re-identification (and/or profiling) of drivers so effortlessly means that CAN logs indeed constitute personal data and, as such, are subject to the European General Data Protection Regulation (GDPR) [8] as of 25 May 2018. It remains an open problem whether more sensitive attributes such as health status are also predictable from CAN logs.
Our work also advocates the widespread adoption of standardized security protocols for the protection of CAN network traffic [12] . Although ad hoc solutions like dispersing the bits of a sensor signal within a CAN message would make our approach less effective, we conjecture that extracting signals from such obfuscated CAN messages remains feasible with appropriate statistical approaches.
It also remains an open problem whether captured CAN logs can be effectively anonymized. Although car companies stress that the collected network logs are "anonymized", the proper anonymization of such high-dimensional data is notoriously difficult in practice without significantly degrading accuracy
[1] . We believe that most viable approaches include the release of aggregate noisy statistics with provable privacy guarantees [6] .
Vii Acknowledgement
Gergely Acs was supported by the Premium Post Doctorate Research Grant of the Hungarian Academy of Sciences (MTA). This work has been partially funded by the Eu- ropean Social Fund via the project EFOP-3.6.2-16-2017-00002, by the European Commission via the H2020-ECSEL-2017 project SECREDAS (Grant Agreement no. 783119) and the Higher Education Excellence Program of the Ministry of Human Capacities in the frame of Artificial Intelligence re- search area of Budapest University of Technology and Economics (BME FIKP-MI/SC).
[1] C. C. Aggarwal (2005)
On k-anonymity and the curse of dimensionality
. In VLDB, pp. 901–909. Cited by: §VI.
[2] A. Bagnall, J. Lines, A. Bostrom, J. Large, and E. Keogh (2017-05-01) The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining and Knowledge Discovery 31 (3), pp. 606–660. External Links: ISSN 1573-756X, Document, Link Cited by: §V-B.
[3] D. Bahdanau, J. Chorowski, D. Serdyuk, P. Brakel, and Y. Bengio (2016) End-to-end attention-based large vocabulary speech recognition. In IEEE ICASSP, pp. 4945–4949. Cited by: §III-C.
[4] S. Bressman (2003) Restricting Reverse Engineering Through Shrink-Wrap Licenses: Bowers v. Baystate Technologies, Inc.. BUJ Sci. & Tech. L. 9, pp. 185. Cited by: §I, §II-A.
[5] Z. Cui, W. Chen, and Y. Chen (2016) Multi-scale convolutional neural networks for time series classification. CoRR abs/1603.06995. External Links: Link, 1603.06995 Cited by: §III-B, §V-B.
[6] C. Dwork (2006) Differential privacy. In ICALP, pp. 1–12. Cited by: §VI.
[7] M. Enev, A. Takakuwa, K. Koscher, and T. Kohno (2016) Automobile driver fingerprinting. PoPETs 2016 (1), pp. 34–50. Cited by: 1st item, §I, §V-A, §V-A.
[8] European Commission (2012) General European Data Protection Regulation (GDPR). Note: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2012:0011:FIN:EN:PDF Cited by: §VI.
[9] H. I. Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P. Muller (2018) Deep learning for time series classification: a review. CoRR abs/1809.04356. Cited by: §V-B.
[10] U. Fugiglando, E. Massaro, P. Santi, S. Milardo, K. Abida, R. Stahlmann, F. Netter, and C. Ratti (2018) Driving behavior analysis through can bus data in an uncontrolled environment. IEEE Transactions on Intelligent Transportation Systems (99). Cited by: §V-A.
[11] U. Fugiglando, P. Santi, S. Milardo, K. Abida, and C. Ratti (2017) Characterizing the driver dna through can bus data analysis. In ACM International Workshop on Smart, Autonomous, and Connected Vehicular Systems and Services, Cited by: §V-A.
[12] B. Groza, P. Murvay, A. V. Herrewege, and I. Verbauwhede (2017) LiBrA-can: lightweight broadcast authentication for controller area networks. ACM Trans. Embedded Comput. Syst. 16 (3), pp. 90:1–90:28. Cited by: §VI.
[13] D. Hallac, A. Sharang, R. Stahlmann, A. Lamprecht, M. Huber, M. Roehder, J. Leskovec, et al. (2016) Driver identification using automobile sensor data from a single turn. In IEEE ITSC, pp. 953–958. Cited by: §V-A.
[14] D. Hallac, A. Sharang, R. Stahlmann, A. Lamprecht, M. Huber, M. Roehder, R. Sosic, and J. Leskovec Driver identification using automobile sensor data from a single turn. In IEEE ITSC, Cited by: 1st item, §I.
[15] S. Haykin (1999) Neural networks: a comprehensive foundation. Prentice Hall. Cited by: §III-D.
[16] K. He, X. Zhang, S. Ren, and J. Sun (2016) Deep residual learning for image recognition. In IEEE CVPR, Cited by: §V-B.
[17] G. E. Hinton, O. Vinyals, and J. Dean (2015) Distilling the knowledge in a neural network. CoRR abs/1503.02531. External Links: Link Cited by: §III-D.
[18] S. Hochreiter and J. Schmidhuber (1997) Long short-term memory. Neural computation 9 (8), pp. 1735–1780. Cited by: §II-C, §III-C.
[19] S. Ioffe and C. Szegedy (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In ICML, Cited by: §IV-D.
[20] A. C. Islam, R. E. Harang, A. Liu, A. Narayanan, C. R. Voss, F. Yamaguchi, and R. Greenstadt (2015) De-anonymizing programmers via code stylometry. In USENIX Security, Cited by: §IV-E1.
[21] Y. LeCun, B. E. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. E. Hubbard, and L. D. Jackel (1989) Handwritten digit recognition with a back-propagation network. In NIPS, pp. 396–404. Cited by: §II-B.
[22] S. Lestyan, G. Acs, G. Biczók, and Z. Szalay (2019) Extracting vehicle sensor signals from can logs for driver re-identification. In 5th International Conference on Information Security and Privacy (ICISSP 2019), Note: shortlisted for Best Student Paper Award Cited by: §II-A.
[23] N. Lin, C. Zong, M. Tomizuka, P. Song, Z. Zhang, and G. Li (2014) An overview on study of identification of driver behavior characteristics for automotive control. Mathematical Problems in Engineering 2014. Cited by: §V-A.
[24] C. Miyajima, Y. Nishiwaki, K. Ozawa, T. Wakita, K. Itou, K. Takeda, and F. Itakura (2007) Driver modeling based on driving behavior and its evaluation in driver identification. Proceedings of the IEEE 95 (2), pp. 427–437. Cited by: §I.
[25] M. Nye and A. Saxe (2018) Are efficient deep representations learnable?. CoRR abs/1807.06399. External Links: Link, 1807.06399 Cited by: §II-B.
[26] C. Raffel and D. P. W. Ellis (2015) Feed-forward networks with attention can solve some long-term memory problems. CoRR abs/1512.08756. Cited by: §III-C.
[27] Z. Szalay, Z. Kánya, L. Lengyel, P. Ekler, T. Ujj, T. Balogh, and H. Charaf (2015) ICT in road vehicles—reliable vehicle sensor information from obd versus can. In IEEE MT-ITS, Cited by: §I, §I, §II-A, §II-A.
[28] W. Wang, J. Xi, and H. Chen (2014) Modeling and recognizing driver behavior based on driving data: a survey. Mathematical Problems in Engineering 2014. Cited by: §V-A.
[29] Z. Wang, W. Yan, and T. Oates (2017) Time series classification from scratch with deep neural networks: A strong baseline. In IJCNN, Cited by: §V-B.
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[32] Y. Zheng, Q. Liu, E. Chen, Y. Ge, and J. L. Zhao (2014) Time series classification using multi-channels deep convolutional neural networks. In WAIM, Cited by: §V-B.


text:
Managing Ubuntu 20.04 Users and Groups
During the installation of Ubuntu, the installer provided the opportunity to create a user account for the system. We should not lose sight of the fact that Ubuntu is actually an enterprise class, multi-user and multi-tasking operating system. In order to use the full power of Ubuntu, therefore, it is likely that more than one user will need to be given access to the system. Each user should have his or her own user account login, password, home directory and privileges.
Users are further divided into groups for the purposes of easier administration and those groups can have different levels of privileges. For example, you may have a group of users who work in the Accounting department. In such an environment you may wish to create an accounts group and assign all the Accounting department users to that group.
In this chapter we will cover the steps to add, remove and manage users and groups on an Ubuntu system. There are a number of ways to manage users and groups on Ubuntu, the most common options being command-line tools and the Cockpit web interface. In this chapter we will look at both approaches to user management.
1.1 User Management from the Command-line
New users may be added to an Ubuntu system via the command-line using the useradd utility. To create a new user account, enter a command similar to the following:
# adduser john Adding user `john' ...
Adding new group `john' (1001) ...
Adding new user `john' (1001) with group `john' ...
The home directory `/home/john' already exists.  Not copying from `/etc/skel'.
Enter new UNIX password: Retype new UNIX password:
passwd:
Changing the user information for john
Enter the new value, or press ENTER for the default
Full Name []:
Room Number []:
Work Phone []:
Home Phone []:
Other []:
Is the information correct? [Y/n] Y
By default, this will create a home directory for the user in the /home directory (in this case / home/john). To specify a different home directory, use the –home command-line option when creating the account:
You are reading a sample chapter from Ubuntu 20.04 Essentials. Buy the full book now in eBook ($14.99) or Print ($36.99) format. Includes 37 chapters. Learn more.
# adduser --home /users/johnsmith john
Once the account has been created, the password can be changed at any time using the passwd tool:
# passwd john
Changing password for user john.
all authentication tokens updated successfully.
An existing user may be deleted via the command-line using the deluser utility. While this will delete the account, the users files and data will remain intact on the system:
# deluser john
It is also possible to remove the user's home directory and mail spool as part of the deletion process:
# deluser --remove-home john
All users on an Ubuntu system are members of one or more groups. By default, new users are added to a private group with the same name as the user (in the above example, the account created for user john was a member of a private group also named john). As an administrator, it makes sense to organize users into more logical groups. For example all sales people might belong to a sales group, while accounting staff might belong to the accounts group and so on. New groups are added from the command-line using the addgroup command-line tool, for example:
# addgroup accounts
Use the adduser tool to add an existing user to an existing group from the command-line:
# adduser john accounts
To remove a user from a group, use the deluser command as follows:
# deluser john accounts
An existing group may be deleted from a system using the delgroup utility:
# delgroup accounts
Note that if the group to be deleted is the primary or initial group for any user it cannot be deleted. The user must first be deleted, or assigned a new primary group using the usermod command before the group can be removed. A user can be assigned to a new primary group using the usermod -g option:
# usermod -g sales john
To find out the groups to which a user belongs, simply run the groups command. For example:
$ groups john
john : accounts support
By default, only the first user account created on an Ubuntu system has the ability to use the sudo command to perform privileged tasks. If a newly added user attempts to use sudo, a message similar to the following will be displayed: john is not in the sudoers file. This incident will be reported. To add the user to the sudoers file, simply add the user to the sudo group:
# adduser john sudo
1.2 User Management with Cockpit
If the Cockpit web interface is installed and enabled on the system (a topic covered in the chapter entitled "An Overview of the Ubuntu Cockpit Web Interface"), a number of user management tasks can be performed within the Accounts screen shown in Figure 10-1 below:
The screen will display any existing user accounts on the system and provides a button to add additional accounts. To create a new account, click on the Create New Account button and enter the requested information in the resulting dialog (Figure 10-2). Note that the option is also available to create the account but to lock it until later:
To modify a user account, select it from the main screen and make any modifications to the account details:
This screen allows a variety of tasks to be performed including locking or unlocking the account, changing the password or forcing the user to configure a new password. If the Server Administrator option is selected, the user will be added to the sudo group and permitted to use sudo to perform administrative tasks. A button is also provided to delete the user from the system.
If the user will be accessing the system remotely using an SSH connection with key encryption, the user's public key may be added within this screen. SSH access and authentication will be covered later in "Configuring SSH Key-based Authentication on Ubuntu".
1.3 User Management using the Settings App
A third user account management option is available via the GNOME desktop settings app. This app is accessed by clicking on the down arrow located in the top right-hand corner of the GNOME desktop and selecting the Settings option as shown in Figure 10-4:
When the main settings screen appears, click on the Users option in the left-hand navigation Managing Ubuntu Users and Groups
panel. By default, the settings will be locked and it will not be possible to make any changes to the user accounts on the system. To unlock the settings app, click on the Unlock button shown in Figure 10-5 below and enter your password. Note that it will only be possible to unlock the settings if you are logged in as a user with sudo privileges:
Once the app has been unlocked, a green button labeled Add User… will appear in the title bar.
Click this button to display the dialog shown in Figure 10-6 below:
To assign sudo access to the new user, select the Administrator account, otherwise leave Standard selected. Enter the user's full name and username and either assign a password now, or allow the user to set up the password when they first log into their account. Once the information has been entered, click on the Add button to create the account.
The settings for an existing user can be viewed, modified or the account deleted at any time by selecting the corresponding icon within the Users screen as shown in Figure 10-7. The option is also available to view the user's login activity. Note that it will be necessary to unlock the Settings app again before any changes can be made to an account:
As a multi-user operating system, Ubuntu has been designed to support controlled access for multiple users. During installation, a single user account was created. Additional user accounts may be added to the system using a set of command-line tools, via the Cockpit web interface or using the GNOME settings app. In addition to user accounts, Linux also implements the concept of groups. New groups can be added and users assigned to those groups using command-line tools and each user must belong to at least one group. By default a standard, non-root user does not have permission to perform privileged tasks. Users that are members of the special sudo group, however, may perform privileged tasks by making use of the sudo command.
Author AnswertopiaPosted on May 28, 2020 May 28, 2020 Categories Ubuntu
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text:
<?php

namespace common\models;

use yii;
use common\models\RecordHelpers;

class MailCall
{

    public static function sendTheMail($message_id)
    {
        return Yii::$app->mailer->compose()
            ->setTo(\Yii::$app->user->identity->email)
            ->setFrom(['proskurin@npc.local' => 'ФГУП "НПЦАП"'])
            ->setSubject(RecordHelpers::getMessageSubject($message_id))
            ->setTextBody(RecordHelpers::getMessageBody($message_id))
            ->send();
    }

    public static function onMailableAction($action_name, $controller_name)
    {
        if ($message_id = RecordHelpers::findStatusMessage($action_name, $controller_name)){

            static::sendTheMail($message_id);

        }

    }

}

redpajama_set_name: RedPajamaGithub


We all have our values; basic needs to fulfil if we're to feel content in our lives. Here are mine. How do they fit with yours?
Reflections on 'personal brand', what I stand for and the least compelling sales pitch I've ever given.


text:
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Fashion Stylist and Personal Shopper
Make Up Artist Courses
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Private Consultations
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Make Up Lessons
Bridal Make up & Style Consultation
Corporate Identity Programmes
Men's Fashion And Image Consultation
Call Joan on
ABOUT JOAN CASHMAN
Joan has been styling the nation with over 20 years. She works with large and small corporate companies and also advises both men and women on a one-to-one basis.
The Academy was created to attract top, highly talented people who are interested in careers in "Fashion Styling", "Colour Analysis", "Makeup Artistry" and "Personal Shoppers" and she recently announced her collaboration with Bronwyn Conroy Beauty School in Dublin.
Cork's Favourite Image Consultant
Joan Cashman is the inspiration behind the company, Colour and Image Academy, a unique centre that specialises in all aspects of makeup and image consultancy training.
Joan has used her extensive experience teaching to to leading consultants throughout Europe, Ireland and the UK and this is what led her to set up this landmark Academy. Her training techniques and unique approach enable both men and women to excel in this very competitive business. Another part of the Academy provides comprehensive image consultancy services.
Joan's Experience
Joan's professional experience in Image Consultancy has spanned decades, she has had a long and distinguished career being head of Colour Me Beautiful in Ireland and her vast international experience as global trainer led to the launch of her award winning Colour and Image Academy.
Her high standards of work earned her the coveted 'Outstanding Contribution Award'and she proudly continues to hold her record as one of Europe's leading award winning Image Consultants.
Joan works extensively in the field of Cosmetic Camouflage and as a makeup artist with some of the leading names in the business. She works with TV, gives advice on radio chat shows, hosts "image for business" days with large corporations, and trains personnel from our well known department stores nationwide.
She's a regular contributor to beauty, fashion, and lifestyle magazines and is a favourite with Irish modelling agencies and VIPs to do styling and photocalls.
She won the inaugural Rabot Tweetie Top Twitter Awards for Fashion two years in a row – plus The Hi Awards "Stylist of the Year" Tia Maria Style Awards 2015, 2018, and was recently honoured with 'The Lifetime Achievement Award' for her contribution to the Beauty Industry May 2022.
Joan says, "It's my pleasure to help men and women feel confident in their style so that they can confidently pull their look together every day with ease"
Joan and her husband two grown up children and a busy and rewarding family life.
Gift vouchers available for all our services icluded:
Colour Analysis
Style Sessions
Personal Spopping
Handle @colourandimage
Twitter Linkedin Facebook Instagram
Copyright © 2020 The Joan Cashman Colour and Image Academy. All rights reserved.


Cadets in the Spa Gardens at last year's Remembrance Service.
Tens of thousands of poppies are being knitted by the Ripon community to line the route from Spa Gardens to the Cathedral as part of the city's Remembrance commemorations this year.
The 'avenue of poppies' will form a visual centrepiece for Remembrance this year, as Ripon joins communities across the country to mark the Centenary of the end of the First World War, and remember those who served and lost their lives in other conflicts.
Businesses along the route are being invited to decorate their shop windows with poppies to show their support, and already a band of knitters is forming to make the enormous display happen.
In a whole community effort, churches, schools and local groups are also being asked to help make Ripon a "destination for Remembrance."
Project coordinators Coun Stuart Martin and Sarah O'Connell are keen to hear from as many people as possible who are interested in getting involved - from Ripon to surrounding towns and villages, and even from across the country and the world. Last year thousands of knitted poppies in Thirsk attracted support from as far as Australia.
With an overwhelming reaction on social media, Ripon's avenue of poppies is already becoming widely known.
Coun Martin said: "I am under no illusion that it will be a lot of hard work, but if we can get the city behind us, we can do it. Everybody is affected by the two world wars and conflicts since then - whether you have a family member or know someone who has been involved or affected. I think that's why this idea of the poppies has hit a chord with people.
"We want to harness that energy and enthusiasm for the poppies to help make this a real community event. I can't thank people enough for their response to this."
The owner of the Sun Parlour cafe, Caroline Bentham along with her mum, will be covering the gates of the Spa Gardens with poppies, and if the necessary permission is given, Stuart and Sarah will also be looking to decorate the bandstand and other parts of the gardens.
After some criticism from residents about how Ripon looked for Remembrance last year, Coun Martin hopes this new project will make all the difference.
He said: "I saw a lot of posts on Facebook about last year's Remembrance, saying that Ripon didn't look anything like some of the other towns around. I was determined then to try and improve, and help make Ripon a destination for Remembrance, the place to come and remember the fallen."
There are plans to deck out the front of the Town Hall with poppies, and there are discussions about having some purple knitted poppies to remember all the animals killed in war.
The Dishforth Military Wives Choir has also expressed an interest in backing the display. The aim is to have the poppies up for one or two weeks before Remembrance, and for one week afterwards.
To find out more about how to get involved, contact Stuart Martin by emailing Bigstu.martin@virgin.net or calling 07811 390448.


Posted at 8:20 am on November 5, 2017 by Sam J.
Donna Brazile's upcoming tell-all seems to be going about as well as could be expected, for Republicans. What a shiznit show with the DNC and the Clintons if even 10% of what she reportedly wrote is true. And considering the number of people on the Left telling anyone who will listen that they shouldn't pay attention to Brazile's book, many are already lining up to order it (full transparency, this editor's Kindle will download it automatically on Tuesday).
Drudge even used an emoji, people, so you KNOW this is serious.
I am buying that book. One Democrat book I will buy.
We can't decide if Donna is really that willing to stab Hillary Clinton in the back OR if this is simply the most brilliant marketing campaign EVER for a book. Many on the Right are clamoring to buy it … capitalism for the win!
Is this for real? Did @donnabrazile really write that? Please tell me it's not.
And even if she wrote it, is it true?
Something indeed and whatever it is, it ain't good.


Customer satisfaction is undoubtedly vital to your company's success. After all, it's more than just a tool to increase bottom line; it can also improve your company morale and retention rate.
Your business needs money to survive. A lot of it, considering today's extremely competitive business market. And to generate profits, you need more than just one-time customers – you need happy and satisfied ones who want to repeat business with you, perhaps even spread the good news about your organization.
An IT support desk can make all the difference between a company that merely survives and one that thrives.
To help you understand the value of outsourced call center services to your business, here's a list that I made enumerating the top five ways it improves your company.
All successful businesses share a continuous goal that makes them profitable: To ensure the highest possible customer satisfaction at all times. Creating positive customer experiences while keeping true to your corporate culture is essential to reaching this goal.
With an IT support desk, you can turn each call into an opportunity for customer interaction. You can then use this as a chance to get insight which you can use to improve your products or service, and ensure the caller hangs up the phone satisfied in choosing your organization.
It's a given: Even the largest, most reputable, and well-established corporations have irate customers – those who call in mad, and sometimes even rude. You may get them yourself, and it's how you handle such calls that can either diffuse the client's anger or make them want to never contact you again.
A well-trained support desk has what it takes to turn irate customers' experience around, and it all boils down to quickly addressing and resolving their issues.
Constantly having to deal with angry customers can dampen anyone's mood. And this can happen a lot to your team when you don't have an expert customer support staff.
It's through the dedication of an IT support desk to boost customer satisfaction and make every call as pleasant as possible that can reduce the number of irate clients. With their skills designed to calm even the angriest of callers, your company will have less of these to worry about in the first place.
As a result, your entire workforce's morale and motivation goes up. Your people will then feel more compelled to perform better, which ultimately results in increased productivity.
You can get even more value from every recorded call your support team receives by using it as a form of training for your future or new employees. This is especially true for calls with difficult customers and tough situations that your reps have successfully resolved.
The more your organization learns from your customer support team, the more improvements you can make to your operational procedures. You can incorporate all the great practices taken from customer interactions into your company culture.
Ultimately, by making your customers happy, you can also make your entire workforce happy.
In the end, having an IT support desk actively listening to customers and striving hard to address issues promptly is key to great customer satisfaction rates. And high satisfaction rates are an essential factor to your company's success.
Want to know more about call center services? Feel free to check out our blog site – you'll find more useful info there.