Hello Readers! I'm Charles A. Shepherd from Defiance, Ohio. I'm passionate about food, A tad bit overweight, love country music, and I can grill a piece of meat in 64 different ways!
If you're wondering why my name seems so familiar, then, let me clarify that for ya! I do share my name with a renowned English photographer, whose works were popular during the second half of 19th century. I haven't checked if he's a forefather, but it's just one of the many pieces of trivia I've gathered about myself over the years.
I am slightly overweight, mostly because I eat up all the things I experiment with and make! If I have to pick a favourite kind of food, it'll have to be grilled food. In fact, like I said earlier, I've tweaked and experimented and have come up with 64 grilling techniques.
I've travelled miles across the country to sample and savour some of the best barbeque the country has on offer. And you wouldn't believe the places in which I've found perfect ribs, brisket and pulled pork at!
I'm hoping to own my own restaurant some day so I can share my passion for food and experimentation with the people of the world! Ain't nothin' like a great barbeque.
I find my inspiration while listening to music. Music and Food can be quite a heady combination, and this is definitely going to play a big part in the concept of my restaurant.
Before the internet, I was just a regular joe – contractor by day and foodie by night. My day job made sure that I had enough money to travel the length and breadth of this country to find the best barbeque money can buy. And trust me, I've had everything from the perfect pulled pork and smoked brisket, to stuff that is a travesty in the name of barbeque.
Do you want to know the biggest discovery of mine over the years? The best barbeque comes out of the American backyard. A grill, some beer, friends, and coal smoked meat on the 4th of July and lots of love in the food; it's a kind of perfect storm of ingredients!
Food is central to all my other hobbies – music, photography and travel. Everything I do comes back to one thing – food. IF you're here after reading one of my blog posts, you'll realise that I love not just eating but also photographing my food for posterity. Why? It just makes me happy to be able to document the things I love and share it with the world.
Anyway, I digress. Let's get down to business! Barbeque is an art form. It takes a lot of patience to find the right proportions of spices for your dry rub, the wood chips needed for the smoking, and the perfect grilling equipment you need. For a barbeque the grill is everything. IT doesn't have to be some fancy some or other like the one that Phil got his father-in-law in Modern Family, only to have it go all crazy on him and waste all that good meat!
Even a basic outdoor grill can get you the best results if you know what you are doing and want to learn. Which is where I come in! This blog has detailed images, reviews, and tips and hacks about BBQ that you would usually find scattered all over the internet. Trust me, it's not fun to have 20 tabs open when you are trying to figure out what to do with a brisket!
Barbeque is ultimately all about the recipe – a great dry rub will ensure that you get a perfectly smoked cut of meat that's seasoned to perfection and has you leaning back in your chair in utter happiness. You know you've have excellent barbeque when you've had to loosen your pants.
Buying a grill is only part of the process. A grill is just going to sit around making you feel like you've wasted your time and are using up valuable space in your home. There's so much more you need – like coals, lighting fluid, spatulas and forks to handle the bigger cuts of meat, a good cooler where you can keep your six pack so that you don't have to leave the grill alone while you go inside to get a refill! – you know important stuff like that.
This blog has some fun posts about barbeques and grills, some serious technical posts about barbeques and grills, some tried and tested recipes that you can use freely with guaranteed success every single time. We even have a handy segment on barbeques in the winter.
For the record, barbeque is no longer confined to certain parts of the country. You'll be surprised to know that even cities like New York have excellent barbeque. Surprising, isn't it? In fact, there are plenty of food trucks that serve some excellent barbeque, and they have their own smokers too! It's amazing how much people have embraced this slow cooking method and how popular it is.
All that being said, sometimes the best thing to do is to grill/barbeque at home. Although it is a tedious job there's nothing more satisfying that grilling a steak to perfection with a perfect sear and seasoning the way you like it without having to send it back. It's great to enjoy meals we love in the comfort of our home. Not only do they offer a great bonding experience, they also give you a better appreciation of what goes on your plate.
If you've read this far, I must say you are patient. This blog has a lot more to say about BBQ and Grilling that I can accommodate in this one page. So go ahead and take a look around – if you need ideas for accommodating a grill in your home (any size and shape), recipe ideas, all about cuts of meat, wood, smoking methods and tips – you've stopped at the right corner of the internet. I hope you enjoy this website and everything that we've put together! May the grill be with you!
Copyright © 2016 The Best Rated Outdoor Grills Reviews Online - KickAssBBQ. All Rights Reserved.


Why Choose Post Office Van Insurance?
We'd put money on the fact that everybody knows what the Post Office is and that's thanks to the number of branches it has in the UK.
In total there are more than 11,500 Post Offices in the UK so it's safe to say it's a well known UK brand.
What you might not know is that the Post Office also provides competitive van insurance quotes, something you will find out if you compare quotes with The Van Insurer.
And they're on top of a list of benefits as standard that includes a three year guarantee on repairs carried out by approved repairers, a UK-based contact centre and 30-day overseas cover for trips within the EU.
Compare van insurance quotes now with The Van Insurer and see if the Post Office can help you save money on your next policy.
It can pay to know somebody who has dealt with a company or product before, no matter what it is you're buying.
Like so much else, that's the case with van insurance too, which is why we collect van insurance user reviews from our customers.
At the top of the page, you can see user reviews for Post Office's van insurance. It gives an overall rating and a breakdown for different features of the policy, including Customer Service and Value for Money.
On top of that, you can also compare Post Office van insurance to other brokers on The Van Insurer panel.
Hopefully, all our user reviews and ratings mean you have enough information to make an informed decision about your next van insurance policy and provider.
I found that compairing on the van insurance site I found the best cover for the right price with The Post Office, nice friendly service and so easy on both! I found that compairing on the van insurance site I found the best cover for the right price with The Post Office, nice friendly service and so easy on both!
Very good price compared to other major insurance company's excellent service all round.. Very good price compared to other major insurance company's excellent service all round..
Completely cocked up the premiums, failed to implement the no claims discount even after a phone call and assurances that it would happen, then when I spoke to someone in... Completely cocked up the premiums, failed to implement the no claims discount even after a phone call and assurances that it would happen, then when I spoke to someone in the dept. weeks later they sorted it out very quickly and its been fine since!


text:
Community / Forums / Gamer Life / Off-Topic Discussions
Deep 6 FaWtL
223,501 to 223,550 of 238,768 << first < prev | 4466 | 4467 | 4468 | 4469 | 4470 | 4471 | 4472 | 4473 | 4474 | 4475 | 4476 | next > last >>
Tacticslion Apr 18, 2019, 07:19 am
Tacticslion wrote:
Turns out it's less than $2 at Staples to get one printed, so I'mma do that.
So, it's like a dollar and fourteen cents!
I ended up printing eight of them: three different types I wanted to try, then five of the one I specifically chose for my players!
It worked really well!
I like the "clean" one the best - the one that lacks any iconography except for country lines; it's the largest and fits more of the paper - but I chose the "full flag" one for the one I gave the players: I have four (out of five) major organized countries' flags below their country's name along the bottom, and those flags place within that country's borders on the map itself.
While the "full flag" version isn't quite flush with the bottom of the paper, and the flags are a bit incongruous with the map (because the flags are hand-drawn by me, which were put into a phone picture, then emailed so I could use MSPaint to paste them in), I chose it because it's the easiest to get a quick idea of what's where on the map.
The other option was a "half" option - the flags were along the bottom, but not on the map. But that was kiiiiiiiiind of the worst of the three, because the big (pleasantly clean) map was smaller (due to flags on the bottom) and it was hard to tell what's where (because colors that "pop" on the computer screen don't so much on the cheapest type of printed page).
Over-all they look really good! I'm very pleased!
(Though, unfortunately, one of my favorite flags has dulled colors, and, when shrunk to fit, just kind of looks dirty instead of awesome. Alas!)
EDIT: While I'm excited, people are coming over to my house - today! - so, you know, *dressed*
captain yesterday wrote:
Ha! I play with the General and the kids.
GM Yesterday: Okie dokie, you're adventuring through a cave system/crashed space ship (traveling through a flooded tunnel to get there) with an undead monster in it that's immune to all your magic, but you just got a bunch of money to spend, what do you buy?
The General: I get some masterwork stilts!
Crookshanks: Oh! I'm getting a mithral waffle maker!
Tiny T-Rex: One thousand fortune cookies!
Yeah, that checks out!
(Mine add various Mario and Zelda power-ups.)
LordSynos wrote:
** spoiler omitted **
Praying for you, my dude. Keep up informed!
Woran Apr 18, 2019, 07:25 am
Pathfinder Adventure, Adventure Path, Maps, Starfinder Adventure Path, Starfinder Maps Subscriber
Wow, good luck man!
NobodysHome Apr 18, 2019, 07:30 am
Sharoth, Lord Synos, and everyone else going through difficult times: Best of luck.
And who says you can't fight city hall:
Project Manager for Berkeley wrote:
Mr. Home,
I notified <the construction company> yesterday and directed them to revise the signs to provide the 72-hours advance notice.
Please feel free to contact me again today if this matter has been addressed.
Vanykrye Apr 18, 2019, 07:33 am
Good luck. That's quite the rollercoaster.
Just a Mort Apr 18, 2019, 07:36 am
Lord Synos, good luck with that. I hope it works out because at least your kid can see both of you more often. And I think she may need help handling a kid as well. Its hard being a single parent.
Sharoth:
Also ask on your end, while keeping the doctors informed - is there anything you can do to help the platelet count so she can go for the Op?
Yuugasa Apr 18, 2019, 08:45 am
13 people marked this as a favorite.
So I'm kinda stunned at the moment. I have a neighbor upstairs above me who I think is a pretty cool guy, but a lot of people say he is inconsiderate, as he plays music super loud in the middle of the night and watches porn with the volume so high up you can here it outside etc.
I'm half deaf anyway so I never cared, as I also play my tv loud to hear it and he has never complained.
Anyway I got a knock on the door this morning and it was him and he cheerfully handed me an envelope of money. A year or two ago he asked if he could borrow $200 and I gave it to him but he never paid it back. He thanked me for being nice to him and never calling the cops on him for the noise or even asking for him to pay me back. I was like it's all good thanks for getting around to it.
I looked in the envelop and it was $2,000!!!
He said it was interest for being nice to him.
Holy crap, apparently being nice to people really does pay off sometimes.
[Redacted tech in India] 10:04 AM:
[Vany] 10:04 AM:
can you help with a ticket
vdi is not working here
You sure about that ticket number? 149829 is a closed new hire ticket.
yeah she is having problem with the pwd
So does she need her password reset?
Reset to [Redacted Password]
can you send the email as well
vdi is not working in our site
Who is it it needs to go to?
[Redacted Manager]
So I send an email over to the Redacted Manager telling her that I reset the Windows password for Redacted User, because I was told she was having trouble logging in.
I get a reply that says "No her password is fine. I was trying to have [COMPLETELY DIFFERENT UNRELATED ISSUE LOOKED AT] but I reached out to someone else and they fixed it."
This is how the world ends.
Freehold DM Apr 18, 2019, 09:18 am
Yuugasa wrote:
strange...I don't remember giving away 2000....
Livin' the NobodysHome life.
Unless you're dealing with construction workers. Gonna lawyer up on these a$$hats soon.
They ignored the cease-and-desist order, tore up my street (starting with MY house, so they know who's causing trouble), my roofers used my driveway anyway, so they've blocked them from leaving with heavy equipment.
At this point if *I* were Albany's city manager I'd throw them out of the city until they learn to behave.
NobodysHome wrote:
Timestamped photos/video. You know that, but we all need a reminder.
Sharoth Apr 18, 2019, 10:05 am
Why the Weak Nuclear Force Ruins Everything - SciShow
And I win. The sewer guys have been forced to retreat.
captain yesterday Apr 18, 2019, 10:29 am
Pathfinder Adventure Path, Rulebook, Starfinder Adventure Path, Starfinder Roleplaying Game, Starfinder Society Subscriber
Be prepared for vandalism.
Sewer Guys: Next time, Nobodyshome...NEXT TIME!!!
slithers into grate
COUNTERATTACK with...Oh man. I have no idea what you of all people would counterattack with.
Honestly, I think they're too big of a company that the workers are going to care. They're union, so they'll get paid whether or not they're working (and they just moved their work out of the Albany borders 'til Monday), and I don't see their executives coming by to trash my house.
We'll see. I'm optimistic that they're too big for that kind of crap.
Freehold DM wrote:
Sugar in the gas tanks. Painful skin irritants on the steering wheels. Flat tires. Police reports that they're harassing the young girls who live on the street...
I was quite the vandal for many years. I could make their work a living hell.
Let's not play the escalation game...
Sugar in the gas tanks.
Don't we have locking gas tanks now to keep that from happening?
Painful skin irritants on the steering wheels.
Breaking into a vehicle just to put schmutz on the steering wheel? I dunno if that's worth the breaking and entering charge over...
Flat tires.
Police reports that they're harassing the young girls who live on the street...
Given the way police are nowadays, that might an unnecessary escalation. Still, keep it in your back pocket.
I dunno...Your moves seem outdated, or they could be.
Amusingly enough, they stopped because it was so "old school". I have yet to see a car manufactured after 2015 that doesn't have "free and clear, open without a hassle" gas caps.
Er, it's construction equipment. There's no "breaking" at all. It would be trespassing, because I'd have to climb onto the vehicle to commit criminal mischief.
Paladin of Erastil. I will obey the law and leave you alone as long as you obey the law and leave me alone.
The moment you threaten my family, I go nuclear and it ends. Badly for you.
NobodysHome Apr 18, 2019, 12:38 pm
Update: I've received apologies from both the cities of Albany and Berkeley, reassurances from my roofers that they can work within the schedule given, and I walked through all the workers, smiled, said, "Hi!" and they're using my hose for water and such.
So I pretty sure it's all good.
Tomorrow is another day...
Vanykrye Apr 18, 2019, 12:45 pm
They are getting better at locking out construction equipment that's left on sites specifically due to vandalism and theft.
The gas caps aren't locked anymore, but the hatches on the body panels frequently are. Between Aiymi, Zelda, and I, our last 5 cars have a panel that can only be opened from inside the cabin. I'm sure that can be bypassed, but perhaps not without physical damage.
The trick to being a good prankster/vandal without getting into much trouble is, yes, do something minimal like irritants on touch surfaces. Something that doesn't cause permanent harm/injury and also doesn't do permanent damage to the equipment. Doing something like that will maybe get police involvement these days (wouldn't used to), but doing more than that will almost certainly get involvement when they can claim physical damage to a $350,000 piece of equipment.
John Napier 698 Apr 18, 2019, 12:52 pm
Woran Apr 18, 2019, 12:53 pm
Yep. Winter can come back now. Musquitos are out. I'm done with the nice weather. Back to freezing please.
Well, like Yuugasa said, being nice to people goes a LOOOONG way towards heading off all such idiocy.
Go out and scream at the workers that they're messing up your schedule, then complain to the city and get them thrown out? Yeah, you're in for a world of irritation.
Get the city to toss them, then swing by and make sure they know they're welcome once the schedule hits, and until then they can use your water and power for whatever they need and they tend to say, "OK, he's not a total jerk."
At least that's my hope.
If you're asking Cap? Concrete saw.
Limeylongears Apr 18, 2019, 01:23 pm
Vanykrye wrote:
Angry bear on a skid loader.
Limeylongears wrote:
Shaved angry bear on a skid loader.
A fortress made of pillows.
Captain Yesterday's Phallacy Apr 18, 2019, 02:20 pm
Wet Willy?
captain yesterday Apr 18, 2019, 02:25 pm
I only said that because I had a friend that complained about a sidewalk crew and they "accidentally" did substantial damage to his fence then made him sue them to get the money to repair it.
This was a municipality crew.
Shaved angry bear on a hairy skid loader.
This guy gets it!
You guys are all weird.
Well, this is a visiting crew. Albany granted them an encroachment permit to come into Albany and do work that Albany did not want them doing.
So they were already on thin ice.
Then they jumped the gun TWICE and started tearing up the streets without Albany's permission.
Albany's Public Works guy PERSONALLY came out to the street to throw their butts back into Berkeley.
If they start "accidentally" damaging my house, they're going to lose their permit to work here at all.
I double dog dare them...
With Improvised Weapon and Throw Anything as my first feats, the only limit is my imagination.
Captain Yesterday fun fact: I once gave someone a concussion by throwing my yoyo at his head, he was chasing me with a knife saying he was going to scalp me so I regret nothing).
Captain Yesterday, Brut Squad Apr 18, 2019, 03:47 pm
I spent the day fixing things other people (or some industrious rodents) messed up, so it was a fun day.
Freehold DM Apr 18, 2019, 04:03 pm
took the words right out of my mouth.
Co-worker now believes that the truest test of my skills is to give an angry bear a flat top.
I will stand in to judge the flat top as I had one for several years.
I've seen enough late night rap videos and episodes of In Living Color and The Fresh Prince I'm confident my work will stand up to the harshest of criticism.
I wonder if they make perm attachments for mini bobcats.
this ain't 1984 man, nobody's doing jerri curls.
So, you're a "big box" studio, you're handed one of the biggest anime hits of 2015, and you're asked to do Season 2. People are horrified that you're going to botch it.
(1) Put your best people on it, do your best to match the style and flavor that made Season 1 such a hit, accept that some people are going to hate you just because you're different, but try to wow the rest, or...
(2) ...just mail it in with a cheap, by-the-numbers job, since enough people are going to hate you anyway that there's really no point in trying.
JC Staff has obviously taken option (2) with One Punch Man Season 2, and it's depressing. One reviewer just put up a screenshot from Season 1 and a screenshot from Season 2, and said, "Compare the colors." Season 2 is grey and lifeless compared to Season 1.
Having read the story arc of Season 2 in the manga, I like it much better than the story arc of Season 1, because it addresses the elephant in the room: One man, no matter how godlike, cannot be everywhere at once, nor can he protect everyone
Yet so far, there is no life in the anime. Episode 2 was one of my favorite books, as Blizzard's emotional progression through the series is really something that resonates with me. The anime makes her a boring, one-dimensional, throwaway character. I don't care whether I ever see her again. Some of the brilliant translations in the manga ("You're gonna attack with rocks and pebbles? Oh please, no, don't.") get butchered in the anime ("You're attacking with stones? Hmm."). One of the most powerful scenes in the manga is in this book, and is just utterly failed in the anime.
In short, Book 9 of the manga includes a lot of lighthearted stuff that lets you see a bit of Saitama's humanity. Episode 2 of the anime tries to focus on the action, which is in the background in the manga.
Another episode of Season 2 down, another serious disappointment.
EDIT: And for those of you who are getting the original manga instead of the compiled versions I'm getting, S2:E1 corresponds to punches 38-40, and S2:E1 corresponds to punches 41-45. So it looks like the anime's trying to do 3-4 punches per episode.
Cap'n Zoolander, FaWtLy Fashion Apr 18, 2019, 06:05 pm
Fashion is cyclical.
Community / Forums / Gamer Life / Off-Topic Discussions / Deep 6 FaWtL All Messageboards

redpajama_set_name: RedPajamaCommonCrawl


text:
What's Hot? 06/17/16
Jim Liebelt | Senior Writer, Editor and Researcher for the HomeWord Center for Youth and Family at Azusa Pacific University | Friday, June 17, 2016
Trending Today on Twitter - 6/17/16
1. #NationalFlipFlopDay
2. #FridayReads
3. #PutYourDadInABand
4. #Charleston9
5. #DemiOnGMA
6. Jerry Lawler
8. George Lapides
9. Soarin
10. Former Auschwitz
Trending Stories Today on Google - 6/17/16
1. Zlatan Ibrahimovic
3. Microsoft Corporation, Cannabis
4. Demi Lovato
5. Amy Schumer, Anna Wintour
6. Warriors - Cavaliers
7. Matt Bevin, University of Louisville
8. ISIL
9. Elizabeth Arden, Revlon
10. Roger Federer, Halle Open
iTunes Top 10 Singles - 6/17/16
1. CAN'T STOP THE FEELING! - Justin Timberlake
2. One Dance (feat. Whizkid & Kyla) - Drake
3. This Is What You Came For (feat. Rihanna) - Calvin Harris
4. Don't Let Me Down (feat. Daya) - The Chainsmokers
5. H.O.L.Y. - Florida Georgia Line
6. Just Like Fire (From "Alice Through the Looking Glass") - Pink
7. Ain't No Stopping Us Now - Kane Brown
8. Mee Too - Meghan Trainor
9. Lost Boy - Ruth B.
10. Ride - twenty one pilots
Source: iTunes
Top 10 TV Shows in Prime Time - Week Ending 6/12/16
1. NBA Finals GM 4
3. America's Got Talent
4. Tony Awards
5. 60 Minutes
6. The Bachelorette - Mon
8. The Bachelorette - Tues
9. The Big Bang Theory
10. NCIS: New Orleans
Source: Nielsen Co.
Trending on YouTube - Teens 13-17 - Today - 6/17/16
1. Welcome to the Block - Orange is the New Black - Netflix
2. 17 Mistakes of Zootopia You Didn't Notice
3. Red Hot Chili Peppers Carpool Karaoke
4. Why I Don't Wear a Bra
5. Ski Slope Scare
Top 5 Movies - Last Weekend
2. Warcraft
3. Now You See Me 2
4. Teenage Mutant Ninja Turtles: Out of the Shadows
5. X-Men: Apocalypse
Source: Rotten Tomatoes


In an extremely odd move, computer maker Lenovo will not install or support Linux on any of its PCs, including Thinkpads and a series of new notebooks. Why is this odd? Well, the Thinkpad had an extremely large Linux following – one that IBM worked fairly hard to build. Go to any Linux convention and you'll see rows after row of Thinkpads and Apples. While it's no surprise they won't be installing Linux, officially calling it not supported is a big step. One has to wonder what IBM will use internally if Lenovo starts to move toward laptops that simply won't run Linux. That part alone could get interesting. But that's not the odd part though. Just a day before this announcement from Lenovo, the government-run Central Trust of China mandated for the first time that all desktop computers purchased from now on must be Linux-compatible. Those two items didn't seem to jive, so I started doing some more research. It seems Lenovo may now be back tracking a little, according to this CNet article. One has to wonder what happened here. Was there something more to the recent 1.2B dollar deal between Microsoft and Lenovo? Would the company really leave Linux completely unsupported despite a Chinese mandate that all government desktops be compatible? Hard to tell at this point, but I didn't like the deal when it happened and I am liking it less and less now.


And our innocent lane turns into a journey to fantasy land.
While the early morning light effects make for a spooky forest.
This isn't a sinister crop of some futuristic superfood but just the ubiquitous weed, goldenrod, getting ready to bloom.
And trees morph into monsters.
But wait, what's happened to my wooden friend? He's twisted his head round to look at something.
Whatever it was, I was too late to see it.


text:
Alternative Medicine / Health
Youth & Mental Health
Divrei Torah
Eblast sign up
The Jewish Link
Home Featured Featured Articles Asthma-causing blood cells could help fight colon cancer
Asthma-causing blood cells could help fight colon cancer
3D of an eosinophil white blood cell among red blood cells. Illustration by Kateryna Kon via Shutterstock.com
New Israeli study finds white blood cells related to allergies and asthma may also be harnessed to destroy cancer cells.
By ISRAEL21c Staff FEBRUARY 27, 2019, 9:00 AM
A surprising new study from Israel finds that malignant colorectal cancer cells can be eliminated with eosinophils — white blood cells that originate in bone marrow and may once have killed off intestinal parasites, but which today are responsible for chronic asthma and allergies.
The research, published in Cancer Immunology Research on January 21, was led by Prof. Ariel Munitz of the Tel Aviv University Sackler School of Medicine department of microbiology and clinical immunology and conducted by his doctoral student Hadar Reichman, in collaboration with colleagues in Tel Aviv Medical Center's gastroenterology department.
"Eosinophils are white blood cells that secrete powerfully destructive proteins," Munitz said. "They may have played an evolutionary role in combating parasites. But now that most people, particularly in the West, enjoy good hygiene and few parasites, the eosinophils have become destructive agents, causing allergies and asthma.
"Our new research theorized that since eosinophils are capable of killing parasites and can cause damage in the lungs of asthma patients, they might play a role in cancer treatment and would be able to kill tumor cells."
The largest eosinophil reservoir is situated in the digestive system, so the researchers initially decided to test their theories on colorectal cancer. In the first stage of research, they selected samples from tumors of 275 patients to determine the number of eosinophils in a tumor as compared with the stage and severity of the disease.
"We found that the higher the number of eosinophils in the tumor, the less severe the disease, which represents a clear correlation," said Munitz. "We identified that the cancerous environment attracts these cells, which infiltrate the tumors and flourish there for a long time."
Potent anti-tumor activities
The researchers subsequently tested their hypotheses in various mouse models of colorectal cancer. They discovered that eosinophils displayed potent anti-tumor activities and could directly kill tumor cells.
"We also found that when eosinophils were activated by a protein called IFN-gamma, they induced an even greater tumor-killing response," Munitz explained.
"Following various extensive analyses, we concluded that eosinophils have unique and distinct activities in comparison with other cells present in the tumor. For example, eosinophils can kill tumors independently of known tumor-fighting cytotoxic T cells."
He believes eosinophils could be used in treating cancer if their robust anti-tumor response somehow could be unleashed pharmaceutically, or by combining treatments to harness the potent forces of both eosinophils and cytotoxic T cells.
"We have discovered a new target for immunotherapy for cancer patients — the eosinophils," concluded Munitz. "We hope that our research will serve as a foundation for drug development in a number of different approaches."
The study was supported by the Israel Cancer Research Foundation, the Israel Cancer Association and the Israel Science Foundation.
Israel Study
malignant colorectal cancer cells
Prof. Ariel Munitz of the Tel Aviv University Sackler School of Medicine
Previous articleKia, Hyundai expand U.S. engine fire recalls by 534,000 vehicles
Next articleIsraeli Research Finds that Home Births are 3 Times More Dangerous than at Hospitals
Israel, UAE give wing to preservation of rare desert bird
Isaac & Rubenstein – Blue Skies – Live Lounge Sessions
Contemptible: Left-wing Jewish groups rejoice over Adelson's death
Israel, UAE give wing to preservation of rare desert bird January 19, 2021
Canada urges Biden not to cancel oil pipeline on first day January 19, 2021
Israeli Company Says Instant Covid-19 Breath Test As Precise As Swab Test January 19, 2021
Parler returns despite Internet giants' ban January 19, 2021
45% of UK adults hold anti-Semitic views, poll finds January 19, 2021
The Jewish Link - Your link to the latest news of interest from around the world
Contact us: info@thejewishlink.com
© Axis Bridge Marketing


2018: Played in 17 games with 12 starts as either the third baseman or designated hitter … Totaled 11 hits with one double … Drove in five runs and scored four runs … Had three multi-hit games … Went 2-for-3 a walk and a pair of RBI against Wofford on May 2.
2017: Played in 41 games, making 32 starts between 2B, 1B, 3B and DH ... Batted .250 (29-for-116) with three doubles, five home runs, 15 runs and 19 RBIs ... Finished third on the team in homers ... Seven multi-hit games and four multi-RBI games, including team-high two four-RBI games ... Played in and started 17 Big South games, batting .296 with three home runs, eight runs and nine RBIs ... Slugged .481 against league opponents ... Went 3-for-4 with four RBIs against USC Upstate on May 3 ... Was 3-for-4 with two runs, a double, a homer and four RBIs at Charleston Southern on May 12 ... Drove in three runs at USC Upstate on Mar. 22 ... Was 3-for-4 with an RBI at Gardner-Webb on Apr. 29 ... Recorded a career-long seven-game hitting streak from Apr. 29 to May 14, going 13-for-28 (.464) with five runs, three home runs and 11 RBIs ... Drew three walks and scored twice without registering an official at-bat against Army on Mar. 19.
2016: Played in 11 games, making one start…was 3-for-12 (.250) with a double ... Back-to-back games played with pinch-hit singles against Savannah State and Western Carolina ... Went 1-for-2 against VCU on May 15.
Before PC: First team All-Conference player at Plant City High School in Plant City, Florida ... Also earned All-District honors…named team MVP as a junior.
Personal: Son of Kevin and Donna Slagill.


STAMFORD, CT -- Harman International Industries, Incorporated (NYSE:HAR) declared a cash dividend on May 2, 2013 of $0.15 cents per share for the quarter ended March 31, 2013. The quarterly dividend will be paid on May 28, 2013, to each stockholder of record as of the close of business on May 13, 2013.
The quarterly dividend will be paid on May 28, 2013, to each stockholder of record as of the close of business on May 13, 2013.


text:
\section{Introduction}
\label{sec:intro}

Web 2.0 social websites are playing an increasingly important role on the Internet by utilizing the wisdom of crowds. One such example is the Community Question and Answer (CQA) portals on which users can post and answer questions, such as Yahoo! Answers, Naver and Baidu Zhidao~\cite{yahooanswers,naver,baiduzhidao}. Some CQA websites like Quora~\cite{quora} attract users by offering professional answers, most of which come from verified people in reality. These websites gain popularity and trust by providing a sense of interaction between the questioner and the masses. With millions of archived Q\&A sessions, CQA forums have become a major source of advice for many Internet users.

As a large knowledge base of crowds, the archived Q\&A sessions have been used for automatic question answering and recommendation. Nevertheless, the quality of user-generated content in the Q\&A sessions varies drastically. For instance, some answers do not match the questions and even contain spam and rude words. In recent years, tremendous efforts have been made to locate better answers and remove spam from the archived questions and answers resource. Techniques such as analysis of text, user-question-answer's link relationship, and user feedback features have been used in tools like PageRank to identify high-quality web pages~\cite{Jeon:2006:FPQ:1148170.1148212,Jurczyk:2007:DAQ:1321440.1321575,Agichtein:2008:FHC:1341531.1341557}.

Existing techniques, however, may not work well in the presence of the so-called Internet water army, a large crowd of hidden posters who get paid to generate artificial content in the social media for commercial profits. Paid posters have become popular with the booming of crowd-sourcing marketing. As confirmed in~\cite{Wang:2012:WWW}, crowd-sourcing systems such as Amazon's Mechanical Turk, Zhu Ba Jie (a similar Chinese crowd-sourcing site), have been broadly used for commercial campaigns. Due to their popularity, the CQA forums have become the targets of those campaigns that create untruthful Q\&A sessions for commercial purpose. Consider the following example:

\textit{Question: I tried several methods to lose weight but all failed. What should I do? Please give me some advice!}

\textit{Best answer: Don't worry, I have experienced the same pain as you. Firstly, you have to keep a healthy diet. Be careful about the nutrition in your food and never eat  fast food. Secondly, don't sit too long in front of a computer. Finally, perform physical exercise everyday. What's more, you can also try a product named X. This product cotains ingredients such as ... and can help you lose weight without any risks.}

The above Q\&A session was actually generated by paid posters. The answer provides very practical advice at first and then gives suggestion on the product which needs to be promoted. The practical advice part is to earn the trust of the users. We have observed that fake answers generated by paid posters are often long enough and quite relevant to the questions, and some paid posters involved in the fake Q\&A sessions are ranked high according to the website's reputation system.

Based on textual similarities, previous work~\cite{Liu:2008:USA:1599081.1599144,Bian:2008:citeulike:2803519,Bian:2009:citeulike:4375490} is likely to treat the above answer as of high quality due to the high relevance of textual features between the answer and question content. As a result, the output may contain commercial spam, resulting in a credibility problem. Therefore, additional strategies, such as \emph{writing templates}, \emph{public calls for commercial campaigns}, and \emph{a poster's track reputation}, should be integrated for the effective detection of paid posters. Furthermore, most existing work relies on offline analysis, while end users demand for instant help and should be warned of potential commercial campaigns when they browse a CQA forum. The call for a real-time response system that can detect potentially fake Q\&A sessions on the fly is strong.

We tackle the above two challenges in this paper by designing an adaptive online detection system tailored specifically for CQA forums. Our contributions are as follows:
\begin{itemize}
	\item We discover that the behavioral features of paid posters are different in CQA forums when compared to other types of forums such as microblog (also called Weibo, a Twitter like service in China) and news reports. We identify the special features of paid posters in CQA forums that are useful in the detection.
	\item Based on the identified special features, we design a detection method which uses machine-learning techniques and assigns credibility scores to each of the best answers by using semantic analysis and user features, such as users' history data.
	\item We implement an adaptive, online detection system which automatically analyzes the hidden patterns of commercial spams and raises alarms instantaneously to end users whenever a potential commercial campaign is detected. Our system is adaptive and accommodates new evidence gathered by the detection algorithms over time.
\end{itemize}

\nop{NOTE:
Campaign-like best answers lead to credibility issue.
}

\section{Data Collection and Labeling}
\label{sec:dc}

\nop{
\subsection{How Do Online Paid Posters Work in CQA Portals}
To understand the background, we start with a brief introduction on how online paid posters work in CQA sites.

With the advent of popular crowd-sourcing websites, companies tend to hire paid posters to help them hype their products in the social media. Research~\cite{Wang:2012:WWW} has shown that paid posters are capable of generating large information cascades that could escape security check and accelerate spam dissemination on social media, like microblogging services and community-based question and answer websites.

Our research is based on Baidu Zhidao, a Chinese CQA website that is similar to Yahoo! Answers. During our study on the CQA-oriented promoting campaigns on crowd-sourcing websites, we discovered specification with detailed requirements and templates for the paid posters. The requirements provide not only basic description regarding the product but also types of paid posters needed. For example, some companies request that posters should have a good reputation. Note that many CQA websites have a reputation system and assign high-level reputation indicators to ``trustworthy'' users whose answers are mostly selected as the best answers. Those reputation systems track the history of users but are not designed to analyse and detect online paid posters.

It is very interesting to notice that companies that hire paid posters also provide several templates for questions and answers. For instance, in a medicine promotion case, the question describes a certain symptom, and the answer explains reasons for the symptom and recommends taking the specific medicine. Both question and answer templates are carefully crafted to sound real. The answers usually include personal experience with the products. In addition, the instructions will advise paid posters to insert their own sentences in the templates rather than just copying and pasting the templates.

Using these templates, paid posters can create complete Q\&A sessions. They first pose a question, and use a different user ID to post the answer. This could be achieved by one user registering for multiple IDs or by several colluding posters. They then select the answer as the best answer, after waiting for other users to post answers. This waiting time is to cheat the readers into believing that the best answer is chosen from many answers. In the CQA portals, once the best answer is decided, the Q\&A session is considered \textit{closed} and no new answers can be added to the session.

\nop{Comments: It is hard to differentiate the two cases: create own Q&A session or post to other's questions. We have no way to check the physical person behind a user ID}

\nop{
If the paid posters reply to related questions instead of creating sessions by themselves, they will get paid only when their answers are selected as the best ones. In both cases, the answerers may provide much useful information \textit{before} promoting the products. For example, they may give some useful advices for an health-related question before recommending a product. }

\nop{
From our dataset, we find paid posters sometimes promotes the products in a fake view of experience of their relatives. And since templates look like normal pattern of answers, it's difficult to tell the difference if you read only one session. In this case, you have to look up other sessions associated with the same user ID, if applicable.
}

\nop{
In fact, Baidu Zhidao has a function for user to report malicious sessions. However, it still takes time to wait until the support team confirms and deletes them.
}
}

\subsection{Data Collection}

\nop{
Before stepping into analysis of spam features, we firstly introduce how we collect and label the Q\&A sessions from the CQA site, Baidu Zhidao.
}

Since the readers tend to pay more attention to the best answers and also due to the manner in which online paid posters are supposed to work, we only collected the best answers and ignored other ones. This is to avoid collecting a large amount of irrelevant information for this study.

In order to collect campaign Q\&A sessions, we first visited the crowd-sourcing websites, where the paid posters apply for campaign tasks and get paid, as stated in Section~\ref{sec:intro}. From the campaigns calling for paid posters, we selected $11$ closed requests because the paid posters who worked for the $11$ products had finished the tasks. We extracted keywords for the $11$ products and searched for Q\&A sessions with them on Baidu Zhidao. We used a crawler to visit and download the web pages associated with searching result. These sessions included not only the campaign sessions, but also normal sessions containing the keywords. After parsing all the collected web pages, we obtained a group of target users, including both paid posters and normal users, as well as the links to the users' homepages hosted by Baidu Zhidao.

By following the users' homepages, we could find useful information for our research. For example, a user's homepage provides the Q\&A sessions where this user posted his/her answers (the question answering records). The question-answer history provides a good knowledge on the multiple campaigns that a potential paid poster might have been involved. Having obtained the initial dataset of IDs and links, we then visited each user's homepage, retrieved every Q\&A session that the user participated in. We only collected the closed Q\&A sessions (i.e., the best answer determined). A closed Q\&A session implies that users can no longer post new answers to the question, but they can click the ``Like" button to support the posted answers, including the best answer and other answers. From those Q\&A sessions, we finally extracted information used in our analysis. The recorded information from those web pages includes \textit{questioner ID}, \textit{answer ID}, \textit{time}, \textit{title}, \textit{question content}, \textit{answer content}, \textit{user feedbacks (visited times, ratings)}.

\nop{Since a paid poster could participate in multiple campaigns, we could find all the records by using the question-answering history.}

\nop{This could be fixed if we have a quite large dataset and can take advantage of information from other users' answering records. A complete list of such history records of each user is significant when applying user-interaction features analysis.}

\nop{
The site also provides other user-specific properties, like answer accepted rate and answer recommended rate. As what will be presented in the Section~\ref{sec:sfo}, we don't use these user-specific features and we leave the problem how to integrate them into our framework as future work.
}

From the Q\&A website, \textit{Baidu Zhidao}, we crawled, $6462$ users' question-answer history records accumulated during a three-month period from October to December in 2011. For each user, we built a list of history information, showing the question, answer, participated user IDs, and other features. Associated with the $6462$ user IDs, we have $75,200$ Q\&A sessions in total, all having the best answer.

\subsection{Manual Data Labeling}

To get a sample dataset for feature analysis, campaign sessions should be differentiated from the normal ones. By reading the best answers, we manually labeled the Q\&A sessions in the dataset. The labeling process mainly depends on the Q\&A templates from the crowd-sourcing websites such as Zhubajie~\cite{zhubajie} and Tiancaicheng~\cite{tiancaicheng}. We summarize the applied techniques below:

\nop{A concise definition of spam should be given before we present our method.}

\nop{
Most of the research work in Section~\ref{sec:rw} assumed that the most relevant answers should be the best ones and deserved higher ranking scores. However, according to our survey, as well as \cite{Wang:2012:WWW}, new types of campaign Q\&A sessions could appear in the CQA websites. Posting questions and answers, the users who are paid by companies hype their products to improve the popularity. In order to attract normal readers, paid posters are often ordered to write rather relevant contents which look very professional. The criteria for judging campaign is always subtle. Our work will be done on a limited scope, that is detecting the advertisement spam, especially the fake experience spam. Campaign sessions usually fool people by fake experience.
}

\begin{enumerate}
\item Since we have collected a list of $11$ products which were hyped in the Baidu Zhidao, we could compare the Q\&A content with the campaign templates. If the product's name is in the $11$ initial samples and the contents match the templates, such as the descriptive words and the organized pattern of sentences, we labeled it as a campaign Q\&A session. We stress that there is difference between our work and related research which needs to judge the quality of answers. The evaluation of quality of answers is usually based on question-answer relevance, length of the texts, grammar correctness, politeness, and so on. To obtain a reliable dataset, researchers often rely on multiple assessors and are faced with the difficulty of reaching an agreement among the multiple evaluation results. Our labeling method differs from the above and largely avoids the annotation difficulty, because we know exactly the name of the hyped product and how paid posters would write the Q\&A sessions.

\item When we encountered new products not in the list of  $11$ initial samples, we recorded the product's name and searched it in the crowd-sourcing websites. If we found the template of this product, we use the above method to compare their contents.

\item If a new product is listed in the campaign websites but the template is not available, we followed some special features normally found in Email spam to make a decision. For example, a spam may use different fonts to write the telephone numbers and insert special characters between the product's name. This type of operations is usually used to escape detection by the filter system. We labeled the session as campaign if the product's name is in a campaign list and the best answer has special features similar to Email spam.

\nop{\item We also notice that some paid posters ask and answer the questions by themselves. If the same user ID asks and answers the same question, we label it as campaign.}

\item If we could not find the new product in the campaign websites, we then tried to identify potential templates used in the same category of products and special features obvious in an Email spam. If none of those could be identified, we labeled the session as a normal session.

\nop{
\item If there is a new product for which we don't have the template, then we will use templates of the same category. For example, there are two medicines, A and B but we only have the Q\&A  template for A. Since both of them are medicines, we assume the Q\&A sessions of B, if they were  campaign sessions, would have the similar structure and pattern as the templates of A. If this is the case, we label the sessions of B as campaign.
}
\end{enumerate}

Up to now, we have labeled $4998$ samples in our dataset. Among these, $2147$ samples are campaign Q\&A sessions and the other $2851$ samples are normal ones. The sample size is large enough for our current study. Since we selected 11 campaigns, which were posted on the crowdsourcing websites, as the seeds of our crawler, the proportion of campaign sessions is relatively high in the dataset.

When we manually labeled our datasets, we carefully read the contents of a user's post. The meaning can be understood by human but is hard to use in machine learning based classification. Even with the above template based labeling method, it is not easy to write an algorithm to automatically identify a campaign session because a poster may re-phrase the template in their own words. Due to these reasons, we need to search for statistical features that can be effectively used towards building a detection system.

\nop{We don't use automatic method to check the product's keywords because we noticed that some sessions who have the keywords are not campaigns. We didn't find efficient methods that could exactly detect the difference. For example, the question asker consults about a product which is recommended by many paid posters on the site. The answerer, however, may already notice the campaign Q\&A sessions and he/she would tell the asker not to believe in those sessions.

What's more, we emphasize that our research is to evaluate the credibility of the best answers. In our work, we assume that a campaign Q\&A session has the credibility problem. On the contrary, an irrelevant answer will be assigned a high credibility scores (or a low campaign score) if the content is not commercial campaign. Consider the following examples:

\textit{Question: How to become a computer expert ?}

\textit{Best answer: Simple, just close your eyes and start dreaming.}

Although the best answer seems like joking, the credibility score will be high if it is not a commercial campaign.
}

\section{Analysis of Statistical Features}
\label{sec:sfo}
\nop{
To identify effective features which contribute to the credibility of Q\&A sessions, we discuss how we choose candidate features in this section.
}
\subsection{Insufficiency of Existing Statistical Features}

We firstly demonstrate the difficulty of the problem we study by analyzing existing features, some of which have been used in related research such as evaluation of high quality answers or detection of Internet water army in news report websites~\cite{Chen:2011} and showing their limitations.

\subsubsection{Interval Post Time}

In~\cite{Mukherjee:2012:SFR:2187836.2187863}, Arjun~{\em et al.} defined several spamming indicators for modelling the behaviour of fake review writers. They found that spammers of a spam group tend to post reviews during a short time interval. This feature has been shown to be a good indicator to detect Internet water army in news report websites~\cite{Chen:2011}.

In our work, we consider two time stamps for a Q\&A session: One is the time when the questioner post the question topic (the ask time), and the other one is the time when the best answer is posted by a replier (the best answer posted time). We define \textit{interval post time} as the latter time stamp minus the former one.

In Figure~\ref{fig:it}, we show the approximated probability distribution of interval post time with dot-dashed lines for campaign sessions and solid lines for non-campaign sessions. The x-axis is drawn by $log$ scale.

\begin{figure}[!ht]
\centering
\includegraphics[width=3in]{it.pdf}
\caption{The PDF and CDF of the interval post time}
\label{fig:it}
\end{figure}

From the figure, we find it difficult to tell the difference between campaign and non-campaign Q\&A sessions. Two reasons may contribute to the above phenomenon. There are many normal users who spend much time on the Q\&A website and try to post answers to \textit{open} questions, especially those questions associated with some \textit{rewards points}. These people are known as \textit{bounty hunters}. Most bounty hunters post very good answers because they want to get more rewards points. On the other hand, online paid posters, before they post and choose the best answer, normally wait for some random time for other answers appearing in the session. This is to give readers a fake impression that the best answer is selected among many answers. While paid posters try to finish a job as quickly as possible in news review websites~\cite{Chen:2011}, the same behaviour does not exist here.

\nop{
The site also provides other user-specific properties, like answer accepted rate and answer recommended rate. As what will be presented in the Section~\ref{sec:sfo}, we don't use these user-specific features.
}

\subsubsection{Number of Other Answers}
Before the question is closed, users can post their own answers. This variable counts the number of answers other than the best one. Intuitively, if the paid posters create the sessions themselves, they may not have patience to wait for more replies. They could close the sessions and get paid as soon as possible. To test this conjecture, we show the probability distribution of this feature for campaign sessions and normal sessions in Figure~\ref{fig:oa} .

\begin{figure}[!ht]
\centering
\includegraphics[width=3in]{oa.pdf}
\caption{The PMF and CDF of the number of other answers}
\label{fig:oa}
\end{figure}

Similar to the interval post time, the number of other answers does not indicate much difference for the two types of Q\&A sessions. This invalidates the above conjecture and we do not consider it a good feature for the detection of paid posters in CQA portals.

\subsubsection{Number of Likes}
Similar to the ``Like" button in Facebook, if other readers find the best answer to be helpful, they may click the ``like'' button. The number on the button indicates the total number of clicks. Intuitively, this feature represents user's feedback and should be helpful in identifying trustful answers. The more ``likes" an answer receives, the more likely it is a good answer. However, as shown in Figure~\ref{fig:likes}, this is not a reliable feature. This is because the paid posters could click the button themselves and even use different user IDs to click multiple times. This behavior is also confirmed in~\cite{Bian:2008:FBV:1451983.1451997} as the ``vote spam attack".

\begin{figure}[!ht]
\centering
\includegraphics[width=3in]{likes.pdf}
\caption{The PMF and CDF of the number of likes}
\label{fig:likes}
\end{figure}

\subsubsection{Relevance between Questions and The Best Answers}
This feature is extensively used before in identifying high-quality answers~\cite{Liu:2008:USA:1599081.1599144, Bian:2008:citeulike:2803519, Agichtein:2008:FHC:1341531.1341557, Bian:2009:citeulike:4375490}.
The previous work is usually based on following assumptions:
\begin{enumerate}
\item Semantically high relevance between questions and answers indicates high quality.
\item Selected best answers should have higher quality than other answers.
\end{enumerate}
The above assumptions are risky for the detection of potential campaigns created by paid posters. In commercial campaigns, answers with \textit{high-quality} are rather misleading and would beat the retrieval mechanism. Many of the answers are well-organized and highly related to the questions. In this sense, a ``high-quality'' answer does not necessarily mean trustworthiness. Thus, we do not consider the relevance measure in our work.

\nop{
}

\nop{
}

\subsection{Special Features for CQA Portals}
\label{sec:feature}
The limitations of existing statistical features shown above led us to look for new features specific to users in CQA websites.
\subsubsection{Spam Grade of Questioner ID (SGqID)}
It indicates whether the questioner tends to ask campaign questions. For a given questioner ID (qID), we calculate the ratio of the number of campaign sessions and the total number of sessions in which the user has participated,
\begin{equation}
SGqID = \frac{q_1}{q_0+q_1}
\end{equation}
where $q_0$ and $q_1$ are the number of non-campaign and campaign sessions where the user appears as the questioner, respectively. To avoid $0$ probability, we specify $0.5$ to $q_1$ when $q_1 = 0$. If the system does not have enough information for a certain user(i.e., the denominator is less than $5$), we set its SGqID value to $0.5$. \footnote{This decision follows the Maximum Entropy Principle~\cite{Kapur}, i.e., we should ``make use of all the information that is given and scrupulously avoid making assumptions about information that is not available."}

\nop{The interaction graph could be different because ours are based on a credibility foundation.}

\subsubsection{Spam Grade of Answerer ID (SGaID)}
It indicates whether the best answer poster tends to write campaign answers. For a given answerer ID (aID), we calculate the ratio of the number of campaign sessions and the total number of sessions in which the user has participated,
\begin{equation}
SGaID = \frac{a_1}{a_0+a_1}
\end{equation}
where $a_0$ and $a_1$ are the number of non-campaign and campaign sessions the user appears as the poster of the best answers, respectively. Similar to SGqID, to avoid $0$ probability, we specify $0.5$ to $a_1$ when $a_1 = 0$. If the system does not record enough information, we set its SGaID value to $0.5$.

\subsubsection{Spam Grade of the Text (SGtext)}
It indicates whether the collection of words in sessions associated to a user tends to be campaign specific. To calculate this feature, we need to perform statistical analysis over the words. Text information of a Q\&A session consists of the title, the content of question, and the content of the best answer. We remove the duplicate words so that we can get a collection of distinct words, $word_1$, $word_2$, $word_3$ ... $word_n$, for each Q\&A session. For each word, we calculate \textit{spam grade} which characterizes the property of the word, i.e., whether it is more campaign oriented or non-campaign oriented. Words with higher benchmark are more likely to imply hidden promotion behavior. To get rid of the impact of different length, we take the average value over the summation of the benchmarks of all words as the spam grade of the whole text. For each word, the definition of spam grade goes like this:
\begin{equation}
SGword_i = \log\left(\frac{N+1}{n_i+1}\right) * \frac{s_i+1}{S+1}
\end{equation}
where \textit{N} and \textit{S} are the total number of non-campaign and campaign sessions in the databases and \textit{$n_i$} and \textit{$s_i$} are the number of non-campaign and campaign sessions where the $word_i$ appears. The term ``\textit{$+1$}" is used to normalize the result in case of zero counts. Then the spam grade of text with $L$ distinct words is calculated as:
\begin{equation}
SGtext = \frac{SGword_1+SGword_2+ ... + SGword_L}{L}
\end{equation}

\nop{
\subsection{Other features}
Other features like user reputation, accept ratio. We don't directly use them but extract the information on our own since we can not only extract the sessions in the profile but also the semantic information will be useful to us. If we use the system-calculated scores, that would not be consistent to the records in their profile. Even though it may show some values, it cannot replace the semantic analysis which can only be done on the existing sessions on their profiles. So we just calculate the score by ourselves, if applicable and necessary. We need to find a mapping from malicious sessions to the ratio. Note that a low acceptance ratio doesn't necessarily indicate the abuse of spam. Acceptance ratio doesn't help us find well-formed spam. In other words, trustworthy is different from the quality of best answers.

According to Jurczyk {\em et al.}~\cite{Jurczyk:2007:DAQ:1321440.1321575}, the distribution of users feedback was not even and might remain sparse for unpopular topics. They showed till Jan 2007, fewer than $35\%$ of all questions had any user votes cast for existing answers, in a sample of Yahoo! Answers.
}

\subsection{Property of the Feature Set}

Figure~\ref{fig:4998_f} exhibits the values using the three ``SG'' features in the previous section.

\begin{figure}[!ht]
\centering
\includegraphics[width=3in]{4998_f.pdf}
\caption{4998 samples captured by SGqID, SGaID and SGtext}
\label{fig:4998_f}
\end{figure}

Through this figure, we can observe a clear gap between the campaign sessions and non-campaign sessions. We can then apply regression based techniques to calculate the campaign score, which indicates whether a Q\&A session tends to be a campaign.

\section{Detection Method}
\label{sec:dsd}

In this section, we introduce a logistic regression approach to calculate campaign scores for Q\&A sessions using the three proposed ``SG" features.

\subsection{The Algorithm}
Figure~\ref{fig:4998_f} has already shown that the samples can be distinguished by the three proposed features, SGqID, SGaID and SGtext. In order to get a score indicating whether a Q\&A session is a potential commercial campaign or not, we apply logistic regression as the learning method.  We can use it to calculate values of $P(Y=1|X,\theta)$ and $P(Y=0|X,\theta)$. Here, $Y$ is a indicator variable, where $Y=1$ and $Y=0$ represent campaign and non-campaign Q\&A sessions, respectively. $\boldsymbol{X}$ is a vector of three features for each session. $\boldsymbol{\theta}$ is a vector of model parameters, each associated with a session feature and including an individually constant item(also called intercept term) which is not related to the session features.

By applying the sigmoid function, the hypothesis $h_{\boldsymbol{\theta}}(\boldsymbol{X})$ which outputs a score of $P(Y=1|X,\theta)$ or $P(Y=0|X,\theta)$ (termed as \textit{campaign score}) is defined as follows:
\begin{equation}
h_{\boldsymbol{\theta}}(\boldsymbol{X}) = \frac{1}{1+e^{-\boldsymbol{\theta^T X}}}
\end{equation}
where $\boldsymbol{\theta^T X} = \theta_1 + \theta_2*SGqID + \theta_3*SGaID + \theta_4*SGtext$. To facilitate the matrix calculation, we add an all-$1$ column to $\boldsymbol{X}$.

In practice, the higher the score, the higher the probability that the given session is a campaign session. The values of $\boldsymbol{\theta}$ will be learned by logistic regression.
The objective then becomes an regression problem where we optimize the model so that the output campaign scores of sessions are close to their true labels ($0$ or $1$).

The convex cost function of this optimization problem is given by
\begin{equation}
J(\boldsymbol{\theta}) = \frac{1}{m} \Sigma_{i=1}^m [-y^{(i)}log(h_{\boldsymbol{\theta}}(x^{(i)})) - (1-y^{(i)})log(1-h_{\boldsymbol{\theta}}(x^{(i)}))]
\end{equation}
where $m$ is the number of samples in the training dataset and $x$ is a matrix consisting of $m$ feature vectors of the training samples. We use gradient descent method to find the minimum of the cost function and the corresponding values in $\boldsymbol{\theta}$.

\subsection{Classification Threshold}
The value of $h_{\boldsymbol{\theta}}$ should be carefully determined. We shuffled the $4998$ labeled samples and took $3500$ of them as training set and the remaining $1498$  as test set. Note that the split of the dataset is arbitrary so that we can observe a suitable threshold value. When the $\boldsymbol{\theta}$ is optimized, we then calculate the campaign score of each Q\&A session in the test dataset. The distribution of scores for normal sessions and campaign sessions is shown in Figure~\ref{fig:cdf_test_score}.

\vspace{-0.1in}
\begin{figure}[!ht]
\centering
\includegraphics[width=3in]{cdf_test_score.pdf}
\caption{Scores of test set (CDF)}
\label{fig:cdf_test_score}
\end{figure}

From the figure, we can see that the two types of Q\&A sessions exhibit great difference on the distribution of the campaign scores. Most of the campaign scores are very close to their true labels ($Y=1$ or $Y=0$). Using the scores, we can either provide the raw scores to the users to help them make decisions when reading the answers, or we can assign the labels based on a threshold value, i.e., $Y=1$ when the campaign score is larger than the threshold value and $Y=0$ otherwise.

\vspace{-0.1in}
\begin{figure}[!ht]
\centering
\includegraphics[width=3in]{roc.png}
\caption{ROC Curve of the classification result with different threshold values}
\label{fig:roc_test}
\end{figure}

In Figure~\ref{fig:roc_test}, we show the ROC curve based on different threshold values, $0.1$, $0.2$, ..., $0.9$. The points on the curve are mostly located at the top left position of the curve. The reason is that the campaign scores of most campaign sessions are higher than $0.9$ while the campaign scores of most normal sessions are smaller than $0.1$.  This curve shows that the system performance is robust with a large range of threshold value.

Based on Figure~\ref{fig:cdf_test_score} and Figure~\ref{fig:roc_test}, we set $0.5$ as our threshold for $h_{\boldsymbol{\theta}}$. We also tested it with other randomly shuffle as well as with samples sorted by timestamp and we observed similar phenomenon.

\section{Adaptive Online Detection System}
\label{sec:aods}

In the previous section, we have shown that we can build a model to effectively calculate the campaign score and predict the labels of unknown sessions. In practice, however, this offline analysis does not work well for users who would like to be advised of potential campaigns in real time. This requirement encourages us to design an online version of detection system, which can return campaign scores and/or predicted results in real time. We therefore build a prototype of such an adaptive online detection system. The word ``adaptive" implies that this system can update its database using new samples and generate new model parameters.

\subsection{Overview of System Design}
The major components of the detection system include browser plugin and a remote server.
Figure~\ref{fig:system} shows the system architecture and the communication between the client plugin and the server.

\begin{figure*}[!ht]
\centering
\includegraphics[width=4.5in]{system.jpg}
\caption{System architecture and communication between the client and the server}
\label{fig:system}
\end{figure*}

As shown in Figure~\ref{fig:system}, the sequence of actions that take place when a user opens a Q/A session are:
\begin{enumerate}
\item The plugin first sends only the URL of the page to the server. The
server searches for the url in its database. If it is found, the server
returns the score (spam rating) to the client. The client side script
displays the result. This avoids unnecessarily sending complete web page to the
server if it is already present in the database.
\item If the URL is not present, the server sends a response \textit{not found}
and the client after receiving the response sends the rest of the
data to the server through another \textit{XMLHTTPRequest} and waits for the
server's response.
\item The server receives the data,  segments the text into words,
and stores it in the database. The server then extracts the statistical
features necessary for the analysis from the data. Logistic regression analysis is performed to predict the class of the session (spam or no spam). If the session is classified as a spam, an alert is returned back to the user.
\item The client-side script displays the result to the user.
\item (Optional) If the user is an authorized user, the user can provide feedback to the server (whether or not he/she feels the session is a campaign session). There are three types of users in the system: regular users are those who use our system and they are not granted the right to annotate sessions; helper users are those who have experience and are capable of helping label the data; the administrator is the person responsible for the management of the system.
\item When newly labelled sessions are available, the system updates the detection model using existing and newly labelled data. Note that this step could be done regularly in a  daily or even weekly basis.
\end{enumerate}

\subsection{Plugin Design}
The plugin is a Google Chrome extension. It must be installed on the
Chrome browser in the user's system. The plugin consists of \textit{manifest.json}
file, a HTML file and a \textit{contentscript.js} file. The \textit{contentscript.js} file specifies
the javascript to be executed on the webpage the user is browsing.
The \textit{manifest.json} file contains information regarding the name, version
of plugin and the HTML, script files associated with the plugin. The manifest file also contains a list of permissions that the plugin might use to access servers. The functions of the plugin can be separated into three major steps.
\begin{enumerate}
\item Extract data from the webpage. All the data required from the webpage are extracted from the HTML
source of the webpage. Separate javascript functions were written
for extracting various information. The information extracted includes
the page URL, Question, Questioner Name, Questioner URL, Time of posting
question, Question Category, Best Answer, Answerer Name, Answerer
URL, Time of posting answer, and Rating of the answer. All the functions
are written in the \textit{contentscript.js} file.
\item Send data to the server. The server processes the data and returns
the result. The client-side Javascript communicates with the server by sending
a \textit{XMLHTTPRequest}. The \textit{POST} method is used to send the request because
the data to be sent may be big for using the \textit{GET}
method. Also for data extracted from the \textit{zhidao.baidu.com} website
the encoding of the data is set to \textit{gb2312} in order to encode
Chinese characters.
\item The result is displayed to the user. If the user is an authorized user,  the user can enter his/her feedback to the server.
\end{enumerate}

\subsection{Server Design}
The server communicates with the plugin and also maintains a database system. The database system stores the information of Q\&A sessions and the prediction model. The server receives the Q\&A session data sent from the browser plugin. If the database has the label for the session, the server returns the label. If it is a new session, the server stores it in a buffer, calculates the spam grade based on the current model parameters, and returns the spam grade if necessary (i.e., a campaign session is detected). When enough data has been collected, we can use the \textit{helpers} to label the data. Using logistic regression, the detection model will be updated using previous data as well as the newly labelled data.

\nop{
\subsubsection{Problems}
\begin{enumerate}
\item Some of the data in the webpage were dynamic and hence were not loaded initially. Hence they were not available in the source code of the page. In order to get such data the document.readystate = ``complete" condition is checked. This makes sure that the document is completely loaded before starting to extract the data.
\item The data in the ``zhidao.baidu.com" website was of the encoding `gb-2312'. So the encoding of the XMLHTTPRequest was set to `gb-2312'. On the server side the data is first decoded using `gb-2312' and then encoded to `utf-8' because the database uses `utf-8' encoding.
\end{enumerate}
}

\subsection{Evaluation of Adaptive Online Detection System}
To evaluate the performance of adaptive online detection system, we use the collected data from \textit{Baidu Zhidao} and replay the data in multiple iterations to simulate a real-world scenario. In particular, we pretend that initially we only have partial data and use the data as the training dataset to build a detection model. In each iteration, we add some new sessions and use them as the test dataset to test the performance of the detection system. At the end of an iteration, the new sessions are added into the training dataset, and the detection model is updated using the new training dataset. This step corresponds to the scenario that new data are labeled and added into the system. Then we repeat with another iteration. Note that we sort the Q\&A sessions according to the timestamp when a session is closed. In this way, the performance is closer to that of a real-world scenario.

For the test, we begin with $200$-sample training set and build an initial detection model. At each iteration, we add $200$-sample test set. After evaluating the detection performance, we expand the training dataset with the $200$ test samples, and update the detection model with the new training dataset. We repeat this process until we use up all $4998$ samples.
We evaluate the following four performance metrics:
\begin{align}
Precision &= \frac{True Positive}{True Positive + False Positive}  \nonumber\\
Recall &= \frac{True Positive}{True Positive + False Negative} \nonumber\\
F-measure &= 2*\frac{Precision*Recall}{Precision + Recall}\nonumber \\
Accuracy &= \frac{True Negative + True Positive}{Total Number of Users}\nonumber
\end{align}

Figure~\ref{fig:changingtheta} and Figure~\ref{fig:changingmetrics} show the update of model parameters and the detection performance in each iteration, respectively. In Figure~\ref{fig:changingtheta}, the four ``Theta" from $1$ to $4$ are the parameters for intercept term, SGqID, SGaID, and SGtext, respectively. We can observe that the detection model tends to converge after enough sessions have been added into the database over several iterations. For example, after 10 iterations, the precision achieves $85\%$ - $90\%$.

We also notice that there is a ``degraded" point at the 15th iteration in the recall, f-measure and accuracy figures. After carefully checking the log file of this iteration, we find out the True/False Positive and True/False Negative of this iteration, as listed in Table~\ref{tab:15it}. We can see that the False Negative is very high, which means a large number of campaign sessions is classified as the normal ones. Nonetheless, the system is able to recover from the bad performance and works well over all measures after more Q\&A data is taken into account in training the model. The four metrics are all above $80\%$ during the last few iterations. This test scenario is similar to the practical application where we predicate the unknown sessions using current knowledge and train a new model based on the sessions after we manually label them.

\begin{table}[!htp
\centering
\caption{Test results of the 15th iteration}
\begin{tabular}{|c|c|c|c|}
\hline
TP & FP & TN & FN \\\hline\hline
$92\%$ & $6\%$ & $31\%$ & $71\%$ \\\hline
\end{tabular
\label{tab:15it}
\end{table}

\begin{figure}[!ht]
\centering
\includegraphics[width=3in]{changingTheta_200ini_r2.pdf}
\caption{Adaptive changes of model parameters over time}
\label{fig:changingtheta}
\end{figure}

\begin{figure}[!ht]
\centering
\includegraphics[width=3in]{changingModelMetrics_200ini_r2.pdf}
\caption{The performance of the online detection system over time}
\label{fig:changingmetrics}
\end{figure}

To illustrate the advantage of adaptiveness, we also perform another test in which we fix the model after it is trained on the initial dataset. We use $200$ samples as the initial training data and build a model. We fix the model parameters, and at each iteration, we test $200$ new sessions using the fixed model. The results are shown in Figure~\ref{fig:fixedmodel}.

\begin{figure}[!ht]
\centering
\includegraphics[width=3in]{fixedModelMetrics_200ini_r2.pdf}
\caption{The performance of the fixed model}
\label{fig:fixedmodel}
\end{figure}

Since the parameters of the fixed model is only trained on the first set of training samples, we omit the figure for the system parameters. The precision in some tests is nearly 50\% and it even becomes very high in a few tests from the 15th to the 20th iterations. However, compared to Figure~\ref{fig:changingmetrics}, we note that the recall values are always very low. It means that the false negative is high. The low f-measure values confirm this problem in the fixed model.
It means that the non-adaptive model classifies many campaign Q\&A sessions as the non-campaign sessions. Consequently, although the precision is high, other metrics indicate that the non-adaptive model has obvious bias in classification. What's worse, this model cannot update itself by new samples because the parameters are only trained on the initial training dataset. Therefore, making the predication model adaptive is critical for accurate predication in practice.

\section{Related Work}
\label{sec:rw}

\nop{(A paper introduced the proportion of useful best answers.)}

\nop{
Find papers saying that the user feedback is valuable...
Finding the right fact in the crowd
a few bad votes too many
}

\nop{
Our goal of research and the reason why we choose Chinese CQA site as the target.
This paper conducts comprehensive and substantial work of evaluation of answer credibility in the Chinese Q\&A website, Baidu Zhidao. Our work can shed light on Q\&A websites of other languages because we reveal new features of Q\&A spam which were not covered in previous work. Our goal is to identify the credibility of the selected best answers in Q\&A sessions.

According to~\cite{Wang:2012:WWW}, China has the world's largest Internet population (485M) and two largest and most representative crowd-sourcing systems are host on Chinese networks. The work of spreading rumors and malicious advertisements are accomplished by the large amount of crowd-sourced labor. These malicious activities are also called crowd-turfing. The researchers in~\cite{Wang:2012:WWW} conducted a survey on the crowd-turfing market in other countries. They found that only $12\%$ of all the campaigns on the Amazon Mechanical Turk, a US-based crowd-sourcing website, were crowd-turfing type, decreasing tremendously from $41\%$ spam-related tasks according to a report~\cite{AMT:2010} in 2010. This confluence of factors suggest us that dataset collected from the Chinese websites will provide us varieties of spam so as to make our research robust to different types of spam attack.
}

Our research is mostly related to work on spam detection and recognizing experts or authoritative users and trustworthy content in the social media. We discuss prior work on two aspects.

\subsection{Retrieving High-Quality Answers in CQA Sites}

Jeon {\em et al.}~\cite{Jeon:2006:FPQ:1148170.1148212} attempted to predict the quality of answers in a community based question answering service with only non-textual features, such as \textit{Answerer's Acceptance Ration}, \textit{Answer Length} and \textit{User's Recommendation}. They assumed the user feedback was a reliable source for the evaluation. Jurczyk {\em et al.}~\cite{Jurczyk:2007:DAQ:1321440.1321575} presented a study of link structure of Yahoo! Answers. They adopted an adaptation of the \textit{HITS} algorithm~\cite{Kleinberg:1999:ASH:324133.324140} for finding experts in the Q\&A portal. Their research was based on the assumption that the user feedback could be used to assign weights on the edges of their graph representing user relationships.

Liu {\em et al.}~\cite{Liu:2008:USA:1599081.1599144} applied their automated summary technique to summarize answers for questions which ask for opinions. Bian {\em et al.}~\cite{Bian:2008:citeulike:2803519} tried to use both relevance between questions and answers and the quality of answers to retrieve good answers for a user query. Later, in another work by Bian {\em et al.}~\cite{Bian:2008:FBV:1451983.1451997}, they considered the effect of vote spam attacks. Such activities involved malicious voting for specific answers to improve their ranking and to decrease the ranking of competitors at the same time. Agichtein {\em et al.}~\cite{Agichtein:2008:FHC:1341531.1341557} studied the basic elements of social media and combined three features of the social media to facilitate the task of identifying high quality content, namely intrinsic content quality, interactions between users and content usage statistics.

Fichman~\cite{Fichman:2011:journal} conducted a comparative study of answer quality on multiple Q\&A websites. Accuracy, completeness and verifiability were used as the quality measures for cross platform comparison. Fichman found that the quality of answers was significantly improved only in terms of answer completeness and verifiability, not the answer accuracy.

\nop{
Bian {\em et al.}~\cite{Bian:2009:citeulike:4375490} developed a semi-supervised coupled mutual reinforcement framework to calculate content quality and user reputation at the same time. Similar to their previous work, the feature spaces they adopted in the framework were not robust to the spam attack described in our case.
}

\nop{
(In our work, we show the distribution of some statistical features. And show the distribution of common words between questions and answers. And in our work, we mostly depend on text features.)
}

\nop{
Put this to the feature analysis part
According to Jurczyk {\em et al.}~\cite{Jurczyk:2007:DAQ:1321440.1321575}, the distribution of users feedback was not even and might remain sparse for unpopular topics. They showed till Jan 2007, fewer than $35\%$ of all questions had any user votes cast for existing answers, in a sample of Yahoo! Answers.
}

\nop{
Kim {\em et al.}~\cite{Kim:2009:URC:1526645.1526658} provided a comprehensive study about the selection criteria when people chose the best answers.
}

\nop{

}

\subsection{Work on Crowd-Sourcing Spams in Different Realms}
Previous research has also investigated the crowd-sourcing spam in other areas. Jindal {\em et al.}~\cite{Jindal:2008:citeulike:2402521}, Ott {\em et al.}~\cite{Ott:2011:FDO:2002472.2002512} and Arjun {\em et al.}~\cite{Mukherjee:2012:SFR:2187836.2187863} attempted to detect fake review or opinion spam in the online shopping stores, like Amazon's online store. Similar to research in CQA websites, they also used textual similarity features and user-oriented features, like ratings and history records. Huang {\em et al.}~\cite{huang-yang-zhu:2011:IJCNLP-2011} developed a regression model with features suggesting quality-biased short text in Microblogging service, Twitter. They judged the quality of tweets based on relevance, informativeness, readability, and politeness of the short content and assigned different scores from 1 to 5. However, they didn't explicitly present how they define a spam-like tweet. Huang {\em et al.}~\cite{Huang:2010:DCS:1927585.1927611} conducted a similar study of commercial spam on blogging sites. They showed that the propaganda of some products in the comment of a blog post was crucial in detecting the malicious comments. The propaganda appeared in the form of URL, phone number, E-mail address, MSN numbers etc.

\nop{
}

\nop{
}

\section{Conclusions}
\label{sec:conclusion}
Detection of hidden campaigns can improve the user's experience when using current social websites. In this paper, we disclose the behavior of a specific group of online paid posters who create commercial campaigns on the community Q\&A websites. We collect real-world datasets and identify effective features to distinguish normal sessions and the campaigns. The performance of our classifier, with integrated statistic and semantic analysis, is quite promising on the real-world case study. Based on a learning technique, we also implement a prototype of adaptive online detection system which can retrieve the result in real time. The campaign scores and/or predicated labels can help users make better decisions when searching for answers on CQA portals and help the questioners select better answers as well.

This work is our first effort to detect online paid posters of CQA websites. In the future, we will test more features to improve the adaptive performance.

\nop{
\section*{Acknowledgment}
We thank Natural Sciences and Engineering Research Council of Canada (NSERC) for partial funding support and Mathematics of Information Technology And Complex Systems (MITACS) for supporting the fourth author's internship at the University of Victoria.
}

\nop{
One disadvantage of the study is that it relies on the exact named entity recognition. Consider that except the public crowd-sourcing websites, there are underground business assigning missions that we know nothing about. That is, how the detecting system react when new products' names appear in the Q\&A sessions?
}

\nop{
section{Issues}
On this site, the asker should register but can post as anonymous. While the answer doesn't need to have an account. In this case, we cannot use the user's history data.

This work focuses only on the Q\&A sessions, not the users whether they are paid posters or normal users.

We will also work on the prototype and possibly make it public.

}
\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
Q: Match formula on Excel not working I have the following formula in Excel.
=IF(ISERROR(MATCH(G1,$A$1:$A$62052,0)),"NO Match","Match")
The trouble is it only shows Match when the data matches and Match when the data does not match. What is wrong with my logic?

A: It appears that I am getting Match and No Match but because I am dealing with large columns there was a delay in the cell updating when I changed the a data cell in the G column. The formula works. Thanks for your time.

A: Why are you using ISERROR?
ISERROR will return FALSE if your formula does not return an error.
So if
MATCH(G1,$A$1:$A$62052,0)

doesn't return an error, the first argument will return FALSE, and the output will be 'Match'.
I'm not sure exactly what you're trying to do, but I think you want
=IF(MATCH(G1, $A$1:$A$62052, 0), "NO Match", "Match")

redpajama_set_name: RedPajamaStackExchange


Have you heard about the Ultimate Blog Party 2010 at Five Minutes For Mom?
It is a blog carnival where bloggers publish a "party post" introducing themselves and then party hop through the linky lists and meet other bloggers. It is also a great way to network and make new friends! Even if you don't have a blog, you can still participate in the party.
Oh, and did I mention that there are also amazing prizes?
The UBP 2010 will be held April 9-16th and you can find instructions on how to join here.
This will be my 2nd year participating and I can't wait! Hope to see you there!
1 comment on "You're Invited!"


by Eta Epsilon Chapter of Sigma Gamma Rho Sorority, Inc.
Sigma Gamma Rho Sorority is a leading national service organization partnering with March of Dimes to focus on a healthy generation. Our mission is to better the quality of life in the community in a variety of way.
Join Thee "Elite" Eta Epsilon chapter of Western Michigan University in Partnership with March of Dimes for an evening of great food, live entertainment and raising funds for premature babies. All proceeds will donated to research, medical services and aid support for families with premature births.


During winter your furnace contributes 20%-40% to the total utility bills. You can reduce the prices of the natural gas but you can increase the efficiency of your furnace. Some simple steps are to be followed to lower the running cost of your gas furnace and to increase its efficiency.
Call a professional for the yearly maintenance of your gas furnace. Maintenance should be done before the beginning of the cold winter months. Timely check up will help to solve the problems and keeps your furnace running efficiently.
Reduce the temperature of your thermostat to 680C. Wear some hot clothes to stay warm and also reduce your furnace cost.
Clogged or dirty filters reduce the air flow in the furnace and make your gas furnace work harder to maintain the temperature.
Changing filters every month maintains the efficiency of your furnace and furnace warms your room easily without much load resulting in low energy cost.
Programmable thermostat helps you to set different temperatures when you are at home and when you are out. Using a programmable thermostat will help you to reduce your furnace bills.
Warm air travels inside the house through duct work. If there are holes or cracks in the duct work or your ducts are not properly sealed. Use duct sealing tape to seal the ducts.
If you find any leaks in the doors, windows or walls off your house seal those leaks. As air leaks make your gas furnace to work harder and results in high bills.
Keep the curtains of the windows open during day time so that rays of sun can reach your home and make the rooms naturally warmer. This will reduce the load of your gas furnace.


Have you been finishing off all those boxes of chocolates you received last Valentine's Day? Or have you been snacking on some cookies that were given to you by your loved ones and friends? You are not judged. We've also been there and done that. But after feeling special and loved with all these sweet treats, you might experience a painful sugar rush. Nothing to worry, though.
These 10 super foods should help.
Wait, water is not a food. However, we want to highlight its amazing effects on us. Water can help flush out the excess sugar in our body. So, the next morning you wake up with all those chocolate wrappers around you, grab a glass of water.
Although sweet, cinnamon actually helps balance blood-glucose levels. It also helps lower the blood sugar in our body. Therefore, regardless of how you add it to your diet, you can recover from that Valentine's Day binge.
Yes, coconut oil contains fats. But take note that these are healthy fats that protect your body against bacterial growth and all that post-sugar trauma. Now, if your pantry doesn't have this staple, it's high time that you buy one. You don't want to miss a million of things coconut oil can do.
Interestingly, kale has this ability to restore the sugar balance in your body and fight off any possible inflammation. Eat it raw. Juice it. Make kale ice cubes. Or add it to your favorite dishes. Whatever you make out of it, it will surely help.
It's hard to break up with someone. What's even harder is to move on. The same goes with your love for sugar. If you ever decide to put an end to your relationship with sugar, eating brown rice will make the process of moving on easier and faster.
Hummus contains fiber that helps regulate blood sugar. Also, it has protein that can curb your sugar cravings, which cause insulin surges. So, if you want to recover from that sugar overdose, gather some garbanzo beans and process them in a food processor for a homemade hummus.
Too much sugar can deplete the amounts of magnesium in your body. Without it, your body organs might not function properly. Lucky for us, there are pumpkin seeds that contain the right amount of magnesium needed by our body. In case you are asking, "Are there pumpkins on February?". The good news is that there are grocery stores that sell these pumpkin seeds. So, show them some love!
Avocado is another food that contains healthy fats, which aids in balancing blood sugar levels. Having said that, if you want to recover from all that sugar overindulge, start your February 15 with an avocado toast.
Sometimes, when we overeat candies, ice cream, and chocolates, our tummies start to feel strange. If you experience that after Valentine's Day, try to treat yourself with some ginger.
Any way you like your eggs to be cooked, they are truly a great help for your post-sugar splurge. Not only are they healthy sources of protein, they also help burn off those excess sugars that start to build up.
These super foods indeed play a vital role in your road to recovery from that Valentine's Day sugar rush. But if you want to reap more health benefits, consider experimenting recipes with them and these 10 superfoods!


You can't beat the price for this top quality leather girth with removable sheepskin! Leather in a rich, warm cognac color combined with plush natural sheepskin provides comfort for your horse and style for you! Easy to clean, this girth is ideal for use in the show ring or in the schooling ring. Extra sheepskin liners allow you to keep a clean girth on hand at all times!


Several Box Builders needed for Industrial company in Orange. Qualified candidates must have at least 1 year of working forklift experience. Forklift Certification & Valid ISTC a plus. Duties include packaging containers, forklift & container inspections, removal/disposal of product from containers, staging pallets for boxes, building boxes, applying labels per packaging instructions, etc. This is a long term contract position but candidates will have the possibility to be hired as a permanent employee in the future. As a contract employee candidates will be offered major Medical/Dental and vision as well as accrued vacation days. Must be able to work rotating shift work which will include OT. Pay is $12.00 per hour. Interested applicants please apply at 3450 Eastex Freeway in Beaumont at 8:30, 9:30 or 1:45pm Monday – Friday with a valid ID and resume.


text:
У́лица Пали́ха — улица в Тверском районе Центрального административного округа города Москвы. Проходит от Новослободской улицы до Сущёвской улицы.

Описание
Палиха соединяет Новослободскую и Сущёвскую улицу. Нумерация домов начинается от Новослободской улицы. Улица продолжает Лесную улицу, продолжением Палихи за Сущёвской улицей является Перуновский переулок. Палиха идёт с запада на восток, длина улицы 300 метров.

Происхождение названия
Этимология названия неясна и остаётся предметом споров историков. По наиболее распространённой версии улица первоначально называлась «Полиха» по полю, простиравшемуся между улицей и Бутырской слободой, а в XIX веке под влиянием московского «акания» стала «Палихой». Согласно другой версии название происходит от имени владельца имения на этой улице, поскольку большинство московских названий, заканчивающихся на «-иха» (Барышиха, Плющиха) имеют именно такую этимологию. Существуют предположения о связи названия Палиха с глаголом «палить».

Примечательные здания

В советское время улица была застроена зданиями типовой архитектуры, интерес представляет лишь дом № 13 — последний дом по нечётной стороне улицы на углу с Тихвинской улицей, завершающий перспективу Сущёвской улицы.
 № 3 — особняк (1896, архитектор С. Ф. Федотов).
 № 13 — особняк конца XIX века с фасадом, богато украшенным лепниной. В рамках гражданской инициативы «Последний адрес» 30 августа 2015 года на доме установлены мемориальные знаки с именами санитарки Веры Семёновны Морозовой и военного Николая Ипполитовича Бениславского, расстрелянных в годы  сталинских репрессий.

Транспорт
Ближайшие к улице станции метро «Новослободская» и «Менделеевская». По Палихе проходили маршруты трамвая № 5, 7, 19. В 2008 г. движение трамваев было закрыто в связи с уничтожением разворотного кольца на Лесной улице. С 22 сентября 2012 года движение трамваев по улицам Палиха и Лесная восстановлено и было представлено единственным 9-м маршрутом. Для этого вместо кольца был построен оборотный тупик и пущены специально переоборудованные двухкабинные двухвагонные поезда. С 9 сентября 2017 года по улице проходят маршруты № 7 и 9.

Примечания

Литература
 Романюк С. К. Москва за Садовым кольцом. — М.: АСТ, 2007. ISBN 978-5-17-044643-8

Ссылки
 Карты Яндекс — Палиха

redpajama_set_name: RedPajamaWikipedia


themselves, for "knowing" better but just can't pull the trigger on change.
the process of change. One element of change is called AMBIVALANCE.
nature to want and not want two things at the same time.
body, as they have stopped "pitting" the two thoughts against each other.


text:
Between them, Darcy and Paul Ellarby have nearly 60 years experience in curling and over the years have been deeply involved in almost every aspect of the sport. Beginning with their starts in 1983 (Paul) and 2002 (Darcy), the pair has worn almost every hat there is including club curler, competitive player, club founder, certified instructor, certified official, coach, and ice maker!
Together and individually, Darcy and Paul have had competitive success at the regional, state, and even national levels, with Darcy skipping a women's team to Gold Medal victory at the 2013 Arena Nationals and again to a Silver Medal victory at the same event in 2016. Other competitive wins include the MN State New Clubs Championship in three successive years (2009, 2010, and 2011) and a first event championship for Paul at the Raymond Kaiser Memorial Bonspiel in 1985 (a GNCC-wide competition).
Beyond competition however, Darcy and Paul are most well known for their long-standing involvement in the development of our sport at the grassroots level. In 2006, Darcy and Paul founded the Dakota Curling Club playing at the city ice arena in Burnsville MN. After 11 years on arena ice, the Ellarby's successfully led the club's transition to dedicated ice in the nearby suburb of Lakeville MN.
As club founders, Darcy and Paul have intimate experience with nearly every aspect of club operations. With their experience in founding and growing curling clubs, Paul's extensive ice-making experience, and Darcy's on-ice curling and coaching experience, they have a lot to offer clubs, teams, and individual curlers.
In 2018, Darcy and Paul had the good fortune to purchase Dakota Curling Supplies from George and Lucille Phillips, who had built this successful business over the prior 30 years from their location in Langdon North Dakota. After relocating the business to Lakeville, Darcy and Paul have invested heavily in modernizing and growing the business to better serve curlers and clubs across the nation. Major changes include a modern e-commerce website, four mobile pro shops serving the mid-west and GNCC regions, and a well established wholesale program supporting smaller regional curling suppliers and pro shops all over the US.
For those curious about the name overlap between Dakota Curling Club and Dakota Curling Supplies - that was in fact purely a coincidence that now looks a bit like foreshadowing in hindsight! When the Ellarby's founded their curling club in 2006, they named the club after the county in which it is located, unaware of the curling supplies business with such a similar name. Dakota Curling Club and Dakota Curling Supplies are in fact two completely separate business entities with nothing else in common other than a connection to Darcy and Paul!
Darcy and Paul are both extremely dedicated to supporting curlers at every level from club curler to competitive player and every curling facility, whether arena, dedicated, or on the pond in your back yard.
George and Lucille built their business on friendliness and a personal touch that has endeared customers for decades. You can be assured that this tradition will be upheld as Darcy and Paul take this business forward into the future!