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All Articles Tagged "driving"
Safe Driving For Expectant Mothers: License Renew While Driving For Two
With July being the most popular birth month, many soon-to-be moms are currently in their third trimester. A recent survey by Ford found that 88 percent of women say they drove their entire pregnancy, with only seven percent stopping at the end of their third trimester.
When I was pregnant and driving, I had a car side-swipe me and it definitely rattled me up. My doctor and I talked about how to eat, exercise, work and sleep safely for baby, but the topic of driving–I'm not blaming the doctor–never came up. When I took the time to research driving safety for moms-to-be, there was very little information out there are far as rules, guidelines and even suggestions.
With new moms in mind, Ford is starting the conversation by bringing safe driving guidelines to the forefront, and working with Well Rounded NY to kick-off "License Renew While Driving for Two," a comprehensive guide for road-ready moms-to-be — and Mommynoire was invited along for the ride!
Recently at a Mother's Day luncheon with Kaity Velez and Jessica Pallay (co-founders of Well Rounded) at WILD in Brooklyn we real New York moms shared our pregnant driving tips from personal experience. We tested out the all-new 2017 Ford Escape and also got to try on an Empathy Suit, a wearable imitation of third trimester pregnancy, to allow us to experience pregnancy behind the wheel, just like Ford engineers, designers and safety teams.
Driving Tips For A Well Rounded Trip
- Adjust your mirrors. You may be adjusting your seat on a daily basis as your belly grows, but make sure you follow suit with your mirrors to limit blind spots
- Prep is everything when it comes to food cravings. We know cravings can hit at any moment–hard. Have snacks and water on hand and easily accessible so you're never reaching while driving.
- Nausea happens. For many moms-to-be, nausea is practically a given. And being in a moving car can make it worse. Nip it in the bud with a motion-sickness bracelet or an easily accessible baggies filled with anti-nausea baggies.
- Distractions are dangerous. If you're pregnant with baby No. 2, you're probably driving around with the cutest little distraction ever Don't let your little one's cries, giggles and screams cause you to take your eyes off the road. If you're concerned about something, pull over.
- Plan your route. Pregnancy brain is for real. So no matter how many times you've driven these same streets, you may find yourself lost. Map your trip beforehand and use your car's voice-command navigation to help find your way.
Here are some other safe driving tips for moms-to-be:
Always wear your safety belt.
Remove bulky clothes so the safety belt can be placed as close to the body as possible.
Move your seat as far rearward as possible while still able to reach the pedals comfortably.
The lap belt portion of the safety belt should fit snug and be positioned low across the hips, below the belly.
Make sure the lap belt does not run across the belly, but lies as flat as possible under the curve.
Shoulder belt portion of the safety belt should be positioned across your chest, between the breasts and to the side of the belly.
Never tuck the shoulder belt under your arm or behind your back – this can hurt both you and the baby.
Driver Roll Up The Partition, Please. Stars Who Never Learned To Drive
Whether they were too busy on set when they were 16, or they're terrified of hitting the freeway, these celebrities never learned to drive. But that didn't stop some of them from doing it anyway. Better buckle up out there!
Tags:cars, celebrities, driving
Dayton Police Officer Admits To Pulling Over Black Motorist For Making "Direct Eye Contact"
The number of problems brewing with direct regard to law enforcement has been plenty. Recently, the tensions between Blacks and police officers has ensued, as simply "looking while Black" has emerged as a crime in itself.
John Felton, a Black man, was followed by a police officer for nearly two miles while driving to his mother's house in Dayton. Feeling uneasy, he decided to recordthe entire incident in full on his cell phone for proof to support his claim, in case anything should have happened. The officer finally stopped tailing Felton, pulling him over for failing to signal "100 feet prior to his turn." A perplexed Felton answered, "You've been tailing me for how long? You just needed a reason to pull me over. No disrespect, I don't have nothing against police officers, but all this sh*t that's going on now? That's some scary sh*t. To have a police officer just tail you, and then you pull me over, 'cause you said I didn't signal — what? Do you know how it looks?"
After running Felton's identification and his passengers as well, the officer admitted the real reason as to why he was pulled over. "Because you made direct eye contact with me, and you held on to it while I was passing you," the officer said. In the footage, a rightfully frustrated Felton tries to explain to the officer that he didn't even see him. "I'm not going to argue you with you about it anymore, you can keep talking and I can give you your license back and give you a citation for the violation and we settle it in court," said the officer, cutting Felton's reply short.
This incident happened back in July and is just now beginning to spark conversation via social media after being obtained by The David Pakman Show. The footage was even aired on Dayton's WKEF on Thursday (Aug. 27) night. Prior to its airing, Dayton Police Chief Richard Biehl had not returned the local station's calls and emails requesting comment, but later offered that they were "aware of the video and reviewing it" via the department's Twitter account.
Watch the video that Felton recorded above.
Road Rage and Babies Don't Mix!
Just the other day I was coming home from the park after what I call a pregnancy 5K. Only a couple weeks shy of my seventh month preggers, I was quite happy I could still move the way I did before the bump. I proceeded to head home like normal and while I was expecting some evening rush traffic, nothing prepared me for an erratic drive who practically forced me to the side of the road.
There are no words to describe how I felt at that moment. Who did this joker think he was as he used his car to push me out of my lane – coming inches from side-swiping me. Now, the old Tanvier would have felt the need to get out of my car and lose my mind on both the driver and his vehicle. He would definitely have learned today! However, as an expecting mother, I am pretty sure it wouldn't have been a good look to act in such a way. Plus, you just never know how the other person will react.
This got me to thinking about road rage and how stupid it really is. Trust me when I say I am far from being a saint. There may have been a few occasions in the past where I may or may not has cut someone off, given the finger, was "friendly" with my brakes or called a person everything under the sun. It can happen, right? Call it an epiphany, maturity or just plain common sense – I'm not trying to get caught up in a pointless or worse, fatal situation that wasn't worth it to begin with.
Splashed throughout the headlines right now is an incident that stems from road rage and resulted in multiple arrests and a seriously injured man who was ran over. While the police work to sort out who's at fault, I am surprised no one is talking about a pretty seriously issue – the driver of the SUV had his wife and two year-old in the car!
Now he claims a large group of bikers were driving erratic prompting him to call 911, but what I can't understand is why he (according to a witness) began to cut bikers off the road which more than likely was the catalyst for this entire debacle. I don't care how pissed someone makes me feel while driving, I have a responsibility as a parent to protect my family. Even if someone's actions are wrong, my reaction could make things much worse, which it looks to be true in this instance. What if the other person decided to retaliate as well cutting you off thus creating an accident where someone gets hurt (like your child)? What if they decide to pull a gun not knowing who was in your back seat, or if you yourself were pregnant? There are way too many what if scenarios to roll the dice on how our reactions affect an outcome. It's just not worth it.
The bottom line is road rage and families don't mix, period. You would think this would be a common practice for everyone but it's not. How many news stories do we have to read about unnecessary accidents and fatalities? And what about victims who had nothing to do with the situation that get hurt – is it worth catching a case? I know it can get hard but for heaven's sake think about your babies and your goal of making it home.
Too Much Partying:Tracy Mourning Arrested in Miami After Driving Under The Influence
Well, this a bit disheartening but nobody is perfect.
Tracy Mourning, wife of former Miami Heat player Alonzo Mourning, was arrested on Friday morning driving under the influence in Miami.
According to The Huffington Post, Mourning was clocked going 28 miles over the speed limit in Coconut Grove while driving her Porsche.
When a police officer finally stopped her a few blocks down, she'd apparently been swerving and she ran a stop sign.
At that point, the officer says Mourning then put on her charms and asked in a nice voice, "What did I do officer," followed by a wink and then saying, "Was going fast, wasn't I?" Okay so even at almost 4am and knowing she was wrong, Tracy tried to use the "bat your eyes" trick to get out of any trouble.
But the officer wasn't buying it. He said her eyes were bloodshot, she was slurring her speech and reeked of alcohol. After failing a roadside sobriety test and refusing to take a blood alcohol test, she was then arrested.
No word yet on what time she was released and when she'll have to appear in court. Her hair still manages too look good, even in the booking photo.
The Mournings have become pillars of the community in Miami where they spend countless hours giving back. Alonzo has been known for years to throw one of the best fundraising events in town with his "Zo's Summer Groove." Along with that huge money maker, the couple often scouts for other ways they can get back.
Hopefully, Tracy can get this taken care of and be extremely careful when it comes to drinking and driving (how about just don't do it).
Honestly, how many of you still get behind the wheel even though you know you've probably had one too many drinks? Do you still get in the car with someone who you know has had too much to drink and let them drive?
Tags:Alonzo Mourning, basketball, charity, drinking, driving, DUI, heat, miami
Drive Around With Your Diploma! Car Insurance Companies Give Educated People Better Deals
You would assume when it comes to car insurance that the most important aspect in determining your insurance premium would be the number of accidents you've had in the past, right? WRONG! According to CNN Money, that might be the NEXT important thing after your level of education.
After studying the five major insurers in 12 cites, the Consumer Federation of America found that major insurers charged a safer driver with less education and a lower paying job higher premiums two-thirds of the time. In most cases the premiums were 25% higher, however in some cases the premium was double.
In one instance studied by the CFA, two women of the same age and with the same salary requested quotes from Allstate in Baltimore with the only evident difference being that one was married with a master's degree, while the other was single with only a high school education. The woman with the master's degree was quoted $1,248 compared to $3,292 for the high school graduate. Even though, the woman with only the high school education had a flawless 10-year driving record and the college graduate had recently caused an accident.
Surprisingly, other income-related factors like occupation were also weighted more heavily than driving-related factors like miles on the road.
Although I'm not sure how being more educated correlates with being a better driver, especially with all traffic tickets, getting a break on car insurance sounds good to me.
Whether you're a college grad or not, per the advice of the Double Saving Divas, you'd want to ensure you shop around to find the best rate. Speaking of the Divas, be sure to check out Madame Noire Business and the Double Saving Divas money-saving chat on our Facebook page.
Tags:car insurance, Double Saving Divas, driving, money, saving
Android Shuts Down Teens' Ability to Text & Drive
Did you know that motor vehicle accidents are the single highest cause of death for young people?! I can't even remember how it was before texting became so popular that we had to set laws to make people stop doing it while they drive. I have been guilty of this very irresponsible action and now I try my best not to even touch my phone while driving unless I'm taking or making a call.
Right now my 15-year-old daughter is so overwhelmed with driving that she can barely listen to the radio and drive at the same time. It warms my heart to watch her clutching the wheel with white knuckles afraid to take her head off of the road for even a moment for fear that she will make a grave mistake. I'm glad she's scared because it will lend to her being cautious but with time I know this fear is going to wear off. Eventually she will become just like the rest of us, driving with one hand or God forbid her knee while putting on makeup and eating.
As a result of studies which have shown that the dangerous practice of texting while driving is on the rise among teens and young adults, Android has designed an app called Textecution that can be installed on kids' phones. My prayers have been answered! This revolutionary new app is designed to shut down all texting functions—sending and receiving—if the phone handset is moving at more than 10 miles per hour. It seems that Android still has a few bugs to work out but it's a terrific step in the right direction towards keeping our children safe on the road.
Two percent of all deaths in America are caused by traffic accidents. The saddest part about that startling statistic is that a large portion of these accidents are totally preventable. Since it's currently illegal to install mobile phone jamming technology in vehicles, Textecution is your best bet and it's available now for $39.95 but only on Android handsets. Apple refuses to let application developers interfere with core phone functions like texting.
Call your cell phone provider and ask them if they plan to catch up with Android on the technology front.
Source: Gizmag.com
Sid Powell is the NAACP-nominated screenplay writer of 'Somebody's Child', a mother of two, and the owner of SIDPo Productions. Read more about how SIDPo Productions is 'Changing Everything' at www.sidpoproductions.com.
Tags:automobiles, cell phones, driving, internet, school
Driving Safety For Your Teen
I don't know where the time went, but in a little over a month, my teenaged daughter will be eligible for her learners driving permit. My heart rate spikes every time I think about her being behind the wheel of a vehicle, especially one that I'm in! Nevertheless, I'm doing all I can to arm her with the knowledge, education, and hands on experience she is going to need in order to survive in the streets.
Here are a few safety tips that you MUST reiterate to your driver-to-be:
Focus On the Road
Although socializing while driving is acceptable, remember your number one priority – safely operating your vehicle. Let your friends know that you aren't ignoring them; you are simply focusing on keeping them, and yourself, safe.
Don't Get Distracted By Technology
Completely avoid flipping through your music library or answering texts while driving – they can wait. Even removing your eyes from the road for a second can make all the difference between avoiding a collision and having to pay to repair a stranger's bumper.
Recognize Hazardous Situations
You know snow is dangerous to drive in, but what about the seemingly safe situations? Heavy rains can cause flooding and often are accompanied by forceful winds. A dark night and unfamiliar roads are breeding grounds for accidents. Without proper experience it is best to avoid driving in situations that make you uncomfortable. Spend the night or wait out the storm at a friend's house – you'll be happy you did.
Words By: Sid Powell
A Woman's Work: 10 Things Every Woman Should Know How To Do
I remember as a young girl watching my great grandmother do so many things around the house. She'd cook, clean, sew and iron. She always seemed busy doing what my Poppop called "women's work." I thought to myself, "There has to be more to being a woman than knowing how to do domestic things" – but have times changed that much from when I was a child? While traditionally there are some tasks that are gender specific, I can't help but think women nowadays are forging their own traditions with a "roll-up-your-sleeves and get-the-job-done" type of attitude. Yes, you should be able to cook a great meal, keep your house clean enough to keep the vermin away and iron your clothes well enough to be presentable at work. That's a given. But we can do better than that in 2012.
Here are 10 things I think all women should know how to do – we go to work!
Tags:cleaning, cooking, CPR, domesticated, driving, gender roles, How To, swimming, work
3 Questions to Ask Your Insurance Agent Before You Sign on the Dotted Line
Depending on the state you live in, your insurance policy may cover only the listed drivers, the people who live in your home, the occasional borrower, or any combination of these options. For example, Virginia insurance laws require that a policy cover everyone in your home as a driver, whether listed on the policy or not. Before you take out a policy, make sure your agent explains to you exactly who is covered and under what circumstances so you know whose car Junior will and WON'T be driving.
This is actually pretty important. There are two types of car parts: original equipment manufacturer (OEM) parts, which are the exact part specified for your car from the company who made your car, and aftermarket parts, which are parts made by another company for a general range of models but not specifically for your car. Think of it as the difference between Kraft and the store brand cheese. Some state laws allow your insurance company to use whatever parts it likes when fixing your car. Ask your insurance agent what your insurer's policy is on this so that if your car needs repair, you can possibly be supplied with parts that are meant for your make and model and not just any kind of car.
Did you already run my driving record and credit check?
Your insurance rate is based on many factors, including your driving record and your credit rating. Some companies don't run these reports until after you've bought the policy and the underwriting department is setting up the account. Instead, the agent uses the information you gave him to calculate the rate he's giving you. So if there's more to the story than you're telling him, you're going to see a big increase in your rates when you get the initial paperwork in the mail. Know what you're getting into and what you'll be paying before you actually have to start.
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Hotel Normandy
This 4 star hotel is considered as a first class property. It is ideally
situated to do the most famous tourist monuments, museums and shopping
in Paris, and offers its guests good service. WIFI is available. A
delicate "retro" environment for this large hotel which accomodated the
English customers in Beautiful Epoque. You will appreciate there the
charm of the waxed woodworks and the atmosphere cosy of its piano bar.
But Normandy has a master trump : its exceptional location near the
Louvre Museum and at the corner of the street St-Honore, celebrates for
his luxuries shops.
Choose your room
Share it
Score based on 8 reviews
Services and terms: Hotel Normandy
Accommodation facilities
- Lift
- Bar/Café
- Business Centre
- Money Exchange
- Meetings Room
- Nursery
- Internet Centre
- Laundry
- Non-smoker Rooms
- Parking
- Restaurant
- 24 hr Room Service
Standard Room Services
- Safe
- Heating
- Minibar
- Satellite TV
- Pay TV
- Hairdryer
- Telephone
- Television
- Video
Useful information: Hotel Normandy
Other names for Hotel Normandy:
Normandy Hotel Paris
Location: Hotel Normandy
The location of this hotel is excellent, within just a few steps of the
famous area of Louvre Museum, Place de La Concorde, Place Vendome and
Avenue de l'Opera. The hotel is in a few minute walk from the subway
station (line 14), the district is very attractive and offers many
advantages, such as fashionable boutiques on Faubourg Saint Honore,
restaurants and public transport.
By public transport : Underground (station Palais Royal - Musée du
Louvre - Line 1 : direct access to Champs Elysées, la Défense and Lyon
train station. Line 7 : to "Grands Magasins" and the Nord train station.
Underground (Line 14) Station Pyramides. By car: Take the motorway A1
and follow the Centre-ville/Chatelet indication, direction Opera, rue de
l'Echelle. The hotel is on your right.

subset: commoncrawl


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Social media has become the ultimate platform for marketing, advertising and lets face it, selfies.
These celebrities (or their managers) have mastered the art of promoting their brand through Social Media. However, this will not only be judged on the amount of followers each celebrity has (as this is not a completely accurate figure), but factor in the amount of posts each week on various social media platforms, which celebrity has the most genuine followers and comparing each account.
Faking IT?
Katie Perry has the most Twitter followers totalling 81,422,109. Impressive right? But how may of these followers are genuine? Celebrities, marketers, the average Jo next door have all figured out ways of getting their following clan to rise. Achieving this includes using bots or just outright buying followers.
One of the ways to spot fake followers is to look for tell tale signs on an account. Is there any content, does it engage with anyone and can it be identified as a human being or company? According to Twitter Audit just 39% of Katy Perry's followers are real. Lets compare this to both Taylor Swift (number one on Instagram) and Cristiano Ronaldo (number one on Facebook) Twitter accounts.
Taylor Swifts Twitter account came up as just 33% being genuine followers and Cristiano Ronaldo as 36% all these figures being fairly close to each other.
For the purpose of fairness I also audited my own Twitter account. Baring in mind my account has nowhere near the following of my fellow celebs.
It came up as 3% fake which came as quite a surprise. I did not expect to have any come up as fake and the one which did was someone I genuinely knew, so how can we be sure that our celebs accounts are full of fakes too?
Scrolling through Ronaldo's followers a common theme developed. Many of his followers seemed to have no profile picture, not much info on their own accounts or engagement. This therefore, does suggest a lot of faked accounts are in play. Moving on, we take the audits with a pinch of salt and not treat the figures as 100% accurate.
Account Activity
Lets take a look at some of Katy Perry's other platforms.
On Instagram Perry racks up an impressive number of followers at 38.6million, only following 231 people herself. With some calculations (this took me a lot longer to work out than someone who actually understands the numerical world of that horrible word, math), I came up with some interesting figures on Perry's Instagram and Twitter account.
Perry's Twitter account was first set up in approximately February 2009, since then she has posted 974 Tweets per year, this works out at 81 per month and 20 per week. A satisfying number that considerably beats both Cristiano Ronaldo (2010) with his 8 per week and Taylor Swift (who has had her Twitter account since 2008) with her 10 per week.
Perry's Instagram account, established in approximately May 2013, suggests 161 posts per year being about 3 posts per week. What is interesting about this fact is Taylor Swift, the most followed on Instagram, is only slightly in front of Perry with 5 posts per week. Yet, Cristiano Ronaldo is hitting up the Instagram posts with almost 6 posts per week. So Perry may be on her game with her Twitter account, but maybe could pay some more attention to her other platforms too.
Lastly, Perry's Facebook page boasts nearly 72 million likes rising from 0.2% last week. Taylor Swift has 74 million following closely to Ronaldo, who has gathered a more than respectable 103 million likes.
Social Media Mastered
So, if anything, what have we learnt about these facts and figures? We certainly know Perry and Swift are more than capable to hold 3 successful social media accounts, with regular posts, tweets and rises. Ronaldo may have some questionable followers, however manages to stay at the top of the social media tree, it seems that each of the celebrities have mastered their own niche and have successfully honed their accounts.
The accounts of both Katy Perry and Taylor Swift seem to portray themselves as just like the rest of us. Forget the money, glamour, diamonds, stages etc, by just looking through some of their photos such as dinner snaps, the selfies and day trips, these celebs almost depict an average life, one in which the viewer can identify with, which obviously helps with sales and figures of their albums.
Compared to Ronaldo however, photos can mostly be seen on his accounts of his footballing career, photo shoots and team photos. Of course, there are the odd selfies, but we are all human after all.
A Common Theme
If you look closely though, these three giants of the social media world all have one thing in common, they know how to play the game. They know that by adding followers en mass you give the impression that you are at the top of your game. And that will bring more genuine followers who will engage. Through in some natural looking pics and (maybe) pay for mass amounts of retweets (while that is a common practice I haven't verified whether these three individuals do it and you have the perfect recipe for social media success.
And let's not forget. There's also a bit of vanity here. Ronaldo probably had one eye firmly on Messi and wants to ensure that his numbers stay ahead even if his tally for Ballon d'ors doesn't. And for the girls, there's always the likes of Rihanna, Bieber and gaga to contend with.
One Rule for Them…
Lastly, just in case you didn't realise, don't think for a second that it's just soccer stars and pop stars who manipulate the system to kick the proverbial social ass. From TV stars to the world's most famous politicians (no names mentioned), they're all at it. Ironic that twitter doesn't seem to have noticed though. Seeing as their rules explicitly forbid this kind of behaviour and numerous individuals claim to have had accounts removed for engaging in these practices. Yet the rich and famous seem to be able to carry on without a worry. After all, are twitter really going to take the matter up with Katy Perry?
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Job agency fined for religious discrimination
Peter Vinthagen Simpson · 8 Feb 2010, 13:52
Published: 08 Feb 2010 13:52 GMT+01:00
- Reinfeldt: no burqa ban in Sweden (28 Jan 10)
- Equality Ombudsman sued for discrimination (20 Jan 10)
- Swedish university guilty of gender discrimination (21 Dec 09)
According to a ruling in the Stockholm District Court on Monday, the agency has been ordered to pay 60,000 kronor ($8,000) to the man after it withdrew him from a labour market training scheme when he refused to shake the hand of a female CEO at a firm with which he was seeking work experience.
The court ruled that the agency was aware that the man's religious convictions prevented him from shaking hands with women he did not know.
The company elected not to offer the man an internship, later arguing that the decision was taken due to his lack of appropriate skills and experience.
The man lost his job seeker's allowance in May 2006 as a result of his withdrawal from the programme, and he reported the matter to the Discrimination Ombudsman (DO).
DO found in favour of the man's assertion that he had been discriminated against and applied to the court to order the agency to pay 120,000 kronor in compensation.
According to Swedish law, after probable cause has been established for a case of discrimination, the onus is on the opponent to prove that it did not take place.
The court ruled that the agency had not been able to prove that the decision to withdraw him from the programme was not connected to his religion and thus ordered the payment of 60,000 kronor in compensation for violation of religious freedom.
The court conceded that although the violation appeared to have occurred unintentionally, the man had as a result suffered significant consequences and financial loss.


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- Anonym
- Finland, Par, 25-30
- Nybörjarnomad
- 1 Recension
Really nice hostel. The location is perfect, it's easy to go and bicycle to the city or to the nearby park. We were happy that the sheet was provided and breakfast was served, as well! The only thing I would like to change is to have more toilets/showers - it was a bit hard to get to the (only) toilet in the morning when many people were showering.
Many thanks for taking time to leave a lovely review! We are also happy with our location right across Hyde Park, perfect for cycling and nice strolls. The majority of our rooms are en suite and we also have bathroom on all floors across the building, but our maintenance team will look into the matter to avoid issues during morning rush. Safe travels, Astor Queensway team.
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The PokéCommunity Forums
Community Square
Main Street: Meet & Greet!
> new to community
View Full Version :
new to community
September 22nd, 2005, 12:30 PM
just saying hi to all the fans!
John Denver
September 22nd, 2005, 12:32 PM
Hello, this is your first post here huh? Well welcome to PC, enjoy...feel free to PM a staffer if you have a question
September 22nd, 2005, 12:33 PM
Hi!!!!!, I'm hyper but welcome, don't forget to post, read the rules and have fun :D SWOSHHH!!!!
September 22nd, 2005, 12:36 PM
Welcome to PC. Have fun, read the rules, stay cool.
September 22nd, 2005, 3:02 PM
Yo! I am Starchild. You may call me Lexa if you wish. PM me if you need anything, want to be friends, or to be part of my family. ^^V
September 22nd, 2005, 4:01 PM
Preevyet, there! Hope to see you around this joint some more!
September 22nd, 2005, 4:56 PM
Bonjour! Welcome to the PC! Be sure to read AND follow the rules or we'll have some problems. Hopefully that won't be the case. ;) Have fun!
September 22nd, 2005, 7:49 PM
welcome to teh pokemon community :D
September 23rd, 2005, 3:02 AM
New members are always welcome in PC ^^ So welcome and have fun here, and post a lot...and folow the rules! XD
If you need anything PM me and I always love to make new friends. =)
Cybex Mewtwo
September 23rd, 2005, 3:38 AM
Welcome to PC and have a lot of fun. And do not hesitate to PM me or anyone if you wish to be friends.
Sailor Moon
September 23rd, 2005, 12:55 PM
Hi-Hi! I am Sailor Moon, but I prefer Usako. ^_~ Pm Me if you need anything, or wanna be friends or family! :p...
September 23rd, 2005, 2:57 PM
Hey welcome to PC. The land of ...pokemon..XD


text:
Zenit St. Petersburg striker Hulk might be on his way out of the club within a year having had a massive falling out with coach Luciano Spalletti after being substituted in the recent Champions League fixture against AC Milan.
As per the Guardian, Hulk has said he could possibly be forced into leaving the club he only joined this summer.
If the situation with the coach does not resolve itself I may leave the club in the January transfer window.
With the Russian side having spent almost £40 million on the Brazilian striker just three months ago, it might seem that his chances of finding another club on a permanent basis would be somewhat restricted at present.
Zenit would almost certainly have to take a hit if they were to let him leave, with a loan option the only other alternative. Hulk has said his differences have been resolved since the incident, but it remains to be seen how long the latest reconciliation lasts.
Despite Hulk's words, it would have to be next summer if he were to leave Zenit as he is unable to register for another new club in January.
Here are four sides he could join if tensions between player and club persist.


text:
Murphy done in LF
According to David Lennon:
Jerry Manuel just said that Murphy will start the next four games at first base and is essentially done as a leftfielder. Manuel declared the experiment over.
Wow, did not see that coming. Now the call up of Pagan makes more sense.
So is Sheff getting most of the play time then? makes sense in absence of Catlos….
so..its 1b or the bench….great..no pressure


text:
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Patent application title: Assessment Generation Using the Semantic Web
James Alexander Levy (San Diego, CA, US)
IPC8 Class: AG06F1730FI
USPC Class:
Class name: Data processing: database and file management or data structures data integrity policy, rule-based management of records, files, and documents
Publication date: 2010-04-15
Patent application number: 20100094814
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This invention is a method of generating an assessment items. It comprises
configuring an assessment generation engine running on a database or an
information network such as the Web to produce when a topic is specified
a) an incomplete statement or a question about said topic, which are
called the semantic context; and b) a right answer and at least one wrong
answer, each answer in a form suitable to complete the incomplete
statement or to answer the question; The method also comprises operating
the assessment generation engine to produce an assessment item comprising
the semantic context, the true answer and the wrong answers. The method
yet comprises producing the assessment item in a material form configured
to be administered to an assessment taker.
1. A method of generating at least one assessment item comprising:a)
configuring an assessment generation engine running on a database or an
information network to produce when a topic is specified:i) an incomplete
statement or a question about said topic, said incomplete statement or
said question being called semantic context;ii) a right answer in a form
suitable to complete said incomplete statement or to answer said
question; andiii) at least one wrong answer in a form suitable to
complete said incomplete statement or to answer said question;b)
operating said assessment generation engine to produce at least one said
assessment item, each comprising its own said semantic context, its own
said right answer and its own said at least one wrong answer; andc)
producing said assessment item in a material form configured to be
administered to an assessment taker.
2. The method of generating said at least one assessment item of claim 1 wherein said at least one assessment item is in the form of a multiple choice answer quiz.
3. The method of generating said assessment item of claim 1 wherein said database or network is the Web.
4. The method of generating said assessment item of claim 1 also comprising administering said at least one assessment items to said assessment takers, said assessment takers being given the task of choosing between said true answer and said wrong answer.
5. The method of generating said at least one assessment item of claim 1 wherein for each said assessment item, said generating said at least one wrong answer is performed by parallel instantiation in a semantic network characterized by types and attributes, said parallel instantiation comprising:a) locating said right answer as an instance in said semantic network;b) finding said types and said attributes associated with said right answer;c) finding other instances associated with said types.
6. The method of generating said at least one assessment item of claim 1 wherein, for each said assessment item, said generating said at least one wrong answer per said assessment item is performed by alteration of a resource description framework, each said resource description framework comprising three elements, said alteration of a resource description framework comprising:a) locating a resource description framework wherein a first of its said three elements matches said topic, and a second of its said three elements matches said right answer;b) locating a second resource description framework wherein first of its said three elements is correspondingly identical to said first element of said first resource description framework, and wherein third of its said three elements is different from said third element of said first resource description framework;c) assigning the second element of said second resource description framework as one of said at least one wrong answer.
7. The method of generating said at least one assessment item of claim 1 wherein, for each said assessment item, said generating said at least one wrong answer per said assessment item is performed by using latent semantic analysis.
8. The method of generating said at least one assessment item of claim 1 wherein, for each said assessment item, said generating said at least one wrong answer per said assessment item is performed by using fuzzy logic.
9. The method of generating said at least one assessment item of claim 1 for each said assessment item also comprising, editing said assessment item by a human being.
10. A method of generating said at least one assessment item of claim 9, for each said assessment item also comprising:a) said human being generating ontological information;b) said ontological information being fed back into said database or information network.
11. The method of generating said at least one assessment item of claim 1 wherein, for each said assessment item, the operations performed by said query engine comprises:a) selecting a list of URLs associated with said topic;b) retrieving from the Web raw data associated with said list of URL's;c) performing a semantic analysis on said raw data, said semantic analysis generating said right answer and said semantic context.
12. A method of generating said at least one assessment item as in claim 11 wherein, for each said assessment item, said URLs are generated by inputting said topic into a Web search engine.
13. A method of generating said at least one assessment item as in claim 11 wherein, for each said assessment item, said URLs are generated by inputting said topic into a social bookmark service.
14. A method of generating said at least one assessment item as in claim 11 wherein, for each said assessment item, said URLs are generated from links found on semantic tags on the Web.
15. A method of generating said at least one assessment item as in claim 11 wherein, for each said assessment item, said URLs are generated from a predetermined table.
16. A method of generating said at least one assessment item as in claim 1 wherein each assessment item has associated with it a degree of difficulty and each assessment taker has associated with him or her, a degree of proficiency, and said method also comprising estimating said difficulties and said proficiencies.
17. The method of generating said at least one assessment item of claim 16 also comprising generating a score for each said assessment item taken by each assessment taker, said score being based on the performance of said assessment taker in taking said assessment item.
18. A method of generating said at least one assessment item of claim 17 wherein said score is based in part on the time taken by said assessment taker to answer assessment.
19. A method for assessing the degree of knowledge of an assessment taker comprising:a) configuring a database or network to produce, when a query is issued to it, a right answer and at least one wrong answer;b) formulating said query;c) entering said query into said database or network;d) retrieving said right answer from said database or network;e) retrieving said at least one wrong answer from said database or network;f) generating an assessment item comprising said query, said right answer and said at least one wrong answer;g) producing said assessment item in a material form configured to be administered to an assessment taker; andh) administering said assessment item to said assessment taker.
20. A method for assessing the degree of knowledge of an assessment taker comprising:a) configuring an assessment generation engine running on a database or an information network to produce when a topic is specified, an assessment item comprising:i) a true statement about said topic; andii) at least one false statement about said topic;b) generating said assessment item comprising said true statement and said at least one false statement; andc) administering said assessment item to said assessment taker.
[0001]This invention claims the benefit of U.S. Provisional Application
No. 61/104,896 with the title, "Quiz Generation Using the Semantic Web"
filed on Oct. 13, 2008 and which is hereby incorporated by reference.
Applicant claims priority pursuant to 35 U.S.C. Par 119(e)(i). The
present invention relates to computers and the Internet. It also relates
to computerized education and computerized assessment. Specifically, it
relates to generation of assessment questions using information from the
Web, administration of these questions to assessment takers and
assessment of their proficiency.
[0002]Material incorporated by reference include: [0003]a) U.S. Pat. No. 7,197,459 by Harinarayan titled "Hybrid machine/human computing arrangement." [0004]b) U.S. Pat. No. 4,839,853 by Scott Deerwester et al. titled Computer information retrieval using latent semantic structure [0005]c) Matthew Brand (2006). "Fast Low-Rank Modifications of the Thin Singular Value Decomposition". Linear Algebra and Its Applications 415: 20-30. doi:10.1016/j.laa.2005.07.021. [0006]d) Thomas Landauer, P. W. Foltz, & D. Laham (1998). "Introduction to Latent Semantic Analysis" (PDF). Discourse Processes 25: 259-284. [0007]e) S. Deerwester, Susan Dumais, G. W. Furnas, T. K. Landauer, R. Harshman (1990). "Indexing by Latent Semantic Analysis" (PDF). Journal of the American Society for Information Science 41 (6): 391-407. doi:10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9. [0008]f) Michael Berry, S.T. Dumais, G. W. O'Brien (1995). Using Linear Algebra for Intelligent Information Retrieval.
[0009]To effectively assess or certify proficiency of a person in a certain knowledge domain, a method of measuring this proficiency must be specified. When new knowledge becomes accessible, this method must be capable of incorporating this new knowledge. In relatively static areas, this isn't a problem. But the most quickly evolving areas of science and technology defy the pace at which standardized assessments and certifications can be made. The great importance of assessing these proficiencies in quickly evolving areas therefore necessitates a technology that can be more efficient in accessing fresh information related to a given topic, formatting this information in the form of assessments, administering these assessment s to assessment takers, and evaluating both the proficiencies of assessment takers and the difficulty of assessment material.
[0010]The Web is an ever changing gigantic repository of knowledge--much of it at the forefront of scientific and technological discourse. This material can be mined to generate such assessment.
[0011]This assessment generation technology would find application in the field of human resources for the assessment of prospective employees. It may also be of significant use in the field of education, where assessments would be generated for the assessment and teaching of students, as well as for the maintenance of professional competency in fields such as, but not limited to, law, medicine or engineering. The ability of this technology to generate news breaking material may benefit news media, social media, and political culture. Other applications of such technology may be found in advertising, public relations, and entertainment. The above examples are meant to indicate some uses of such assessments but should not be construed as limitations in the diversity of application of these assessments.
[0012]FIG. 1 is a functional block diagram of the invention. It shows selecting a topic to be used to generate a list of URLs; URLs yielding raw data that can be semantically analyzed to produce the semantic entities can be used to generate right and wrong answers; constructing assessment items and editing by humans of these assessment items; and the estimation of the difficulties of the assessment items and of the proficiencies of the assessment takers.
[0013]FIG. 2 shows how The Wrong Answer Engine (WAE) (Wrong but Plausible Answer Engine) operates. Given an input topic, the types associated with the input topic can be used to identify different instances of the input topic.
[0014]FIG. 3 shows how the task of estimating the difficulties of the assessment items and the proficiencies of the assessment takers can be framed as a neural network problem.
[0015]FIG. 4 illustrates how a simplified Kalman Filter can be used to estimate the difficulties of the assessment items and the proficiencies of the assessment takers.
[0016]FIG. 5 shows how expected scores can be calculated from estimated difficulties and estimated proficiencies.
[0017]This invention is a method of generating one or more assessment items. It comprises: [0018]a) configuring an assessment generation engine running on a database or an information network such as the Web to produce when a topic is specified: [0019]i) an incomplete statement or a question about the topic. This incomplete statement or question is called the semantic context; and [0020]ii) a right answer and at least one wrong answer, each answer in a form suitable to complete the incomplete statement or to answer the question; [0021]b) operating the assessment generation engine to produce an assessment item comprising the semantic context, the true answer and the wrong answers; and [0022]c) producing the assessment item in a material form configured to be administered to an assessment taker.
[0023]Wrong answers can be generated in a semantic network by parallel instantiation which comprises: [0024]a) Locating the right answer in a graph (semantic network) where each topic has one of more "type" attributes, and may additionally have other attributes; [0025]b) Finding the type attributes and other available attributes associated with the right answer [0026]c) Finding other instances associated with these types and attributes.
[0027]Wrong answers can also be generated by using Resource Description Frameworks (essentially a database of triplets, each triplet consisting of a subject, a predicate and an object). This method comprises: [0028]a) locating a Resource Description Framework in which one of its three elements matches the topic, and a second of its three elements matches the right answer; [0029]b) locating a second resource description framework in which the first of its elements is identical to the corresponding first element in the first resource description framework, in which the third element is different from the third element in the first resource description framework; [0030]c) assigning the second element of the second resource description framework as a wrong answer.
[0031]The plausibility of a wrong answer can be evaluated by using fuzzy logic. Given a set of fuzzy attributes defining a right answer, the fuzzy plausibility of a wrong answer can be calculated by calculating how far the wrong answer attributes are from the right answer.
[0032]Latent semantic analysis can also be used to calculate the plausibility of a wrong answer by providing a means for calculating distance between topics.
[0033]This method of generating assessments can be monitored and edited by a human being. In addition, the human being can improve the semantic database used by the assessment program by adding ontological information.
[0034]When the assessment program makes use of a query engine to mine the Web for information, the following process can be employed: [0035]a) selecting a list of URLs associated with the topic of interest; [0036]b) retrieving from the Web raw data associated with the list of URL's; [0037]c) performing a semantic analysis on the raw data, to generate a right answer and a semantic context.The URLs can be generated by inputting the topic into a Web search engine, or into a social bookmark service. URL's can also be generated by finding them on semantic tags on the Web, in a predetermined table, in Outline Processor Markup Language (OPML) files and in Really Simple Syndicated (RSS) feeds.
[0038]Assessment takers can be rated by associating with each assessment item a degree of difficulty and with each assessment taker, a degree of proficiency. An estimation algorithm can then use the degrees of difficulty and the degrees of proficiency as state vectors to evaluate assessment takers.
[0039]Variations in the assessment format include generating a correct statement and at least one incorrect statement about a topic and presenting the assessment item as a true/false quiz (if only one incorrect statement is available) or as a multiple choice quiz (if multiple incorrect statements are available). Assessment items can have many forms such as, but not limited to, true/false, multiple choice and fill-in-the-blank. Assessment takers are evaluated by administering assessment items to them.
[0040]This invention is a method that generates assessments using information available on the Web. As shown in FIG. 1 this method essentially comprises the following:
[0041]Topic Selection 1
[0042]A topic 29 is specified and selected by a human being using a graphical user interface, or as the result of an automatic computerized process.
[0043]URLs Generation 2
[0044]A list of URLs 30 containing content related to topic 29 is generated by searching for pages identified with the topic--this can be accomplished in many possible ways: [0045]a) Maintain a database of URLs associated with each topic and use this database to return a list of URLs when queried with a given topic. [0046]b) Use a third-party database of URLs associated with each topic, such as Wikipedia pages. [0047]c) Use a search engine to return a list of URLs for a given topic. For example, Google® and Twitter® have a search API. Given a topic as an input search term, this API returns a list of ranked pages mentioning this topic. [0048]d) Use a social bookmarking service such as Delicious®. These services employ social tagging, classification and indexing, to collaboratively create and share Web entities annotated and categorized by content. Delicious® provides an API that, when queried with a particular topic, returns a list of URLs. [0049]e) Use tools available on the Semantic Web. For example, searches could be conducted to look for machine readable metadata corresponding to the selected topic. For example, the Dublin Core metadata standard could be used as metadata search targets. A simple Dublin Core for example, consists of the following 15 metadata elements: (Title; Creator; Subject; Description; Publisher; Contributor; Date; Type; Format; Identifier; Source; Language; Relation; Coverage; Rights.) A more specific example would be to search for URLs containing metadata in the form of a Dublin core in which the desired topic is encoded in the subject field. [0050]f) Alternatively the URLs could be specified directly without the requirement for a topic.
[0051]Retrieval of Assessment Raw data 3
[0052]The next step is to download or otherwise retrieve the information associated with the specified URL's. This information called the assessment Raw Data 36 can comprise text, images, sounds or video. Preferably, it is semantically tagged to facilitate its analysis.
[0053]Semantic Analysis 4
[0054]Each URL 30 is sent to a semantic analysis tool. For example, given a webpage as an argument, this tool identifies unique Semantic Entities 35 and their associated Semantic Context 31. Semantic Entities 35 may be anything with semantic value, for example words, expressions, images, video clips or audio clips. The Semantic Context 31 is the paragraph or frame in which the Semantic Entity 35 is embedded but does not include the Semantic Entity 35. Thus the Semantic Context 31 is an incomplete paragraph or an incomplete expression with the Semantic Entity 35 blanked out. Using the Semantic Entity 35 to fill the blank in the Semantic Context 31 is, therefore, the right response in a fill-in-the-blank test and the Semantic Entity 35 is also the Right Answer 32.
[0055]Therefore, the Semantic Context 31 forms the basis of an assessment question. The Semantic Entity 35, itself, is assigned as a Right Answer 32. As shall be explained below, the Wrong Answer Engine finds a different but contextual plausible word or expression that can replace the Semantic Entity 35 in its Semantic Context 31. A true statement is defined as the Semantic Context 31 with the original and appropriate embedded Semantic Entity 35. A false statement is defined as a Semantic Context 31 in which the Semantic Entity 35 has been replaced with a different but contextually plausible word or expression.
[0056]Parsing for Semantic Tags 37
[0057]The mechanism for performing the semantic analysis could make use of a formal linguistic parsing program. Alternatively, if the Web content is already tagged, the semantic analysis task can be greatly simplified. The semantic analysis could be accomplished by simply parsing for Semantic Tags 37. An HTML search/parsing tool on the server can then be used to locate Semantic Entities 35 with corresponding Semantic Tags 37 within the Raw Data 36.
[0058]Common Tag is one of many currently popular semantic tagging standard of interest and is discussed here as an example. Its structural model is very simple. It states that a piece of content addressable through a URL (a "resource") can be "tagged" with one or more tag structures. Each tag can contain a pointer to another resource that identifies the concepts described by the content, unambiguously indicating what the content "means." Optionally, the tag may also contain information about when the tag was created (the "tagging date") and what human readable "label" should be used when listing the concepts covered in the content.
[0059]If the semantic analysis tool is operated by a third-party service (such as OpenCalais or Zemanta), the results of the semantic analysis can be formatted as a machine-readable document, which can be downloaded by the assessment generation program.
[0060]Object-Oriented Topic Graph
[0061]The data schema used to organize correct answers as well as incorrect answers (to be discussed below) is an object-oriented graph of topics. Each topic is an object with attributes, and attribute values. Each topic may be assigned "type" attributes describing sets to which the topic belongs. Each topic may also have additional attributes describing values of the topic that are more specific than a `type` attribute. For instance, an Apple is a type of Fruit with attributes of color, taste, and shape that distinguish it from other topics that are types of Fruit. Not all topics must share all attributes, but topics do share many attributes, and similar topics tend to share more attributes. Each topic has attribute values for each attribute key. Many attribute values are IDs of other topics. For instance, if one topic represents a historical person, the value of their `Parent` attribute would be an ID for the topic representing that historical person's father or mother. Attributes can also have types not representing other topics, such as numerical, Boolean, or text, or binary representing some other form of content such as audio or video.
[0062]Supplemental attribute key:value pairs, visualization objects, or content objects from a distributed world-wide set of datasets can be retrieved by linking them to the given topics in question through joining datasets through use of a shared key, such as a Common Tag or other resource address.
[0063]Wrong Answer Engine 5
[0064]The next step of the process is to use the Semantic Entity 35, the Semantic Tags 37 and Semantic Context 31 to generate one or several Wrong Answers 33. This operation is performed by the Wrong Answer Engine (WAE). This module generates plausible but Wrong Answers 33. It takes a Semantic Entity 35 as an input and returns a list of topics that bear a certain degree of similarity with it. Several approaches can be used. [0065]a) Wrong Answer by Parallel Instantiation; [0066]b) Wrong Answers by Alteration of Resource Description Frameworks; [0067]c) Wrong Answer Plausibility Determination Using Latent Semantic Analysis; [0068]d) Wrong Answers Using Fuzzy Logic.
[0069]a) Wrong Answer by Parallel Instantiation
[0070]This module makes use of a semantic network such as the Freebase® database. The difference between two concepts embodied, for example, by a Right Answer 32 and a Wrong Answer 33, can be expressed in the semantic schema. More specifically, one possible way of generating Wrong Answers 33 is as follows: [0071]a) start by expressing a Right Answer 33 as a particular instance in the semantic schema; [0072]b) go up one or more one level in the semantic tree to the parent type; [0073]c) select a different instance from the original one; and [0074]d) go down one level or more level to that new instance.
[0075]A simple example illustrating the parallel instantiation process is shown in FIG. 2: given the input topic 20 (i.e., Right Answer 32) all of the types 21 associated with the input topic 20 are returned. This can be implemented with the Freebase® service by issuing the following MQL command:
TABLE-US-00001 [{ "name" : {{Right_Answer}}, "type" : [ ], }]
[0076]Upon receiving a list of types "ty" associated with the Right Answer, a second query is issued for each type "ty" to produce an instance of each type. This query generates a list of topics 22.
TABLE-US-00002 [{ "name" : null, "type" : "{{Right_Answer_Types[ty] }}", "id" : null }]
The retrieved topics 22 share inherited types with input topic 20. Clearly, other methods of exploiting a semantic schema to generate plausible but Wrong Answers 33 also exist.
[0077]Alternatively, the degree of similarity between topics can be based on inferences from other sources, such as aggregated user-generated data from search engines or social networks. For example, the returned topics can be those found to be frequently coupled with the input topic in search requests, indicating similarity.
[0078]b) Wrong Answers by Alteration of Resource Description Frameworks.
[0079]Another possible implementation of The Wrong Answer Engine 5 can involve the use of Resource Description Framework (RDF) entities. Each RDF entity contains a subject, a predicate and an object: [0080]RDF=(Subject, Predicate, Object)
[0081]The Wrong Answer Engine 5 uses a database of RDF relations. Let's say that this RDF database contains one RDF entity such that the subject field corresponds to the input topic and the Object field corresponds to the Right Answer 32. [0082]RDF1=(Input Topic, Predicate1, Right Answer)
[0083]One may generate a set of wrong (but related) answers by looking for object fields in RDFs which contain the same subject field but a different predicate. [0084]RDF2=(Input Topic, Predicate2, Wrong Answer)
[0085]For example considers the following relations describing Shamu the whale: [0086]RDF1a=Shamu Is a Mammal [0087]RDF1b=Shamu Swims-Like a Fish
[0088]Using the incorrect predicate would result in the incorrect statement: [0089]RDF2="Shamu Is a Fish."
[0090]Wrong Answer Plausibility Determination Using Latent Semantic Analysis
[0091]In this approach the distance between topics is measured in a Euclidean latent space. The object-oriented topic graph can be represented as an adjacency matrix or other form of latent space. Eigendecomposition of this adjacency matrix (or the scalar product when the topic graph is represented in a latent space) provides components representing an estimated plausibility rank, where wrong answers 33 are ranked according to how closely their plausibility as a correct answer is to an optimal difficulty level for the given quiz item.
[0092]Each of these distances represents an estimation of the given wrong answer's plausibility, or believability to the end-user, in place of the given correct answer. The results of these estimations can be incorporated as feedback to assign weights to attributes representing the attribute's reliability in determining a plausibility score. More information about Latent Semantic Analysis can be found in the following documents: [0093]a) U.S. Pat. No. 4,839,853 by Scott Deerwester et al. titled Computer information retrieval using latent semantic structure [0094]b) Matthew Brand (2006). "Fast Low-Rank Modifications of the Thin Singular Value Decomposition". Linear Algebra and Its Applications 415: 20-30. doi:10.1016/j.laa.2005.07.021. [0095]c) Thomas Landauer, P. W. Foltz, & D. Laham (1998). "Introduction to Latent Semantic Analysis" (PDF). Discourse Processes 25: 259-284. [0096]d) S. Deerwester, Susan Dumais, G. W. Furnas, T. K. Landauer, R. Harshman (1990). "Indexing by Latent Semantic Analysis" (PDF). Journal of the American Society for Information Science 41 (6): 391-407. doi:10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9. [0097]e) Michael Berry, S.T. Dumais, G. W. O'Brien (1995). Using Linear Algebra for Intelligent Information Retrieval.
[0098]d) Use of Fuzzy Logic to determine Plausibility of Wrong Answers. Yet another method of generating wrong answers is to use a database in which topics are described in terms of a function of its fuzzy logic attributes. The truthfulness (or plausibility) of the answer could then be described of its fuzzy logic value--for example continuously ranging from 0.0 for completely wrong to 1.0 for completely true). A true answer would then be arbitrarily defined as any topic whose truthfulness falls, for example, between 0.9 and 1. A plausible but wrong answer would then be defined as any topic falling in the range, for example, between 0.7 and 0.9. Thus to generate such a wrong answer one would perform the following: [0099]a) Extract form the context (the question or incomplete statement) a set of fuzzy attributes that would define a right answer. This may be done for example if the test is annotated with XML tags to specify these attributes in advance. Otherwise, more elaborate artificial intelligence techniques may be used. [0100]b) Perform a variation analysis on the database: starting with the fuzzy attributes, generate variations in those attributes and search the database for topics having these attributes that make the topic fall within the desired range of plausibility.The output of the search would then consist of topics representing wrong answers with the desired plausibility.
[0101]Difficulty Level Customization. Wrong-answers 33 are selected based on how close their plausibility score is to an optimal difficulty level for the given quiz item. To personalize quiz items for each quiz taker, the Wrong Answer Engine 5 may specify a custom optimal difficulty level to create a custom set of plausibility ranks and therefore may select a custom selection of wrong answers 33.
[0102]Assessment Generator 6
[0103]The next step is to generate an assessment item 38. The assessment item is comprised of the following: [0104]a) an assessment statement. Essentially this statement comprises the assessment context (for example a paragraph or a sentence) left ambiguous by blanking out the embedded Semantic Entity 35; [0105]b) the Right Answer 32 which can be the Semantic Entity 35; [0106]c) the set of Wrong Answers 33.
[0107]Essentially the task is similar to generating a multiple choice quiz. The output of the assessment generator is an Assessment Item 38. For example an assessment could be of the form: [0108]"Fortran is a general-purpose, ______, imperative programming language that is especially suited to numeric computation and scientific computing."
[0109]Replace blank by the most appropriate term below:
[0110]i) Procedural
[0111]ii) object oriented
[0112]iii) parallel
[0113]iv) English
The Right Answer 32 is "procedural." All others are wrong but plausible answers.
[0114]It is clear to persons having skill in the art that many variations can be employed on this basic scheme to generate many kinds of quiz items, such as but not limited to [0115]a) true/false; [0116]b) multiple choice; [0117]c) fill-in-the-blank that requires an assessment taker to enter his own answer without providing him with multiple choice. (For example: "What is the composition of water? Hydrogen and ______? Clearly the only answer is Oxygen and any other answer would be wrong. In this case the assessment taker would not be provided with a list of terms to pick from such as "Oxygen, Nitrogen, Carbon, Air, Water Vapor." [0118]d) numerical. The assessment taker would be required to enter the correct numerical value to complete an assessment statement, for example, "The value of PI to three significant figures is ______." The answer of course is 3.14.
[0119]If a difficulty ranking is available at this point, the assessment items could be ranked according to their difficulty. Otherwise, as shall be described below, an algorithm can be employed to estimate the difficulties of assessment items and the proficiencies of assessment takers using measured score data.
[0120]In addition, the type of questions does not have to be limited to text but could include images, audio and video. The questions format can be decided by the software on the basis of Semantic Tags 37 associated with the Raw Data 36.
[0121]Editing 7
[0122]As an option, the Assessment Item 38 can be given to a human moderator for editing. U.S. Pat. No. 7,197,459 by Harinarayan and entitled "Hybrid machine/human computing arrangement" which discusses how humans can assist a computer to solve particular tasks, is hereby incorporated by reference as already mentioned in the first paragraph. An editing interface can be provided to allow a human to select and edit the Assessment Items and rank them by degrees of difficulty. For example, the human assistant who has access to the Right Answer 32 could edit the Semantic Context 31. For instance, he could remove from the Semantic Context 31 "giveaway" references to the Right Answer, and adjust the boundaries of where the item text begins and ends. The human assistant could also select the most plausible but wrong choices among the Wrong Answers 33, and adjust the estimated degree of difficulty of the assessment item. After editing, the Assessment Items 38 are called Edited Assessment Items 39.
[0123]Each Edited Assessment Item 39 is saved to the database with a link back to its originating URL, which in turn links back to its parent topic. This metadata can later be used in the Ranking process.
[0124]Production of the Assessment
[0125]The edited assessment 39 is produced in a material form that can be read or otherwise understood by a human being, for example in print, on an electronic display, or aurally. The assessment presentation requires the assessment taker to make a choice between the Right Answer and the Wrong Answers 33.
[0126]Assessment Administration 8
[0127]Assessment Takers use an Assessment Taker interface to take assessments which comprise Assessment Items. There are many possible ways of presenting an assessment item. For example, the content of each item resembles a fill-in-the-blank, where the Right Answer 32 is must be chosen from among many Wrong Answers 33. In addition, there may also be a timer measuring the time it takes to answer the question, and/or giving the Assessment Taker limited time to answer the question.
[0128]Rendering Quiz Items Using Supplemental Content and Visualized Answer Representations: Supplemental data and content such as images or video can be linked to the given correct answer topics and wrong answer topics through aforementioned disambiguation methods to join sets of linked data. This supplemental data and content can be used to render the quiz answers to the interface in richer forms, such as rendering answers as data visualizations if a rich dataset can be linked to the given wrong answer, or image or video content that can be linked to the given wrong answer.
[0129]Estimation Algorithm 9
[0130]After the administration of the assessment test, the answers generated by the assessment takers as well as their response times are recorded. The data generated by the administration of the assessment includes score data, each data score item corresponding to the performance of a single assessment taker taking a single assessment item. The relationship between scores 42, Assessment Items 40, Assessment Takers 41 proficiency 44 and difficulty 43 is illustrated in FIG. 3. A score 42 is defined as the performance of one assessment taker 41 on a single Assessment Item 40. The scores 50 are computed as a function of whether the answers provided by the Assessment Takers 41 are correct or not, and of the time the Assessment Takers 41 need to answer each question.
[0131]Using the aggregate score results as an input, an algorithm is used to calculate the degree of difficulty for each assessment item (or single question) and the degree of proficiency for each assessment taker. As is evident for those skilled in the art there are many methods of solving this problem, for example but not limited to, least square, gradient technique and Kalman filtering. One possible approach is to begin by defining the following two quantities: [0132]a) The degree of difficulty 43 for each assessment. [0133]b) The degree of proficiency 44 for each assessment taker.
[0134]The problem is then reduced to estimating the degree of proficiency of the assessment takers given the scores 42 data.
[0135]Least Square Estimation: A state X vector can be defined as the concatenation of the Difficulty vector and the Proficiency vector:
x=(d1, d2, d3, . . . dm, p1, p2, p3, . . . pn)
where (di, d2, d3, . . . dm) is the Difficulty vector (each vector element assigned to each assessment item and (pi, p2, p3, . . . pn) is the Proficiency vector, each vector element assigned to each assessment taker.
[0136]For a given score, s, linked to assessment item, i, and assessment taker, j, the score sk can be calculated as
where the values of di, and pi are assumed to range from 0 to 1 and the values for sk are assumed to range from -1 to +1. The measurement vector h corresponding to this score is therefore
h=(0, 0, 0, . . . -1, 0, 0, . . . +1, 0, 0, . . . 0)
where -1 corresponds to -di, and +1 corresponds to pj.
[0137]sk=h xT
[0138]For the set of all scores expressed by the vector s, let H be the measurement matrix. Since s=HxT
The least square estimate of x is therefore given by:
[0139]Neural Net Implementation: FIG. 3 illustrates how assessment items 39, assessment takers 41 scores 42, difficulties 43 and proficiencies 44 can be viewed as a neural network. In this particular case, backpropagation could be used to solve for the difficulties and proficiencies given the score values.
[0140]Kalman Filter: One could also use a Kalman Filter to estimate the difficulties of the assessment items and the proficiency of the assessment takers. FIG. 4 illustrates a simplified Kalman Filter implementation wherein, akin to a simple gradient method, the covariance matrix is restricted to diagonal values only. Whereas in this method, convergence per algorithm cycle may be slower than in a conventional Kalman Filter, the computations are significantly simpler. In the figure, the proficiency states 44 and the difficulty states 43 are shown separately. As shown in FIG. 5 each expected score can be calculated in the feedback loop 13 from the estimated values of the difficulty of the assessment item and the estimated value of the proficiency of the assessment taker. The equation below assumes that the values for Difficulty, Proficiency and Score range from 0 to 1.
Expected Score=(Proficiency-Difficulty+1)/2
[0141]The expected score is subtracted from the measured score to generate an error signal called a residual 14. Optionally, in a technique called simulated annealing, a small noise signal 15 can be added to the residual 14 to overcome the existence of local minima in the error signal.
[0142]The residual 14 is then multiplied by the gains Gp 16 and Gd 17 to produce the amount of change that should be applied to the Difficulty states and to the Proficiency states. In this simplified Kalman Filter, the gains are scalars, as in a conventional gradient method. As the number of state corrections increases, the uncertainty associated with the estimates decreases. Consequently, as more scores are processed by the algorithm, the gains can be made to decrease in order to provide a greater weight to the latest estimates in comparison with the new score measurements. One of the advantages of this method compared to the least square approach discussed above is that it can be used incrementally rather than in batch: each new score can be processed by itself to improve the accuracy of the whole database.
[0143]Development of a Standard Database 10
[0144]Once a database is developed that contains a satisfactory estimate of the difficulties of assessment items and the proficiencies of assessment takers, one may want to prevent any more changes to the difficulties 43 and the proficiencies 44 and keep the database constant. When a new set of scores linking new assessment items to new assessment takers becomes available, the assessment items' difficulties and the assessment takers' proficiencies can be calculated by running the new scores through the simplified Kalman Filter algorithm. To prevent changes to the existing database, only corrections to the difficulties of the new assessment items and the proficiencies of the new assessment takers should be allowed.
[0145]Machine Learning via Quiz Administration Feedback. The user's choice of answer when the quiz is administered can be used as feedback to determine weights for the reliability of each of the wrong answer's topics attributes or supplemental topic attributes to determine the plausibility score required to select wrong answers at the optimal difficulty level.
[0146]Writing to Semantic Network 11
[0147]Semantic databases may be enhanced through human computation, such as confirming that a topic belongs to a certain ontological category. By making these computations in the assessment editing process, it may be possible to write useful semantic information back to semantic databases such as Freebase®. For example an assessment editor may confirm that a selected Wrong Answer 33 should be co-assigned to one of the semantic types of the Right Answer with a write operation:
TABLE-US-00003 [{ "id" : {{ Wrong_Answer.id }} , "type" : {{ Right_Answer_Types[s] }} }]
[0148]Therefore, in the process of editing an assessment, a human being, can increase the knowledge of a database or information network from which the assessment is generated by 1) generating ontological information, and 2) feeding back this ontological information into the database or information network.
[0149]While the above description contains many specificities, the reader should not construe these as limitations on the scope of the invention, but merely as exemplifications of preferred embodiments thereof. Those skilled in the art will envision many other possible variations within its scope. Accordingly, the reader is requested to determine the scope of the invention by the appended claims and their legal equivalents, and not by the examples which have been given.
Patent applications by James Alexander Levy, San Diego, CA US
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- Repost from TOG: Urochordata! Urochordata! Rah! Rah! Rah!
- (For the purposes of this little essay, a tunicate refers to a member of the phylum Urochordata.) As adults, tunicates are basically a soft sack for filtering water, surrounded by the tough tunic that gives them their name.
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I went on a little Castlevania-playing spree recently, so I want to talk a little bit about one of my favorite platformer series, Castlevania. Specifically the three "main" 16-bit games, at least in terms of American release. By that I mean Super Castlevania IV, Castlevania: Dracula X, and Castlevania: Bloodlines. I had played these before, but recently I actually sat down and, more or less, played them back to back.
Super Castlevania IV was the first 16-bit game in the series (Not counting Haunted Castle, but we'll talk about that later. I think that's 16-bit), and it's technically a remake of the first Castlevania. Most people will tell you that it's the best Castlevania in the classic, linear style of Castlevanias, and they're probably right. I've never played Rondo of Blood, which a lot of people seem to love, but something tells me it gets more praise than it deserves because it's not available in America. Just a hunch.
Anyway, Super Castlevania IV's gotten its fair share of praise over the years, so there's not a lot gameplay-wise I can talk about that would be a fresh idea. Anyone looking for a good analysis of the overall experience should check out the AVGN's Castlevania series review, part 3. It pretty much covers anything I could say about the gameplay or controls.
What I want to talk about instead is the music in Castlevania IV, which was made by Masanori Oodachi and Souji Taro (I've never heard of them either). I've never found a person that doesn't think the Castlevania series as a whole doesn't have some of the best video game music ever written, and Castlevania IV is no exception. That said, it jumps a lot from the prior series' trends of classical-inspired chiptunes with bouncy bass and drum lines, instead becoming creepier and more atmospheric. The genre inspriations expand a lot, too. "The Sunken City," for example, is straight up jazz fusion. Take a listen.
Weird time signature, upright bass timbre, kind of atonal and technical sounding... sounds like jazz fusion, right? Check out that meandering flute solo at 1:17. Pure jazz. How weird is that? You're going through this Greek-ruins-y looking place covered in algae and monsters, and it's playing music like you're in an avant garde jazz club. Another somewhat jazz-like track is "Spinning Tower," which the guy who uploaded it to Youtube called "Spinning Room." I don't know which the actual name is.
It's almost got some metallic blast beats at points, but on the whole I think the song has a jazzy feel to it. It's not as pronounced as in "The Sunken City," but it's got the progressive style similar to jazz fusion. One thing I like a lot in music is when the drums can change frequently without the song sounding disjointed. "Spinning Tower" does this really well. But let's look at some of the other "genres" of music represented in Castlevania IV. How about "Rotating Room?"
What the hell was that? You have this weird atmospheric song starting, then suddenly it's like "Oh shit! The mothership is coming overhead!" I have no idea why that part is there. I just wanted to include it here for its crippling weirdness. Crippling's too strong of a word? Yeah, maybe. This is one of the weirder tracks in the game, and probably not the one you'd be most prone to listen to of your own accord. Regardless, it's a neat song that fits the atmosphere well. I also wanted to talk about "Treasury Room," because I like it.
The intro's a little boring, yeah, but once it gets going it's pretty cool. The drums are really active under a comparatively passive melody track, which makes for a neat contrast. Especially when you consider Castlevania games generally aren't the fastest paced platformers out there. It does a good job building tension as you near the end of the game.
Of course, the game's not without its share of traditional Castlevania-y tracks. Quite literally, in the case of when you get to Dracula's Castle. Three of the songs are "remixed" versions of Vampire Killer, Bloody Tears, and Beginning, from Castlevania1-3. This tradition of including these songs is continued in Dracula X and Bloodlines. Theme of Simon is a new addition in a string of these more traditional Castlevania offerings. No surprise, it's good like the rest. The one thing I don't like about it, and it's minor, is the "thin" sound to it. Consider some of the grandiosity of some of the other songs in the game, and Theme of Simon just.. seems a little thinner. This isn't a problem at all, though, because the melodies are great, and it works perfectly for first level music, especially when it comes right after the purely atmospheric Prologue track. When it comes in as Dracula enters his second form is really cool, and makes you feel so bad ass when you finally whip his head so hard that the boarded-up window smashes open and reveals the new day's sun. Check it out.
And then the credits music is good too. I actually had it stuck in my head once, and it basically made me sad for the whole day. You know how when you finish a good game, you're kind of sad to see the credits go by, because you know you'll never experience the game again like the first time you played it? Well, that's how Castlevania IV goes. It doesn't help that the song's sad, reminding you you'll never enjoy this game like you did the first time.
Just as an aside and personal musing, Resident Evil 1 and 2 are two games that I feel bad about every time I play them again. I loved them the first time, and still like them a lot on subsequent plays The with an adventure game like RE, you know exactly what to do after the first time. You never have to search around for items, you're never worried about how many monsters are in the next room, you're not surprised when things smash through windows or walls, etc. When Mr. X (Spoiler alert for the 5 people who haven't played RE2) smashes back through the wall outside the press room or whatever after you think you've given him the slip, I was like AHHHHHHHHHH. But that was just the first time. Ever since then, I always think how cool that was when I see him smash out again, but I'm not surprised. Of course, in Resident Evil 3, they continued the idea of Mr. X with Nemesis, but it's not a surprise when he follows you into another room, because he does it for like a billion years. Fucking Nemesis.
To try to rein this digression back in, the credits songs for RE1 and 2 aren't nearly as memorable as the ones in Super Castlevania IV. In both Castlevania IV and Resident Evil, they show you little clips of all the places you went in the course of the game while they're rolling the credits. The problem is, the music in the RE credits is like something that'd accompany a video welcoming you to a corporation you just got hired by. You know what I mean. Castlevania IV's credits music, on the other hand, is sad, and goes well with the theme of looking down memory lane for the last few hours. Oh, and RE2's credit music is pretty bad too. The A game credits are generic piano, and you don't even notice or care because you know you have half a game left. The B game credits are unfitting rock guitar. Not trying to take anything away from RE2, which is one of, if not the best, adventure games ever, but.. yeah. Just wanted to tie that together with an association I thought of while writing this.
Anyway, the main point here is that the music in Super Castlevania IV is a good way to cap off the best of the classic-style series. With the move to 16-bit, both the environments and the music had the technology to sound and look a lot more atmospheric and creepy. Castlevania IV did this well, and made this early SNES release one of the most memorable games on the system. Next time, I intend to talk about Castlevania: Dracula X, though I don't know if I'll focus as much on the music for that one. See ya next time.


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Saturday 3rd December & Sunday 4th December 2016
Welcome to our Victorian Fayre of 2016. On the Friday will see the Light Switch on with entertainment on stage from local schools. On the Saturday, another Victorian market, again with festive entertainment on stage, but then on the Sunday – the main event itself - The Victorian Fayre. This takes place throughout the entire town of Melton Mowbray, with streets closed to traffic, more stalls erected, and even more entertainment arrives, for what is a fantastic day out for the entire family.
We would love you to follow us on twitter........... and facebook.............also for further updates.
We'd like to say a big thank you to all the sponsers listed below who have helped us create a wonderful 2016 Victorian weekend!


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