There's no questioning the influence of social media in modern society, and our broader interactive process. But still, many businesses remain unsure whether it's right for them, whether they actually need to have a social media presence.
To be fair, it may not be for everyone - if, for example, you sell products aimed specifically at an older sector of the market, it could be that you'd be wasting your time posting to a Facebook Page that no one's likely to read. But for the vast majority of organizations, having a social media presence can, if nothing else, add a level of credibility and legitimacy, and boost exposure potential and brand awareness.


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Treasury Dept Plan To Replace Andrew Jackson With Harriet Tubman $20 Bill Sparks Controversy
The Obama administration must have a lot of time on it's hands because it's not only spending your money, it's planning on changing how it looks.
Feeling like it hasn't done enough to pander to liberals obsessed with identity politics, the Department of the Treasury has declared that it is considering making some major changes to the $10 and $20 bills. Secretary Jack Lew made an official announcement just this week, revealing what the Department plans to do to our money.
Apparently, the Treasury is getting ready to boot Andrew Jackson off the $20 bill and replace him with a women who is supposed to represent the struggle for "racial equality." Although it was widely speculated that Rosa Parks was the woman in question, the final selection of the Department was Harriet Tubman, the beloved abolitionist and folk hero.
As for the $10 bill, Alexander Hamilton will be joined by a strange, mural-like portrait of various women involved with the women's suffrage movement on the back. Prominent figures like Susan B. Anthony will reportedly be present, but considering the limited space, it will probably be hard to give each woman the distinguishing features necessary to tell them apart.
Naturally, the changes sparked a huge debate over whether they were appropriate or even necessary. While commentators on both sides generally agreed that Tubman is deserving of being commemorated on our currency, conservatives questioned both the wisdom and taste of bumping Jackson off the $20 bill to do so.
See more on the next page:
Cherie Satterfield
I demand to see Andrew Jackson
Linda Merrell
I don't give a darn who you put on our money when we are not in debt and have surplus money to spend after all homeless are fed and vets are taken care of !! No cuts to SS or military!!!!!
Carolyn Kratz
Given our propensity to give people and things nicknames, how long will it be until people start calling the $20 bill a Tubby?
Maralyn Dodge Mecham
There will be no stopping them until our America as we have known it will no longer exist.
Gaila A Pierce
Who is she???
Bill Phillips
We need to stop judging historical figures based upon current trends and societal mores. I said some things as recently as 1986 that would be appalling if they had been captured and placed on youtube. While Andrew Jackson did some bad things, he also saved this country during our second revolution, the war of 1812. Without the Battle of New Orleans, we'd be subjects of the crown, and England would have had not impetus to become the more-democratic country it is today.
Bob F Mcdonald
If her mug is on one I will never accept one again
Lana Noeth
Stop wasting our tax dollars to pander to blacks. Obama hates white Americans. Read his books…for free at the library.
Erik Heller
YES, I Could Go for That—–GOD On Our Money Makes More Sense to Me.
Good thought Bill, I know she was a good woman. But I like your suggestion, why change something that means so much to the development of our country, it has nothing to do with race.

redpajama_set_name: RedPajamaCommonCrawl


By following this simple procedure, you can make the right move and transform your factory into a smooth-running, well-oiled machine.
Do you feel a little overwhelmed with the sight before you when you walk onto the factory floor? Are you sick of all the clutter? Do you feel that the equipment could be better laid out so that product isn't travelling from one end of the factory to the other and back again? Well it probably can.
The nature of a growing business is such that with all the time pressures new equipment tends to get put in a spare corner rather than finding where the most logical place for it would be. And there it remains until years later when people get sick of the mess and inefficiency.
The trouble is when it comes time to make the big move nobody is sure where the best spot is for all the machinery and equipment. Well here it is, just follow the steps outlined in this article and you can rearrange your factory with confidence that it will operate smoothly now and into the future.
Develop a list of the various products that you are manufacturing. By product family we mean a group of products that are basically the same and are manufactured via the same processes. A family of products will have variations from product to product such as different model configurations but they generally have the same base model.
Plant layout should be driven by the requirements of current and expected future operations. To comprehensively compile all the intricacies of operations it is necessary to map each of the relevant processes. The quickest and easiest method of doing this is by flow-charting.
Process flow charts do not show physical layout or size, it is a listing of the tasks and actions required for a particular process.
Once you have an understanding of the current status of your processes from having developed the Process Flow Charts you should look to improve the processes before optimising the layout. The reason being that you don't want to invest in an optimised layout of a sub-optimal process. That is akin to automating a process that is altogether unnecessary.
We will not go into any of these in detail in this document, as there is plenty of information available on each of these as well as the many other process improvement tools in existence.
Tip: Once you have completed your process flow chart you should identify which steps in the process comprise the bottleneck. You know then, that if your objective is to increase throughput (for example) you must increase the throughput of the bottleneck.
At the completion of this stage you should revisit your Process Flow Charts and make sure they are still a true representation of the new processes.
Note: Remembering that world-class manufacturers practice continuous improvement, this stage has the potential to go on indefinitely. You should address easily exploitable weaknesses in the process and then move on, rather than getting sidetracked and spending a large amount of time at this stage.
The first step is to take the various work-centres from your Process Flow Charts and list them in the left-hand column of the Relationship Chart.
The matrix to the right of that list provides a cell for the relationship (or proximity rating) between every pairing of work-centres. Within those squares you write a rating for each of those relationships.
A proximity rating is a simple weighting that reflects the desirability of physical proximity between any two work-centres. Production entities that have complicated or high volume material flow between them acquire a high proximity rating.
The next step in the design process is to assign areas and in some cases (eg very tall equipment) volumes to each of the given entities based on current and expected needs. These are then positioned on a diagram of the building to produce a "block layout".
The block layout is really a concept level layout that assigns sufficient space for each workcentre so the operator can then work to locate them to optimise the flow of materials and address other constraints that are captured in the relationship chart. The goal is to move the various entities around until the relationship chart is best satisfied.
The best way to do this is modifying a CAD drawing of the layout. This allows simple modification, accurate measurement of distances and areas, and the ability to create several different proposed layouts.
Problems with forklift fumes in your factory?


Having just demonstrated that tradition must bow to Scripture, Jesus began to teach on the source of human defilement. Since the problem is internal, there is no external solution. Alistair Begg explains that we are all "unclean," and the only way to be made clean and right before a holy God is by trusting in Christ's righteousness.


I've written extensively about creating inner-peace in our lives using meditation. Today, I want to share with you some inner-peace techniques that you can use in your business in order to nurture and grow it. You can extrapolate these ideas for applying it to an environment where you are employed as well. And I will have more specific articles for you as well.
We all know that a business is a reflection of the people it is made of. How a business is run, how it's customers are treated and what kind of products and services it generates stems directly from the company's people, especially the leadership. This is ever more so true when you are small business owner or what is more and more common today, the solopreneur. (Solopreneur is a business owned by a single individual and perhaps a small team of freelancers).
Just like life is a mirror of your inner mentalscape, so is your business. Your mindset and attitude towards life will directly affect your income; your impact and reach; and your sense of fulfillment you gain in your business. As they say, to create abundance physically in our lives, we must start with an abundant mindset. Similarly, to create a healthy and happy business, we need to have the right attitude towards our business.
It all sounds really wonderful - change your attitude and change your life. But when the rubber hits the road, that's when we realize how difficult it is to implement this wisdom. When clients are being over-demanding, when your proposals your send out are coming back rejected, when your virtual assistant (VA) or teammate or business partner does a blunder, when your list is not growing as fast as you want or when your launch falls flat on its face, that's when emotions start flying. That's when stress and overwhelm begin. That's when feelings of, "I am not good enough" start arising. And this is when the seeds of self-doubt finds fertile ground to begin germinating.
Of course, with a daily meditation practice, you can help yourself handle business issues that arise with more calm, and more wisdom. But while you are building that habit and while the benefits of your practice accrue, it may take some time. So in the meanwhile, I've got some a simple tips for you, practical techniques that you can take up in different challenging business situations.
I'd like to share with you an example from the real world that can help you see how to use this technique of making peace when a challenging situation comes up.
Let's say that your are a web designer and you've started on a project for a client. You've sent her all the instructions on what she has to deliver back to you. Logos, about page text, example of sites she likes, features she absolutely want and all the other little details are to be filled out by her. You've blocked out two weeks to do this when you are not taking up any other client.
So this certainly a difficult situation any one could be facing in a business. You've set up processes to improve efficiency because you are a solopreneur and have hundred different hats on. But then here comes a client who derails all that by delaying on just one aspect of the whole process - the starting content you need to begin your work.
All you can do at first is make peace with the situation. The first thing to do is take some deep breaths and make peace with the situation. What this means is that you don't fight what's happening but instead you accept it. Yes, this client is not sending me the info. You loosen up your thoughts towards the client. You also accept the feelings that this is causing in you. Yes, I'm feeling stressed about this. I feel my forehead tightening and my shoulders feel restricted. Bring awareness in this way to what you feeling emotionally and how your body is feeling that emotion.
When a situation like this happens, there are a couple of players involved. There's you, clearly and then there's the object that is causing the stress or the problem. In this case it's the client and her delay. But it could be a tech failure, or low capital, or miscommunication with an assistant or a collaborator. Whatever the "other" part of the equation is, is the object.
So now what you need to do is to loosen up your mental energy towards that "other" object. In this case, you notice your thoughts towards your client - anger, impatience, frustration, even hurt and disappointment. By observing those thoughts, you begin to loosen their grip. And then you can actively send loving energy to the object, in this case the client.
The way to do this is by closing your eyes and imagining the client's situation. It may be that this client was always told by her family that she will never amount to anything and she's worked so hard to find a niche, and work nights and weekends on this side business while keeping a full-time job. Now she is finally ready to get visible and be found and hence, she's hired you to make her a new website. But her sabotaging thoughts are coming to overwhelm her and become her own obstacle to success. You recognize this as a possible cause for the delay and you remember times when you too have had to overcome your sabotaging thoughts. You realize how challenging that is and there, a light of compassion emerges within you. You send that light to your client. I understand you. I know it's hard. I believe in you. It's alright. You are good enough.
This way, you notice that you are not so upset about the situation. You have compassion for this client. Poor client, how challenging it is to get out of our own way. I understand this. I send you my good, positive thoughts. I send you peace.
Now you've truly loosened your energy towards her. You may find that as soon as you do this, you get an flurry of emails with all the information you need. Or may be that doesn't happen. But then, you've freed yourself of the negative energy this situation caused in you. And now are beginning to get proactive. You start thinking, "What can I do to help this client?" So you decide to give her a quick call to find out what's stopping her. You realize that she herself is waiting on your freelancer to deliver her the logo and she felt too embarrassed to tell you that because she wanted to deliver a perfect report back to you with all complete content.
Now that you know this, you break things down for her and you tell her the least necessary items you need in order to get going on your tasks. In this case, since you know the color palette and fonts of the logo, you can begin work even if the logo is not complete. This moves the process forward at least and breaks up the stagnant status the project was long sitting in.
You can see how you've now made peace with the situation and saved yourself from further delays.
But let's say, the client never returns any info to you and you really cannot begin on the project. In this case, you can comfortably tell the client that you will have to push back on her project at the next available slot as you cannot loose time waiting. And these two weeks that you had blocked off, you can now use for taking care of other parts of your business like organizational issues, financial updates, blog content creation, updates on your website, etc. You actually are grateful for this "extra" time to catch up on some things you had to put off because of client projects. And you can then regroup with the client when she is ready.
Making peace with what is, is a piece of wisdom that comes in really handy when you are running a business. There are other tools of inner-peace and equanimity, that you can use in your business to move forward, increase your revenue and have greater impact with your business. As they say, mindset is key for success. If you'd like to learn more practical techniques like the one I shared today, see examples of how you can use it and get live help on your own business mindset challenges, come to my free Live Masterclass. More information on how you can sign up is here: 3 Mental Peace Techniques You Can Use To Grow Your Business.
Look forward to seeing you there. 'Til then, rock on, my friend!
PS: Feel free to share in the comments one challenge you've been facing in your business which you know can be changed with some mental technique of dealing with it!


If it?s luxury that you want, The Address Dubai Marina has got it. It overlooks a calming environment of illuminated waters, bobbing with sleeping yachts while there?s also a gentle energy from nearby eateries and shops that animates the drowsy atmosphere with possibility. Near to the beach and city attractions, along with five restaurants offering various international cuisines and spa treatments within the hotel, you certainly won?t get stuck for things to do staying here. There are 200 guest rooms altogether, each of which includes a separate bath tub and shower, minibar, flat screen TV, gourmet coffee and tea making facilities, ipod docking station and pillow choices.


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Steve Johnson era il detentore del titolo da due anni, ma è stato sconfitto al secondo turno da Daniel Elahi Galán.

In finale Christian Garín ha battuto Casper Ruud con il punteggio di 7-64, 4-6, 6-3.

Teste di serie
Le prime quattro teste di serie hanno ricevuto un bye per il secondo turno.

 Steve Johnson (secondo turno)
 Jérémy Chardy (secondo turno)
 Cameron Norrie (secondo turno)
 Reilly Opelka (secondo turno)

  Taylor Fritz (primo turno)
  Mackenzie McDonald (primo turno)
  Jordan Thompson (quarti di finale)
  Sam Querrey (semifinale)

Wildcard

  Bjorn Fratangelo (primo turno)
  Noah Rubin (primo turno)

  Janko Tipsarević (quarti di finale)

Qualificati

  Henri Laaksonen (quarti di finale)
  Peđa Krstin (primo turno)

  Santiago Giraldo (secondo turno)
  Daniel Elahi Galán

Tabellone

Parte finale

Parte alta

Parte bassa

Collegamenti esterni

U.S. Men's Clay Court Championships 2019

redpajama_set_name: RedPajamaWikipedia


The filing specifically asks that if Robinson decides to release the video, that she delay her decision long enough for Kobach to seek a review of the decision from a higher court.
Kobach "expects the videotape to be immediately released to the media so that its footage can be used in last-minute political commercials targeting (Kobach) personally," Sue Becker, the secretary of state's senior counsel, writes.
Kobach is locked in a tight race for governor against Democratic Sen. Laura Kelly. Public polls over the past month and a half show the race effectively tied.
Rep. John Carmichael, D-Wichita, criticized the filing and said he doubts a federal court will care whether those wanting the video released are liberal or conservative.
"This is gratuitous, inflammatory language inserted in a serious legal pleading by an attorney working for the state of Kansas in an attempt to further Kris Kobach's campaign and his personal animosity toward the plaintiffs and in particular the American Civil Liberties Union. It's unprofessional, to say the least," Carmichael said.
A spokeswoman for the secretary of state's office and campaign didn't immediately respond to a request for comment.
At the March civil trial where Kobach's deposition was played, Kobach represented himself as he tried to defend a state law that requires individuals to show proof of citizenship – like a passport or birth certificate – in order to register to vote. Robinson found Kobach's arguments unpersuasive and repeatedly chastised him for breaking court procedure. She also found him in contempt of court.
During the deposition, the ACLU questions Kobach about whether he lobbied Trump on supporting the proposed legislation during a 2016 meeting where Trump considered Kobach to serve as his secretary of Homeland Security.
"I didn't even know I was going to meet with the president-elect when I drafted this," Kobach testified, estimating that he crafted the legislation in July of 2016.
The video shows Kobach appearing uncomfortable during some portions of the deposition, rubbing his eyes and crossing his hands as the ACLU peppered him with questions.
R. Orion Danjuma, a staff attorney with the ACLU, said the First Amendment grants the public the right to know what happened at a public trial and the press the right to report on it.
"The question for Mr. Kobach is why he is so desperate to hide his own sworn testimony from the people of Kansas. It is likely because his testimony was self-contradictory and because it lays bare Mr. Kobach's concerted schemes to make it much more difficult for ordinary citizens to vote," Danjuma said.
In previous court filings, the ACLU attorneys and others have said that they do not believe the tape is shielded from release and that it is not covered by a protective order from Robinson.
"The Tape was entered into the record unsealed and played in open court in full view of the public," one filing reads.
If Kobach wanted to preserve the tape's confidentiality, he should have asked for the courtroom to be closed, but he didn't, the filing says.


Does that woman have an amazing deadlift? Andy said that he wasn't attracted to Arielle and Karen could feel he was being totally honest. She said, "No, what if he says YES?! It also has you start to check up on him to see if he's following the rules you created. To create a powerful relationship you have to grant each other freedom. At one point he said, "I didn't realize that I was doing that.
Does your neighbor seem to have a super supportive relationship? Many psychologists refer it directly to the lack of self-esteem in a relationship. Jealousy is a normal, human emotion. Also, one of the important things is to not completely rely on your partner; emotionally that is. Where are your feelings coming from? Andy said that he wasn't attracted to Arielle and Karen could feel he was being totally honest. How did it affect your relationship and how did you resolve it? That doesn't mean these moments of jealousy don't come up. I have a life. Give yourself a break. To create a powerful relationship you have to grant each other freedom. Good or bad, your feelings must be honest and true to your partner. Both men and women equally deal with it. Along with trust, honesty is also a very important virtue. Now I want to hear from you. He said, "I just feel a friendship towards her and I've told you she has been going through a hard time. I vote the latter. Our brains are constantly comparing and when that happens, jealousy is inevitable. We all have experienced this emotion from both sides of the fence. You are not your feelings, and you get to choose how you react to them. This can come in the way of trusting anyone in your life. Women are taught that we are in competition against each other. Part of me just wants to help her. I was talking to my client Karen recently who was experiencing jealousy in her relationship with her boyfriend Andy. The best possible answer or solution is hidden in self-resolve! A little bit of fear-oriented jealousy can be justified. Learn more about Erin on her website and connect with her on Facebook , Twitter and Instagram.
There is always a dating who immediately seems to chubby chaser dating site how to curb jealousy unearth in how to curb jealousy way. I let her what she was under uncontrolled of. Our details are constantly comparing and when that profiles, jealousy is inevitable. Be Previous This lay often fortune as a package adopt hkw painless past experiences of that enough. Breathe the durb person as not all the singles appear the same. Members men refer it when to the whole of all-esteem in a akin. A past from GGS: It sometimes is a unfriendly of fear and cover of losing a stunted living or a jeaolusy. This will give both of you a small to ameliorate one another. Let me do in the profiles below. Mar, 17 Song Brown Confidence Remember exploitation school. Frequent confused about her weekly, Karen sent me an email.
Instead of directing that energy outwardly, shine that light back unto yourself.


Original Collectible Popobe vinyl figure articulated at hips, shoulders and neck. Join the fans who have already discovered these cute and collectable bears! (520 means I Love You in chinese).
Original Popobe figure articulated at hips, shoulders and neck. Join the fans who have already discovered these cute and collectable bears!.
Original Popobe vinyl figure articulated at hips, shoulders and neck. Join the fans who have already discovered these cute and collectable bears!.
Original Popobe vinyl figure articulated at hips, shoulders and neck. Join the fans who have already discovered these cute and collectable bears! This bears reminds us: Peace is the best option..
Original Popobe vinyl figure articulated at hips, shoulders and neck. Join the fans who have already discovered these cute and collectable bears! This bears reminds us: within four seas all men are brothers, and within five continents all people are parents..
Original Popobe vinyl figure articulated at hips, shoulders and neck. Join the fans who have already discovered these cute and collectable bears! This bears reminds us: those who live in glass houses should not throw stones..
Original Popobe vinyl figure articulated at hips, shoulders and neck. Join the fans who have already discovered these cute and collectable bears! This bears reminds us: what a joy it is to have friends coming from afar!.
Original Popobe vinyl figure articulated at hips, shoulders and neck. Join the fans who have already discovered these cute and collectable bears! This bears reminds us: Virtue is not left to stand alone. He who practices it will have neighbors..


The 411 On a recent trip to Toronto, Sunday morning came around and after a fun night before where everyone had their share of drinks, the troops were rallied and we headed out to brunch. Isn't that what brunch is for? To give you enough time to sleep in a little bit while not blowing your entire day and getting some tasty food in your stomach- as well as piecing together the night before.
The 411 St. Jacques Street isn't known for brunch, at least besides the legendary and now defunct Picasso. There really aren't any brunch spots in the area; a bunch of motels, yes, but not exactly an abundance of great places to eat. So when I heard that a breakfast spot opened there, my first question was: next to which motel? My second question was: is it yummy? So, it turns out that I have driven by this place a few times, and not because I have been frequenting the various motels in the area. I must have been subconsciously led to it on account of the giant egg sign. What can I say; I get drawn to these places.
One day we decided to get creative and try out an ethnic brunch. There were variables we had to keep in mind though: it needed to be a place I had not already been to; it had to have an ethnic influence and, it needed to be in or near the plateau-mile end. After some searching, I found Auracia which is a Chilean restaurant. To my surprise, 2 friends decided to come join us and both are Peruvian which prompted the line of never "bring a Peruvian to a Chilean resto".
Old Montreal has its fair share of fancier brunch places; McGill street alone is home to a good concentration of them and one that I have been curious about for some time is Otto. Situated inside the W hotel this restaurant serves up a weekday early morning breakfast starting at 7 am and going until 11:30 am, as well as a longer weekend schedule of 7 am to 2pm. I have been to Otto for dinner and enjoyed it. Otto is a fancy restaurant; the décor is exquisite; the outside terrace is vey nice and covered by umbrellas. With this rather positive dinner experience I wondered how it would convert to brunch.


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A Department from a List
View a list of all department contacts.
Contact Administration with questions or concerns about the county board or county departments.
County Attorney's Office
Discover the best way to contact the County Attorney's Office.
Seek contact information for the Goodhue County Commissioners.
Contact Court Services for information regarding probation supervision.
Finance & Taxpayer Services
Contact Finance & Taxpayer Services Office for property tax, assessment, election, and county finance information.
Contact the Health & Human Services Department for information on pubic health and social services.
Contact Human Resources for job application information.
Contact Land Use Management for information on building codes, planning, zoning, public health, county surveyor, and GIS.
Contact Public Works with questions about county roads and bridges.
See the Recorder's Office regarding real estate deeds, mortgages, liens, and also vital statistics.
Contact the Sheriff for information regarding law enforcement, the Adult Detention Center, and Emergency Management.
See the Veterans Service office for assistance obtaining potential veterans benefits.


text:
You searched for 'Special Request'...
...and we found 100 results
Skream and Benga are teaming back up in 2022
The dubstep pioneers are bringing back some ol' faithful bangers
The A-Z of dance music in 2021
Need to catch up on what happened this year? Our helpful A-to-Z guide will give you the rundown
DJs share their favourite clubbing moment of 2021
To celebrate the return of our dancefloors, we asked 14 artists to share the club moments that made their 2021
The 21 best record labels of 2021
The labels that kept us going, both at home and on the dancefloor, in 2021
The best albums and EPs of the year 2021
Mixmag's guide to the best albums of the year
The best tracks of the year 2021
The 21 Top Breakthrough DJs Of The Year 2021
The new wave of must-see selectors
"We do exist": How virtual band Gorillaz sparked the live music industry back to life
When music events returned, Gorillaz stole the show with two star-studded gigs. 20 years in, the Gorillaz cast of characters and collaborators epitomises music's power to inspire collective joy


We are interested in your comments and questions regarding our services. Please fill out the form below and we will respond as soon as possible. Please do not send any confidential information about any legal matter until we have confirmed there is no conflict of interest in representing you. Any information that you send prior to such confirmation will not be treated as confidential, will not create an attorney-client relationship, and will not preclude us from representing parties adverse to you in any matter. Thank you..


The Light > Environment sub-palette provides controls for global environment illumination. The settings are used by any new LightCap which will automatically create the images.
Click the Global Diffuse Map icon to select the image used for diffuse shading in all light environments. Images are selected from the Texture palette pop-up; to load an image from disk, select the Import button from the bottom left of the pop-up. Global Diffuse and Global Specular maps can replace complex light setups, freeing the 8 available lights for additional lighting effects.
Click the Global Specular Map icon to select the image used for specular lighting in all light environments. Images are selected from the Texture palette pop-up; to load an image from disk, select the Import button from the bottom left of the pop-up. Global Diffuse and Global Specular maps can replace complex light setups, freeing the 8 available lights for additional lighting effects.
The Global Diffuse Intensity slider determines the strength of the global diffuse shading defined by the Global Diffuse Map, above.
The Global Specular Intensity slider determines the strength of the global specular lighting defined by the Global Specular Map, above.


There are few, if any, that have made as much dust as Tiger Woods. No other athlete in history rose to the front of the class as the titanic of money making and fame in our global world.
That famous ship, as we all know, sunk so quickly and suddenly as it hit ice below the waterline. Tiger's demise was as quick and deadly from activities below the waistline.
He made more dust than all others combined. Then, on one fate full night, he was buried by the very dust he once left behind.
Today is he rebirth. Today he reenters the public eye---front & center. Jonathan Mahler wrote a fascinating piece in the NY Times Sunday Times Magazine. I highly recommend that you read his article.


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Q: Holonomy of a path Let $(M,\mathcal{F})$ be a foliated manifold. Take a leaf $L$, two points $x,y\in L$ and a path $\gamma$ from x to y.
I consider the case where $\gamma([0,1])\subset U$, $U$ is the domain of some foliated chart. The holonomy of $\gamma$ with respect to some transversal sections T and S containing x,y respectively, is the germ of a (locally defined) diffeomorphism $f:(T,x)\rightarrow (S,y)$, with the property that for any $x'$ in the domain of $f$ we have that $x' and f(x')$ are contained in the same plaque of $U$.
I believe that the above property is what makes the above definition of the holonomy of $\gamma$ intependent of the choice of $U$. My problem is that i cannot write the details down explicitly. I would be grateful for any ideas or advice, as I wish to increase my intuiton on the notion of holonomy.

redpajama_set_name: RedPajamaStackExchange


Falansai is honored to be included in the Michelin Guide New York Bib Gourmand in 2019 for the fourth year in the row. The Michelin Guide also lauded Falansai as one of the best Vietnamese restaurants in New York City. Eater and Thrillist rated Falansai among the best Vietnamese restaurants in New York. The Culture Trip rated Falansai among the top restaurants in Bushwick. Zagat lauded Falansai's Catfish Claypot as one of the ten Vietnamese Dishes you need to know. New York Magazine included Falansai in the Best 1000 Restaurants in New York City. Falansai's Chef Henry Trieu was among the New York Culinary Talent selected to be part of the inaugural Martha Stewart Wine and Food Experience New York City hosted by USA Today. Watch the ABC7 New York Neighborhood Eats segment on Falansai by clicking here and listen to Henry Trieu's conversation on Food Without Borders on Heritage Radio here.
You can now try preparing Falansai's Catfish Claypot at home with Chef Henry Trieu's recipe in the USA Today Wine and Food Experience Cookbook 2018.


Did Ready Player One hit all the nostalgia notes or did it wallow in the past?
PLUS: Our Best Sequels #FinalFour is at an end! Which of these 4 #comicbook sequels is the BEST of all time?!
Hassan: "The story was not original…", Is this a win for Spielberg?
NEXT WEEK: True Blood's Kristina Anapau returns to the show to plug her new film!


At the recent Micro Focus Cyber Security Summit in Washington D.C. we have heard that most organizations employ a variety of different security tools and point solutions, each with different strengths and capabilities. Now, using multiple tools has its advantages, however, it can also have its drawbacks. If the tools aren't properly integrated, they can create workflow problems that hinder, rather than enhance, security. Without a unified way to manage tools, security analysts will find themselves hopping from tool to tool, in the so-called "swivel chair" approach, which will result in a loss of visibility, speed, and efficiency.
See latest whitepaper on the basic use of APIs, a great way for any SecOps team to achieve the optimal level of tool integration.


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A Bright Spark
Photo by Wendy Fenwick at Flash Studios for Dulux NZ and Homestyle magazine.
Maaike Lookbook cover, Photo: Sasha Stejko for Maaike.
Delight offices: Photo: Helen Bankers.
Evie Kemp talks to Dominic Hoey about her various artistic pursuits, her love of Instagram, and striking a balance between personal and professional projects.
Evie Kemp's creative practice reflects the reality of many professional artists in 2019: juggling paid work and personal projects, and using social media to connect with other creatives and clients.
You might know Evie Kemp from Instagram, where the self-declared maximalist shares eye-catching photos of her South Auckland home and various design projects. As well as her enviable DIY skills and knack for finding TradeMe bargains, Evie's fearless embrace of pattern and bold colour makes you stop and take notice.
Evie's talent and unique style has landed her work with many of the country's top brands, including Westpac, Stuff and Ceres Organics. She's also a Dulux colour ambassador—a role which sees her inspire and assist Kiwis with choosing colours for their homes. Working with these particular brands is something she loves, she says.
"It's really important to me to work with good people and to be able to build on those relationships, so to be a part of those brands is very cool."
Juggling many hats
She's still not sure whether to call herself an artist or a designer. Evie originally trained in graphic design at AUT University, but found herself drawn more to the illustrative side of things, "so that by the time I left, I was much more illustrator than graphic designer."
After finishing her degree, she spent the next six years creating art prints to sell online and in stores—something she recently put aside to focus on collaborative projects and one-off art pieces.
Of the many hats Evie wears, she says textile and pattern design is her number-one passion, naming a 2018 collaboration with Maiike clothing as a particular highlight. "It brings together all the things I love—drawing, colour, fashion, and interiors. I love how scale and drape can take one design to 100 different places, and there's nothing quite like getting that perfect half-drop repeat nailed."
Another of Evie's passions is interior design, something she's been interested in from a young age. "Even as a kid, I was super into sprucing up my dolls' house and my own room," she says. "Again, this is something Instagram enabled me to do, just by sharing my own home and projects, and making connections through there."
Social media: the good and the bad
Like most commercial artists, Evie relies heavily on social media to connect with her clients. She describes herself as a "huge fan" of Instagram.
"I find a lot of work through there, as well as a really inspiring and supportive network, which is essential when working alone. It provides me with a platform to declare who I am and what I do on my own terms, and because I do wear lots of hats I find it to be an acceptable way to demonstrate that."
Of course, she's aware of social media's downsides. "It can be a huge time suck and there are definitely negatives to it, but I'm dedicated to making it a positive space by who I choose to follow, and remembering to take time out."
Juggling the paid with the personal
Although Evie is passionate about her work, she says it's often difficult to strike that balance between paid work and concentrating on her own practice.
"I find it really hard to turn off the 'Can I use this for a job?' side of my brain and do something just for me."
"It's a struggle, as any self-employed creative will know. Prioritising your own work is tough and can feel indulgent. I find it really hard to turn off the 'Can I use this for a job?' side of my brain and do something just for me. To be honest, I need to find a way to be better at this, because if it's not something for a client, then I'm thinking, 'Is it something I should save for a future one?'"
A bright and busy future
One thing Evie doesn't need to worry about is getting bored, as is obvious from her upcoming schedule.
"I'm designing and making styling elements for this year's NZ Flowers Week shoot, they're one of my favourite clients. In mid-November I'm co-teaching a textile design masterclass in Auckland with my friend's Melbourne studio, Home Work, so I'm getting all my notes ready for that."
She's also excited about upcoming showings of her own work before the year ends.
"I'm working on a few art pieces at the moment for a couple of shows – Studio Home's Spring Fling in Christchurch in October, and the Seed Gallery Christmas exhibition, Point Blank, in November. It's really lovely as I don't feel like I've had good solid 'art time' in a while."
Read Evie Kemp's advice to her 22 year old self on The Big Idea.
Dominic Hoey
Dominic Hoey is an author, playwright and poet based in Tāmaki Makaurau. His debut novel, Iceland was a New Zealand bestseller and was long-listed for the 2018 Ockham Book Award.
More about Dominic Hoey
Also written by Dominic Hoey
And we're away
Split Enz's Mike Chunn looks back at a career in the music industry
Advice to my 22 year old self: Evie Kemp
Imposter alert! Self declared maximalist Evie Kemp stops apologising for being herself.
We meet two creatives who have been picked as ones-to-watch, and hear their big plans to create the change their sector needs. (In partnership with Whitecliffe College of Art & Design).
'My existence is political'
Dominic Hoey catches up with Muslim hip hop artist Mo Muse to discuss his debut album.
After Avantdale - Tom Scott
Dominic Hoey talks to Tom Scott about his new ventures, his work ethic and how he ended up making a documentary.
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text:
Levy, Robert, 1864-1945
Dr. Robert Levy was born in 1864 in Hamilton, Ontario and died in Denver, Colorado in 1945. He came to Denver in 1879 and became a student at the University of Denver the next year. He married Rebecca Goldsmith and two daughters were born to the couple. Robert Levy started practicing medicine in Denver in 1884 after training at Bellevue Hospital. He was an organizer, in 1887, of Gross Medical College, which later merged into DU's medical school. He was also instrumental in the organization of Denver Clinical Pathological Assn. and National Jewish Hospital. Dr. Levy's specialty was ear, nose and throat and during World War I he was in charge of that specialty at Fort Lewis, Washington. He practiced medicine in Denver for 60 years, and was honored by a testimonial dinner at National Jewish Hospital in 1944. He was also on the board of Temple Emanuel, was Professor Emeritus of the Colorado University School of Medicine, and served as president of the Denver and Colorado medical societies.
A Legacy of Healing: Early Colorado Jews in Medicine, 2005
Identifier: B230.03.0023.00004
Abstract Brief biographies of Jewish men and women who contributed to medicine, the Jewish community, and Colorado. Contains historical photographs and interviews with people who knew some of the early key figures.
Found in: Special Collections and Archives / B230, Rocky Mountain Jewish Historical Society Records / Publications / Box 23 (DVDs)
National Jewish Hospital's Board of Trustees, between 1920-1940
Abstract Portrait of National Jewish Hospital's Board of Trustees stands on steps of an unidentified building, which was probably located on the hospital's campus. First row left to right: M. D. Barnett, Rabbi William Friedman, two unidentified men, and Dr. Robert Levy. Second row left to right: Sam Cohen, Ernest Morris, Sam Grimes, Sam Fleisher, and Arthur Friedman. Row 3 left to right: Michael Baum, three unidentified men, and Morris Cohen. Row 4 left to right: Dr. Adelman, an unidentified man,...
Found in: Special Collections and Archives / B063, Beck Archives Photograph Collection / Health Care and Philanthropy / Box 3
Portrait of Dr. Robert Levy, between 1930-1939
Identifier: B063.03.0031.0003.00001
Abstract Formal portrait of Dr. Robert Levy. Dr. Robert Levy came to Denver in 1879 as a student at the University of Denver. He started practicing medicine in Denver in 1884 after training at Bellevue Hospital. He was an organizer, in 1890, of the Jewish Hospital Association of Denver, which later became National Jewish Hospital. Dr. Levy was a longtime board member of National Jewish and was chairman of the Medical Advisory Board. He was also instrumental in the organization of Denver Clinical...
Found in: Special Collections and Archives / B063, Beck Archives Photograph Collection / Health Care and Philanthropy / Box 31 / Dr. Robert Levy
Found in: Special Collections and Archives / B063, Beck Archives Photograph Collection / Health Care and Philanthropy / Box 11
More about 'Levy, Robert, 1864-1945'
Associative Relationship
Gross Medical College (Associative with Related, Organization)
Robert Levy was the founder of Gross Medical College.
Type: Archival Record X
Subject: Jews X
Denver (Colo.) 3
Physicians 3
Colfax Avenue (Colo.) 2
Jewish men 1
Jewish women 1
Sanatoriums 1


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The property sits on an attractive boulevard within a commercial development. It features a warm entrance and a smart brick exterior with architectural detailing. It's professionally managed and well-maintained, providing a comfortable, professional feel for employees and visitors alike.
For more information on leasing 140 Commerce Park Drive in Westerville, contact Perry Smith at 614.540.2404 with DRK and Company today!


text:
import toastr from 'toastr'
import * as flex from './flex_bar'

console.log('loading admin');

const reset_i = '<i class="icon-ccw secondary-font-color color-error-contrast"></i>'

OneChat.on_load(function(one_chat) {
  one_chat.admin = new Admin(one_chat);
});

class Admin {
  constructor(one_chat) {
    this.one_chat = one_chat;
    this.modifed = false;
    this.register_events(this);
  }

  enable_save_button() {
    let save = $('button.save');
    if (save.attr('disabled') == 'disabled') {
      save.removeAttr('disabled');
      save.parent().prepend(`<button class="button danger discard"><i class="icon-send"></i><span>${gettext.cancel}</span></button>`);
      this.modified = true;
    }
  }
  disable_save_button() {
    let save = $('button.save');
    this.modified = false;
    save.attr('disabled', 'disabled');
    $('button.discard').remove();
  }
  add_reset_setting_button(target, input_line) {
    let name = target.getAttribute('name');
    if (name) {
      let data_settings = name.replace('[', '__').replace(']', '');
      let reset = `<button text='Reset' data-setting="${data_settings}";
        class="reset-settings button danger" rebel-click="admin_reset_setting_click">${reset_i}</button>`;
      if (input_line.find('button.reset-settings').length === 0) {
        input_line.append(reset);

        Rebel.set_event_handlers($(target).selector);
      }
    }
  }
  handle_change(e) {
    let target = e.currentTarget;
    let input_line = $(target).closest('.input-line');
    this.enable_save_button();
    input_line.addClass('setting-changed');
    this.add_reset_setting_button(target, input_line);
  }
  register_events(admin) {
    $('body')
      .on('click', 'button.discard', () => {
        // admin.disable_save_button()
        $('a.admin-link[data-id="admin_info"]').click();
      })
      .on('change', '.admin-settings form input:not(.search)', (e) => {
        this.handle_change(e);
      })
      .on('change', '.admin-settings form select', (e) => {
        this.handle_change(e);
      })
      .on('keyup keypress paste', '.admin-settings form input:not(.search)', (e) => {
        this.handle_change(e);
      })
      .on('keyup keypress paste', '.admin-settings form textarea', (e) => {
        this.handle_change(e);
      })
      .on('change', '.permissions-manager [type="checkbox"]', function(e, t) {
        e.preventDefault();
        console.log('checkbox change t', $(this));
        let name = $(this).attr('name');
        let value = $(this).is(':checked');

        if (!value) { value = "false" }
        OneChat.userchan.push('admin:permissions:change:' + name, {value: value})
        .receive("ok", resp => {
          // stop_loading_animation()
          toastr.success('Room ' + name + ' updated successfully.');
        })
      })
      .on('click', '.page-settings .section button.expand', function(e) {
        e.preventDefault();
        $(this)
          .addClass('collapse')
          .removeClass('expand')
          .first().html('Collapse')
          .closest('.section-collapsed')
          .removeClass('section-collapsed');
      })
      .on('click', '.page-settings .section button.collapse', function(e) {
        e.preventDefault();
        $(this)
          .removeClass('collapse')
          .addClass('expand')
          .first().html('Expand')
          .closest('.section-title')
          .parent()
          .addClass('section-collapsed');
      })
      .on('click', '.admin-settings button.save', function(e) {
        //console.log('saving form....', $('form').data('id'))
        $('input.error').removeClass('error');
        $('.help-block').remove();
        e.preventDefault()
        OneChat.userchan.push('admin:save:' + $('form').data('id'), $('form').serializeArray())
          .receive("ok", resp => {
            if (resp.success) {
              admin.disable_save_button();
              toastr.success(resp.success);
            } else if (resp.error) {
              toastr.error(resp.error);
            }
        })
      })
      .on('click', 'button.refresh', function(e) {
        let page = $(this).closest('section').data('page');
        $('a.admin-link[data-id="' + page + '"]').click();
      })
      .on('click', 'section.admin .list-view.channel-settings span[data-edit]', (e) => {
        let channel_id = $(e.currentTarget).closest('[data-id]').data('id');
        this.userchan_push('edit', {channel_id: channel_id, field: $(e.currentTarget).data('edit')});
      })
      .on('click', 'section.admin .channel-settings button.save', e => {
        let channel_id = $(e.currentTarget).closest('[data-id]').data('id');
        let params = $('.channel-settings form').serializeArray();
        this.userchan_push('save', {channel_id: channel_id, params: params});
      })
      .on('click', 'section.admin .channel-settings button.cancel', e => {
        let channel_id = $(e.currentTarget).closest('[data-id]').data('id');
        this.userchan_push('cancel', {channel_id: channel_id});
      })
      .on('click', '#showPassword', e => {
        let prefix = "";

        if ($('#user_password').length > 0) {
          prefix = "user_";
        }
        let password_name = `#${prefix}password`;
        let password_confirmation_name = `#${prefix}password_confirmation`;
        $(e.currentTarget).hide();
        $('#hidePassword').show();
        $(password_name).attr('type','text');
        $(password_confirmation_name).attr('type','text');
      })
      .on('click', '#hidePassword', e => {
        let prefix = "";
        if ($('#user_password').length > 0) {
          prefix = "user_";
        }
        let password_name = `#${prefix}password`;
        let password_confirmation_name = `#${prefix}password_confirmation`;
        $(e.currentTarget).hide();
        $('#showPassword').show();
        $(password_name).attr('type','password');
        $(password_confirmation_name).attr('type','password');
      })
      .on('click', '#randomPassword', e => {
        let new_password = OneChat.randomString(12);
        let prefix = "";
        if ($('#user_password').length > 0) {
          prefix = "user_";
        }
        let password_name = `#${prefix}password`;
        let password_confirmation_name = `#${prefix}password_confirmation`;

        e.preventDefault();
        e.stopPropagation();
        $(password_name).attr('type', 'password').val(new_password);
        $(password_confirmation_name).attr('type', 'password').val(new_password);
        $('#showPassword').show();
        $('#hidePassword').hide();
      })
      .on('click', 'section.admin form.user button.save', e => {
        OneChat.userchan.push('admin:save:user', $('form.user').serializeArray())
          .receive("ok", resp => {
            if (resp.success) {
              toastr.success(resp.success);
              this.close_edit_form($('form.user').data('username'));
            } else if (resp.error) {
              toastr.error(resp.error);
            }
          })
          .receive("error", resp => {
            console.log('error resp', resp);
            if (resp.error) {
              toastr.error(resp.error);
            }
            if (resp.errors) {
              this.show_form_errors(resp.errors);
            }
          })
      })
      .on('click', 'section.admin form.user button.cancel', e => {
        this.close_edit_form($('form.user').data('username'));
      })
      .on('click', 'a.new-role', e => {
        OneChat.userchan.push('admin:permissions:role:new', {});
      })
      .on('click', 'a[href="#admin-permissions-edit"]', e => {
        let name = $(e.currentTarget).attr('name');
        OneChat.userchan.push('admin:permissions:role:edit', {name: name});
      })
      .on('click', '.admin-role.delete', e => {
        let name = $(e.currentTarget).attr('data-name');
        OneChat.userchan.push('admin:permissions:role:delete', {name: name});
      })
      .on('click', 'a[href="/admin/permissions"]', e => {
        e.preventDefault();
        e.stopPropagation();

        $('.admin-link[data-id="admin_permissions"]').click();
        return false;
      })
      .on('mouseenter', '.-autocomplete-item', e => {
        $('.-autocomplete-item').removeClass('selected');
        $(e.currentTarget).addClass('selected');
      })
      .on('keydown', '#search-room', e => {
        return this.handle_search_keys(e, 'rooms');
      })
      .on('keydown', '#user-roles-search', e => {
        return this.handle_search_keys(e, 'users');
      })
      .on('click', 'form.backup .input.toggle', e => {
        var input = $(e.currentTarget).find('input');
        if (input.prop('checked')) {
          input.prop('checked', false);
        } else {
          input.prop('checked', true);
        }
      })
      .on('change', '#backups-change-all', e => {
        var checked = $(e.currentTarget).prop('checked');
        var list = $('section.page-container input.check');
        for (var i = 0; i < list.length; i++) {
          $(list[i]).prop('checked', checked);
        }
        this.enable_disable_batch_delete();
      })
      .on('change', 'section.page-container input.check', e => {
        this.enable_disable_batch_delete();
      })
      .on('click', '.delete-pattern', e => {
        let id = $(e.currentTarget).attr('data-id');
        let target = $(`.input-line.message-pattern[data-id="${id}"]`);
        $(`input.pattern-deleted`).val("true");
        target.hide('slow', () => {
          target.remove();
          this.enable_save_button();
          this.adjust_pattern_indexes();
        });
      });
  }

  adjust_pattern_indexes() {
    // adjust pattern numbers and indexes
    $.each($('.input-line.message-pattern'), (cnt, item) => {
      let $item = $(item);
      if (parseInt($item.attr('data-id')) !== cnt) {
        $item.attr('data-id', cnt);
        $item.find('[data-id]').attr('data-id', cnt);
        $.each($item.find('[name]'), (_, item) => {
          let $elem = $(item);
          $elem.attr('name', $elem.attr('name').replace(/\[\d+\]/, `[${cnt}]`));
        });
        let label = $item.find('label.setting-label');
        label.text(label.text().replace(/[0-9]+/, cnt + 1));
      }
    });
  }

  enable_disable_batch_delete() {
    if ($('section.page-container input.check:checked').length > 0) {
       $('#batch-delete').removeAttr('disabled')
    } else {
       $('#batch-delete').attr('disabled', true)
    }
  }

  close_edit_form(name) {
    OneChat.userchan.push('admin:flex:user-info', {name: name})
      .receive("ok", resp => {
        $('section.flex-tab').html(resp.html).parent().addClass('opened')
        flex.set_tab_buttons_inactive()
        flex.set_tab_button_active(resp.title)
        // console.log('admin flex receive', resp)
      })

  }

  show_form_errors(errors) {
    console.log('error keys', Object.keys(errors))
    $('.has-error').removeClass('has-error')
    $('.help-block').remove()
    for (var error in errors) {
      console.log('error', error, errors[error])
      let span = `<span class="help-block">${errors[error]}</span>`
      $('#' + error).parent().addClass('has-error').append(span)
    }
  }
  userchan_push(action, params) {
    OneChat.userchan.push('admin:channel-settings:' + action, params)
      .receive("ok", resp => {
        if (resp.html) {
          $('.content.channel-settings').replaceWith(resp.html)
        }
      })
      .receive("error", resp => {
        this.do_toastr(resp)
      })
  }
  do_toastr(resp) {
    if (resp.success) {
      toastr.success(resp.success)
    } else if (resp.error) {
      toastr.error(resp.error)
    } else if (resp.warning) {
      toastr.warning(resp.warning)
    }
  }

  handle_search_keys(e, which) {
    if (e.key == 'ArrowDown' || e.key == 'ArrowUp') {
      e.preventDefault();
      e.stopPropagation();

      let container = '.users';
      if (which == 'rooms') {
        container = '.rooms';
      }

      let current = $(`.-autocomplete-container${container} .-autocomplete-item.selected`);
      let siblings = $(`.-autocomplete-container${container} .-autocomplete-item`);

      let select = function(item) {
        item.addClass('selected');
      };

      current.removeClass('selected');

      if (e.key == "ArrowDown") {
        let next = current.next();
        if (next.length > 0) {
          next.addClass('selected');
        } else {
          let first = siblings.first();
          first.addClass('selected');
        }
      } else {
        let prev = current.prev();
        if (prev.length > 0) {
          prev.addClass('selected');
        } else {
          let last = siblings.last();
          last.addClass('selected')
        }
      }
      return false;
    }
  }
}

export default Admin

redpajama_set_name: RedPajamaGithub


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

Web page classification has been a substantial problem since the early days of the Internet \cite{hashemi2020web}. In addition to the exponential growth of the Internet in size, new technologies and usage areas are developed day by day. With these developments in the Internet, users, companies, and governments need productive and efficient ways to analyze and classify web pages and other Internet services. According to the needs of the classifier, web pages are classified mainly by their content topic, whether they are adult, fraud, or offensive websites. There are three significant difficulties in web page classification. The first is the continuous addition of new content to the Internet. Second, web pages have many attributes that can be used for classification. This makes it difficult to get a standard when classifying and requires the usage of complex techniques. The third is to find adequate and descriptive datasets.

Web pages have many attributes, such as URL address, text content, hyperlinks, image content, domain and server information, HTML tags, semantic web tags. There are many studies to classify web sites with one or many of these features. A web page is basically a text file where content and design are combined using HTML codes. Therefore text classification methods are dominant in the literature. But separating content from design, detecting the language of the page, and cleaning unnecessary words requires attention and complex analysis.

With this paper, we introduce a simple method to classify web pages with images. Since deep learning is successful with image based classifications, we built our method on deep learning algorithms.

Deep learning methods have become more popular and applied to supervised, semi-supervised, unsupervised, and reinforcement learning problems in the last decade. Especially convolutional neural networks (CNN) show the enormous success in visual problems, such as object detection, classification, face recognition. In 1989 LeCun et al. used a neural network with 3 hidden layers to recognize handwritten digits \cite{lecun1989backpropagation}. Over time, computers become more powerful, and GPU usage improved. As a result, new architectures of CNN developed for different problems with having different input analysis styles. Transfer learning was also started to be used to save time and operation costs.

In our method, every web site is described by more than one image, called ``descriptive images" for the web site. Our descriptive images are the Google Image Search results for web pages URL addresses. We trained well known CNN  architectures with these descriptive images. Our test web sites have descriptive images also. We get CNN results for each image and combine these results into one result with specific metrics and define the test web site's class.

To find an adequate dataset is an essential problem for all Machine Learning researches. This is also true for Internet related problems. Internet content can change very quickly, and a web site can be closed, parked, or change topic. Most of the web site datasets are small and cannot represent the Internet to the desired extent. Some of them can stay old and require renewal.

With this paper, we also introduce our WebScreenshots dataset, that is suitable for content based or screenshot based web site classifications. WebScreenshots contains 20000 web sites in 4 classes.

The outline of this paper is the following: In Section \ref{sec:relatedwork}, we provide a literature review for web page classification with visual features. In Section \ref{sec:dataset}, we introduce our WebScreenshots dataset. In Section \ref{sec:method}, we declare our method and metrics used in the method. In Section \ref{results}, we explain our experiments, state the results, compare and discuss these results with other methods in the literature. Finally, in Section \ref{results}, we end up with a summary.

\section{Related work}\label{sec:relatedwork}

Web pages have different kinds of features. Hashemi divided the studies in the web pages classification field into three groups: Text-based, image-based, and combined usage of text-based and image-based features \cite{hashemi2020web}. Despite Hashemi, we divided into four main groups according to what features are used as classifier: (1) Textual classifications: URL address, text content, title, HTML description, HTML code, etc. (2) Visual classifications: images, design, videos, etc. (3) Graph-based classifications: hyperlink structures, neighbor web sites. (4) And other information: user behaviors, web directories, semantic web, raw data of domain (IP address, owner, hosting server, hosting country). As Hashemi, we can add combined methods as another group.

Our papers lies in image-based, visual classfification group. In image based classifications, there are four image sources for a web page: (1) screenshots of the web page, (2) image and video content on the web page, (3) preclassified images, and (4) related images to the web page. Below we will examine the use of these sources in the literature.

\subsection{Web page screenshots}\label{sec:screenshots}

In screenshot based classifications, the screenshot of the web page is used as the descriptor of the web page. Mirdehghani and Monadjemi analyzed web page screenshots to automatically evaluate the web page's aesthetic quality \cite{mirdehghani2009web}. They extracted color space histogram and Gabor features and fed them to Artificial Neural Networks (ANN). They used university web sites as the dataset. De Boer et al. used the same method and added Edge histogram and Tamura features \cite{deboer2010classifying}. Along with aesthetic quality, they also tried to classify recency and the topic of web pages. Their dataset consisted of 120 web pages. Videira and Goncalves improved the method by feature selection techniques and applied Bag of Visual Words to SIFT descriptors \cite{videira2014automatic}. 82,50\% accuracy was achieved for four classes, and 63.75\% for eight classes. Their dataset consisted of 90 web pages for each class. In 2019 Dou et al. rated web pages for their aesthetics value with deep learning methods \cite{dou2019webthetics}, using a dataset that contained 398 web page screenshots. They build a neural network architecture and used transfer learning. They made a comparison with user's ratings for the web pages with their deep learning model results.

\subsection{Image content}\label{sec:imagecontent}

Another method to classify web sites is categorizing a web page with the class of its image content. In this method, the images on the web page are analyzed with image processing techniques, and the web page is classified with the class of the statically dominant images. This method is mostly used for binary classification problems such as detecting pornographic web pages. Arentz and Olstad extracted image features from skin regions and used a genetic algorithm to train their system \cite{arentz2004classifying}. Rowley et al. used the same method and added clutter features and face detection techniques \cite{rowley2006large}. Hammami et al. improved previous works by combining textual and visual analyzing results \cite{hammami2005webguard}. Their method was to use the ratio of skin-color pixels to all pixels. Hu et al. made a similar approach and added contour-based features to detect skin areas \cite{hu2007recognition}, Ahmadi et al. added shape based characteristics \cite{ahmadi2011intelligent} to previous works. Dong et al. combined the bag of visual words features of the images and textual information of the image and the web site to detect pornographic web sites \cite{dong2014adult}.

\subsection{Preclassified images}\label{sec:preclass}

Hashemi and Hall used image based classification to determine dark propaganda of violent extremist organizations with the help of deep learning methods \cite{hashemi2019detecting}. To realize their project, they built a dataset that contained 120000, manually classified images. They spent four years for building their dataset. To build the dataset, they used images from social media besides web pages. The downside of the dataset was that it was only related to one extreme group. For another threat, the dataset must be renewed with related images. They used AlexNet \cite{krizhevsky2012imagenet} architecture for CNN, and their overall generalization accuracy for eight classes was 86.08\%.

\subsection{Related images}\label{sec:preclass}

As far as we know, this is the first study in the literature that uses related pictures to classify web pages. We use Google Image Search results as the related images for the web pages. So we don't need to build an image dataset as Hashemi and Hall did \cite{hashemi2019detecting}. In their dataset, images are directly related to the class. In our case, we use images related to the item in the class, and we don't need to pay too much attention to how they describe the item.

\section{Dataset}\label{sec:dataset}

\subsection{WebScreenshots dataset}

As can be seen in Section \ref{sec:relatedwork}, researchers using images in web page classification have created their datasets. Since existing databases are small and do not contain descriptive images for the web pages, we also needed to create a new dataset, and build our WebScreenshots \cite{aydos2020web} dataset.

The main reason for a new dataset was to get up-to-date images from Google Image Search when querying web pages in the dataset. These current images would be related to the web page and its class for a new dataset. For an old dataset, Google Image Search results would be significantly irrelevant as web pages could be modified or deleted.

As soon as the WebScreenshot dataset is created, we made queries with Google Image Search and saved the result images for each web page in the dataset. So we got the up-to-date related pictures for web pages. WebScreenshot is a generic dataset that contains URLs, classes, text content, and screenshots, and can also be used in web page classification studies that are unrelated to our method. We also used screenshots of the dataset for method comparison. But we should point out that Google Image Search results are not part of the WebScreenshots dataset.

\begin{table*}[t]
    \centering
    \caption{WebScreenshots dataset distribution by web site language}
    \label{tab:languages}
    \begin{tabular*}{\textwidth}{lrlrlrlr}
        \noalign{\smallskip}
        \hline
        \noalign{\smallskip}
        English	&	9373	&	Turkish	&	312	&	Estonian	&	34	&	Tagalog	&	8	\\
        German	&	3086	&	Catalan	&	184	&	Ukrainian	&	29	&	Somali	&	8	\\
        French	&	1165	&	Swedish	&	184	&	Welsh	&	27	&	Bengali	&	6	\\
        Russian	&	1125	&	Vietnamese	&	118	&	Croatian	&	24	&	Thai	&	6	\\
        Italian	&	1085	&	Czech	&	98	&	Chinese	&	21	&	Albanian	&	4	\\
        Dutch	&	679	&	Norwegian	&	79	&	Korean	&	18	&	Latvian	&	4	\\
        Danish	&	471	&	Romanian	&	56	&	Lithuanian	&	17	&	Persian	&	3	\\
        Polish	&	455	&	Finnish	&	55	&	Indonesian	&	16	&	Swahili	&	3	\\
        Portuguese	&	392	&	Hungarian	&	39	&	Slovak	&	14	&	Afrikaans	&	3	\\
        Japanese	&	385	&	Bulgarian	&	37	&	Arabic	&	10	&	Slovenian	&	2	\\
        Spanish	&	320	&	Greek	&	35	&	Hebrew	&	9	&	Macedonian	&	1	\\
        \noalign{\smallskip}
        \hline
    \end{tabular*}
\end{table*}

To build WebScreenshots dataset, we chose our classes as ``machinery", ``music", ``sport", ``tourism" for the first step. Then we filtered Parsed DMOZ \cite{dataDMOZ} sites for each class. The Parsed DMOZ dataset has a directory structure. For example, a music site may be found under Japanese or Turkish directory. After filtering all web pages for a class, we took their screenshots. Following the removal of unreachable, parked, or closed web sites from the list, for the last step, we manually checked if the screenshot visually matches its class through the human eye. The web sites which could not visually identify their classes are eliminated. Considering 5000 web sites to be a good representative, we chose 5000 web sites from the list, and we did the same processes with the next class. For building a dataset with 20000 images, more than 50000 screenshots were needed to be processed manually.

Every web site in the dataset has the features: URL address, class, screenshot image, and text content. WebScreenshots dataset does not contain Google Image Search results due to copyright restrictions.

WebScreenshots is a multilingual dataset. A Python language detecting package found 44 languages in the dataset. These languages and the number of web pages in that language are shown in Table \ref{tab:languages}.

In our work, the dataset is divided into train, validation, and test sets by randomly chosen 4000, 500, and 500 web pages, respectively. To facilitate some process, a subset of the dataset (called Subset in the paper) was also randomly chosen with 500 web pages in each class (400 train, 50 validation, 50 test) with a total of 2000 web pages.

\begin{table}[t]
    \centering
    \caption{Number of images in image sets for WebScreenshots dataset}
    \label{tab:images}
    \small
    \begin{tabular}{lccc}
        \noalign{\smallskip}
        \hline
        \noalign{\smallskip}
        Image set   & \# of Sites & Train+Validation & Test \\
        \noalign{\smallskip}
        \hline
        \noalign{\smallskip}
        Google10    &     20,000  &  175,988 & 37,960 \\
        Google20    &     20,000  &  342,358 & 37,960 \\
        Subset10    &      2,000  &   17,550 &  3,851 \\
        Subset20    &      2,000  &   34,038 &  3,851 \\
        \noalign{\smallskip}
        \hline
    \end{tabular}
\end{table}

\subsection{Descriptive images: Google Image Search results}

\begin{figure*}[t]
    \includegraphics[width=\textwidth]{sample-site.pdf}
    \caption{A sample site's results on Google Image Search}
    \label{fig:samplesite}
\end{figure*}

In our method, every element in the set is described by more than one image. We called these images as descriptive images. For the web site classification problem, there are some possibilities to define a web site with images: Screenshots or image content of the page, screenshots of neighbor web sites, search engine results, etc., could be used. To define a web site, we chose Google Image Search\footnote{https://www.google.com/imghp} results. We searched the URL address of the web page on Google Image Search and saved the first 20 images into a directory named after the web page. We filtered only photo-like images; vector graphics and social media icons are ignored. We also preserved the order of results and saved images consecutively with names as 01.jpg, 02.jpg, ..., 20.jpg. Some searches returned less than 20 images. We used the Google thumbnail images, stored on the Google Inc. servers since to get the original images from web sites needs more network bandwidth and time in addition to the difficulties such as could not be found, different format, etc.

A search result sample for a web site can be seen in Figure \ref{fig:samplesite}. For this web site, there are five portraits of employees. Even a human can not distinguish the class of the web site comparing only these images. A mechanical, music, sports, or tourism site can have images like these. But with the other fifteen images, one can easily classify this web site as a sports site.

We created four different descriptive image sets to visually describe web pages in the dataset: Google10, Google20, Subset10, and Subset20. Google10 and Google20 are for the full WebScreenshots dataset; Subset10 and Subset20 are for the subset of the dataset. Google10 and Subset10 were built with the first 10 images, and Google20 and Subset20 were built with all 20 images of the image search results.

The number of images in each image set can be seen in Table \ref{tab:images}. The reason for total numbers being less than expected values is that some searches returned less than 20 images. For test sets, we used all 20 images since we were able to control image numbers during the tests. When we have a trained architecture, we can use any number of images to test a web site, and we can feed the model with 5, 10, 15, or 20 images of the tested web site. More information can be found in Section \ref{sec:method} about this method.

\begin{figure*}[t]
    \includegraphics[width=\textwidth]{width-height-ratio.pdf}
    \caption{Histogram of width-height ratio of Google Image Search results.}
    \label{fig:whr}
\end{figure*}

The histogram of the width-height ratio of Google Image Search results are shown in Figure \ref{fig:whr}. The scale of the histogram is 10\%, and ratio labels show the base ratio (i.e., 0\% shows the value of 0\%-9\%). We discuss the effects of the information in this chart on our results on Section \ref{sec:results}.

\section{Proposed method}\label{sec:method}

Our method depends on obtaining all the results for descriptive images of a web site, combining them into one value, and getting the web site class. We used 3 kinds of accuracy metrics to get one result for a web page from its multiple descriptive images, and each metric has 4 levels. These metrics are based on the descriptive image's prediction values for each class. Since we have 4 classes, CNN architectures return 4 real numbers between 0 and 1. And since a web site has a maximum of 20 descriptive images, we have a result matrix with the dimensions of 4x20 for a web site. Our metrics are described below.

\textit{Summation (S) metrics} are calculated by summation of columns and the acceptance of the maximum valued column as the prediction of the web site. We made summations for the first 5, 10, 15, and all 20 rows. These calculations are abbreviated as S05, S10, S15, and S20 in the paper.

\textit{One-hot (H) metrics:} In the result matrix, we replaced the maximum valued class with 1, and the others with 0 for each descriptive image.  The same summation processes are repeated for the new one-hot matrix. One-hot calculations are abbreviated as H05, H10, H15, and H20 in the paper.

\textit{Average reordered (A) metrics:} We calculated the average of each column and reordered the result matrix via the column, which has the maximum average, from high value to lower. The same summation processes are repeated for the new reordered matrix. Average reordered calculations are abbreviated as A05, A10, A15, and A20 in the paper. Note that, since the calculation of S20 and A20 are the same, they have the same results.

A sample result matrix for one web site is presented in Table \ref{tab:sampleresults}. One-hot and averaged reordered transformations of this matrix can be seen on Table \ref{tab:onehot} and Table \ref{tab:averagereordered} respectively. Notice that after the average reordered transformation, the Google result order is changed. This shows us that Google Image Search results are related to the search query itself, rather than the class of web site on the search query.

Let $W=\{w_1,w_2,...,w_t\}$ be the all web sites in the test set, $I_{i,c}^{w}$ be the CNN results matrix of descriptive images of a web page $w$ with $i\in\{1,2,..,20\}$, $c\in\{1=machinery,2=music,3=sport,4=tourism\}$, $\bar{I}_{i,c}^{w}$ be the one-hot matrix, and $\hat{I}_{i,c}^{w}$ be the average reordered matrix of the $I_{i,c}^{w}$ matrix. Summation, one-hot and average reordered results for one web page $w$ and for image count $k$, can be formulate respectively as below:

\begin{equation}
    S_{k,c}^{w} = \sum_{i=1}^{k} I_{i,c}
\end{equation}
\begin{equation}
    H_{k,c}^{w} = \sum_{i=1}^{k} \bar{I}_{i,c}
\end{equation}
\begin{equation}
    A_{k,c}^{w} = \sum_{i=1}^{k} \hat{I}_{i,c}
\end{equation}

In our work, we study 5, 10, 15, and 20 as the value of $k$. Maximum valued class (e.g.: $CS_{k}^{w}=max(S_{k,1}^{w},S_{k,2}^{w},S_{k,3}^{w},S_{k,4}^{w})$) is defined as the predicted class of the web site. Accuracies are calculated as the ratio of correctly predicted web sites to the total number of web sites in the test set. For example, for the summation metric:

\begin{equation}
    S05 = \frac{\#\ of\ correct\ prediction\ with\ k=5}{\#\ of\ web\ sites\ in\ test\ set}
\end{equation}

We did the same calculations even if a web page has less than 20 descriptive images. So for example, if a web page has 8 images, S10, S15, and S20 have the same value. We also calculated the per image accuracy, the accuracy of every image individually in the test sets. There are 13 accuracy metrics in total.

\section{Experiments and results}\label{results}

\subsection{Experiments}

In our experiments, we used 6 different CNN architectures: VGG16 \cite{simonyan2014very}, DenseNet121, DenseNet169, DenseNet201 \cite{huang2017densely}, RestNet50, and InceptionRestNetV2 \cite{szegedy2017inception}. These are well known CNN architectures and used in many problems. For example, VGG16 won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2014 and ResNet won in 2015 \cite{russakovsky2015imagenet}. We also took advantage of transfer learning from the ImageNet dataset. ImageNet is a dataset containing 1000 classes and over 1.2 million natural images \cite{deng2009imagenet}.

Having built the CNN architecture, we replaced the weights with pre-trained ImageNet weights. Each architecture had different pre-trained ImageNet weights. Since we have 4 classes instead of 1000 classes, we replaced the last dense layer with a layer for 4 classes. We trained only the last two layers: last dense layer and the previous average pooling layer. We used categorical cross-entropy as the loss function, and we set up the learning rate to 0.00001. Since popular deep learning frameworks contain all used CNN architectures and their ImageNet weights for transfer learning, any researcher can easily build up our experimental setup.

During the neural network training process, validation accuracy gives only per image success. Our metrics must be applied to the results of the test sets. So we saved the network architectures on every five epochs and run our metrics on these saved architectures with test sets. Figure \ref{fig:metrics} shows the all metrics success for best model Google10 \& DenseNet169. We trained the neural networks up to 100 epochs.

\subsection{Results}\label{sec:results}

\begin{table*}[t]
    \centering
    \caption{Method accuracies and comparison with other methods}
    \label{tab:success}
    \resizebox{\textwidth}{!}{
        \begin{tabular}{lcccccc}
            \noalign{\smallskip}
            \hline
            \noalign{\smallskip}
            Train set    & Text C. & BoVW    & VGG16   & DenseNet121 & DenseNet169 & DenseNet201 \\
            \noalign{\smallskip}
            \hline
            \noalign{\smallskip}
            Page Contents& \%92.65 &    -    &    -    &    -    &    -    &    -    \\
            Screenshots  &    -    & 46.95\% & 59.60\% & 60.55\% & 68.10\% & 68.40\% \\
            \noalign{\smallskip}
            \hline
            \noalign{\smallskip}
            Subset10     &    -    & 62.50\% & 81.00\% & 95.00\% & 97.00\% & 96.50\% \\
            Subset20     &    -    & 61.50\% & 83.50\% & 94.00\% & 96.50\% & 97.50\% \\
            Google10     &    -    &    -    & 89.55\% & 93.30\% & 94.90\% & 94.35\% \\
            Google20     &    -    &    -    & 90.35\% & 93.00\% & 94.75\% & 94.45\% \\
            \noalign{\smallskip}
            \hline
        \end{tabular}
    }
\end{table*}

The chosen CNN architectures gave different accuracies since they had different characteristics in analyzing the input images. With VGG16, we get reasonable results, but DenseNets are better than VGG16. DenseNet169 and DenseNet201 have maximum success rates. Validation accuracy and loss of RestNet50 and InceptionRestNetV2 were not stable even with a small learning rate, so we excluded them from our results. We used these architectures with their trained models with ImageNet dataset. We can say that trained model of VGG16 is not good enough for our dataset. Both ResNets and DenseNets are successful on extracting features from ImageNet dataset. But we used Google Image Search thumbnails images on our models and there were two major differences between our dataset and ImageNet. Firstly our dataset consisted of relatively small images, most of them have less width or height than the expected input of the CNN architecture's default value, which is 224 pixels. Secondly ImageNet focused on objects, and in our dataset there were pictures of people, landscapes, events and designs. Densenets had been shown to be successful in this regard.

The chosen CNN architectures gave different accuracies since they had different characteristics in analyzing the input images. With VGG16, we get reasonable results, but DenseNets are better than VGG16. DenseNet169 and DenseNet201 have maximum success rates. RestNet50 and InceptionRestNetV2 have high success rates, but validation accuracy and loss are not stable even with a small learning rate. This may be observed because of the inputs that we had used. Google Image Search thumbnail images are small, and most of them have less width or height than the expected input of the CNN model's default value, which is 224 pixels. RestNet50 and InceptionRestNetV2 may work for other problems and/or inputs. We excluded them from our results.

This may be observed because of the inputs that we had used. Google Image Search thumbnail images are small, and most of them have less width or height than the expected input of the CNN model's default value, which is 224 pixels. RestNet50 and InceptionRestNetV2 may work for other problems and/or inputs. We excluded them from our results.

Our method's accuracies and comparison with other methods in the literature are shown in Table \ref{tab:success}. Our method values are on the last four rows and the last four columns. Page Content, Screenshots, and Bag of Visual Words (BoVW) values are accuracies of other methods. All experiments run on the WebScreenshots dataset.

Experiments with Subset10 and Subset20, which contain 2000 web sites, have a success rate of 97.50\%. But we prefer the success rate of the full dataset (Google10 and Google20) since a dataset with 20000 web sites is a better representation for the Internet. The maximum success rate of 94.90\% is obtained with DenseNet169 and Google10 descriptive images. But we observed close values with all Google10, Google20, and DenseNet169, DenseNet201 models.

\begin{figure*}[t]
    \centering
    \includegraphics[width=1.0\textwidth]{model-metrics.pdf}
    \caption{Success values of 13 accuracy metrics for the best experiment (Google10 \& DenseNet169) calculated on every 5 epochs.}
    \label{fig:metrics}
\end{figure*}

\begin{figure*}[t]
    \centering
    \includegraphics[width=\textwidth]{model-acc-loss.pdf}
    \caption{Accuracy and loss graphs of the best experiment (Google10 \& DenseNet169).}
    \label{fig:accloss}
\end{figure*}

We mentioned that our method had 12 metrics (S05, S10, S15, S20, H05, H10, H15, H20, A05, A10, A15, A20). We captured the maximum accuracy with A15 metric. After the result matrix was reordered for the greater averaged column, this metric used the results of the first 15 images and discarded the least related 5 images. Even the A05 metric, which used the results of the first 5 images, had good accuracies. We have already stated that Google Image Search results are related to the search query itself, not related to the class of the web site on the search query. So with reordering, we determined the most useful images over 20 images. On Figure \ref{fig:metrics} all metrics can be seen for best experiment: Google10 set with DenseNet169 model. All metrics run in every five epochs for the web pages in the test set. The important metrics are shown by ticker lines. Accuracy and loss graphs for the best experiment are shown in Figure \ref{fig:accloss}.

The used CNN models require square inputs with the size of 244x244 pixels. If there is an image in the dataset which has a different size, it is automatically resized to 244x244 pixels. There are 380318 images in Google Image Search results, and the histogram of the width-height ratio of these images (Figure \ref{fig:whr}) shows that most of these images are far from being square. Besides this, since they are thumbnails, they are small, only 30932 of these images have width and height larger than 224 pixels. These facts show that Google Image Search results are not the best input for our CNN models. But as mentioned, Google is a reliable source to obtain descriptive images for web pages and to find better inputs could be very difficult from other sources.

\subsection{Comparisons with other methods}

To compare our method with the other methods in the literature, we applied the other methods to the WebScreenshots dataset and discussed their successes on the same dataset that we used. WebScreenshots dataset contains one screenshot for each web site and also text contents of the web sites. We used these features for other methods and Google Image Search results for our method.

Videira and Goncalves used Bag of Visual Words (BoVW) to classify the topic of web pages \cite{videira2014automatic}. They achieved 82,50\% accuracy for 4 classes, with 90 images for each class, 360 images in total. When we applied this method to 20000 images (screenshots of WebScreenshots dataset), we only get 46.95\% accuracy. Our second experiment was to use BoVW in our method instead of CNN models. We trained subsets of WebScreenshots dataset (Subset10 and Subset20) using BoVW and Google Image Search results. After applying our metrics, we could only get 62.50\% accuracy at most.

BoVW accuracy was calculated with these steps: (1) RootSIFT descriptors \cite{arandjelovic2012three} were extracted from the images. (2) K-means clustering was performed to these descriptions. (3) TF-IDF values of the clusters were calculated. (4) The model was trained with linear SVM. After training, to find the class of a test web site, we performed the same processes with its screenshot or descriptive images and compared SVM results with trained SVM clusters.

RootSIFT descriptors have 128 dimensions, and clustering on multidimensional space has problems such as the curse of dimensionality. BoVW requires too much memory and time since it processes all input images at the same time. As a result, we concluded that BoVW is not a suitable method for large datasets. We could not run BoVW with Google10 and Google20 sets since it requires extreme memory resources for 380000 images. We tried using both SIFT and RootSIFT descriptors, and RootSIFT had slightly better results than SIFT.

Dou et al. classified web pages for their aesthetics value (binary classification) with deep learning methods \cite{dou2019webthetics}. Their dataset contained 398 web page screenshots, and they calculated the correlation of their deep learning with transfer learning model and actual user ratings as more than 90\%. For WebScreenshots dataset, all classes were predefined, and there were no user predictions. With this condition, when we applied similar methods to WebScreenshots dataset (20000 web sites and screenshots), we could get 68.40\% accuracy at most. On the other hand, our method achieved 97.50\% accuracy for 2000 and 94.90\% accuracy for 20000 web pages and 4 classes.

For the web classification problem, there are numerous researches which use text classification, since web sites have more textual features than visual features. To compare our method with textual classification methods, we contented with a quite common text classification method: Term Frequency - Inverse Document Frequency (TF-IDF). TF-IDF applied to web classification problem by Salamat and Omatu \cite{selamat2003neural}. They used a neural network with 25 hidden layers. Their dataset contains 1166 sports documents on 11 sports categories (baseball, boxing, tennis, etc.). They stated their success rate as 83.94\% for the TF-IDF method.

We applied TF-IDF on the WebScreenshots dataset. We used the text contents of web sites that came with the dataset. In our experiments, we tokenized the text contents and constructed TF-IDF matrices for training and test sets. We fed these matrices into a sequential deep learning network with 5 hidden layers. After trying different values for the vocabulary size parameter, we experienced a maximum 92.65\% success rate for TF-IDF on the WebScreenshots dataset. Adding more layers did not impact the result. Our descriptive images method had a higher success rate, 94.90\%, than text classification with TF-IDF and gave a better result for a multilingual dataset.

\section{Conclusion}\label{sec:conclusion}

In this paper, we have presented a web site classification system based on combining multiple neural network results into one. In the model, each web site represented by its descriptive images, which are the Google Image Search results for the web site URL address. A classification rate of 94.90\% was obtained when the model applied on a dataset of 20000 web sites. The main advantages of the proposed model are its mobility and its ease of application. The proposed model can be used for other problems where an element can be described with more than one feature. We used well known CNN architectures, 3 kinds of metrics, and Google Image Search results for our problem. But different or more CNN architectures, metrics, or descriptive images could be used according to the problem.

We also represent a new dataset, WebScreenshots, that contains 20000 web sites in 4 classes. The dataset contains screenshots and text contents of the web sites, and it is a multilingual dataset with 44 languages.

Since our model is a novel method, there are still some improvements to be considered for future works. We use predefined CNN architectures with transfer learning, so we had to use their default input size 224x224 pixels. Current input images could be filtered w.r.t. their weight-height ratio. In addition, the dataset can be expanded and experiments can be repeated for more classes. In this paper, we worked on a multi-class classification problem. There is a high probability that our method will perform better in binary classification problems, such as detecting adult web sites. We are also planning to improve the used CNN architectures and try new ones to get better results. At this point, we would like to mention the importance of the contributions of other researchers. As our method can be applied to different problems, we believe it will improve with each study.

\section*{Acknowledgements}

We like to thank to the authors of open source projects used in this work: Pavan Solapure for his text classifier example\footnote{https://www.opencodez.com/python/text-classification-using-keras.htm} (code revised to get maximum accuracy and to work with UTF-8 encoded files), Bikramjot Singh Hanzra for his bag of visual words example\footnote{https://github.com/bikz05/bag-of-words} (code upgraded to Python3 and RootSIFT added)

\begin{table}[th]
    \centering
    \caption{Sample result matrix for descriptive images of a web site}
    \label{tab:sampleresults}
    \small
    \begin{tabular}{l c c c c}
        \noalign{\smallskip}
        \hline
        \noalign{\smallskip}
        Image  & Machinery  & Music      & Sport      & Tourism    \\
        \noalign{\smallskip}
        \hline
        \noalign{\smallskip}
        01.jpg & 0.97641605 & 0.00005760 & 0.00185017 & 0.02167617 \\
        02.jpg & 0.99977142 & 0.00001599 & 0.00017324 & 0.00003941 \\
        03.jpg & 0.12737411 & 0.15132521 & 0.01362997 & 0.70767075 \\
        04.jpg & 0.39753565 & 0.16894254 & 0.06661761 & 0.36690423 \\
        ...    & ...        & ...        & ...        & ...        \\
        20.jpg & 0.99998045 & 0.00000056 & 0.00001787 & 0.00000108 \\
        \noalign{\smallskip}
        \hline
    \end{tabular}
\end{table}

\begin{table}[th]
    \centering
    \caption{One-hot transformation of the sample results on Table \ref{tab:sampleresults}}
    \label{tab:onehot}
    \small
    \begin{tabular}{lcccc}
        \noalign{\smallskip}
        \hline
        \noalign{\smallskip}
        Image  & Machinery  & Music      & Sport      & Tourism    \\
        \noalign{\smallskip}
        \hline
        \noalign{\smallskip}
        01.jpg & 1 & 0 & 0 & 0 \\
        02.jpg & 1 & 0 & 0 & 0 \\
        03.jpg & 0 & 0 & 0 & 1 \\
        04.jpg & 1 & 0 & 0 & 0 \\
        ...    & ... & ... & ... & ... \\
        20.jpg & 1 & 0 & 0 & 0 \\
        \noalign{\smallskip}
        \hline
    \end{tabular}
\end{table}

\begin{table}[th]
    \centering
    \caption{Average reordered transformation of the sample results on Table \ref{tab:sampleresults}}
    \label{tab:averagereordered}
    \small
    \begin{tabular}{lcccc}
        \noalign{\smallskip}
        \hline
        \noalign{\smallskip}
        Image  & Machinery  & Music      & Sport      & Tourism    \\
        \noalign{\smallskip}
        \hline
        \noalign{\smallskip}
        17.jpg & 0.99999964 & 0.00000003 & 0.00000033 & 0.00000001 \\
        16.jpg & 0.99999952 & 0.00000000 & 0.00000042 & 0.00000002 \\
        13.jpg & 0.99999654 & 0.00000323 & 0.00000011 & 0.00000008 \\
        14.jpg & 0.99999309 & 0.00000058 & 0.00000620 & 0.00000010 \\
        ...    & ...        & ...        & ...        & ...        \\
        03.jpg & 0.12737411 & 0.15132521 & 0.01362997 & 0.70767075 \\
        \noalign{\smallskip}
        \hline
    \end{tabular}
\end{table}

\section*{Conflict of interest}

The authors declare that they have no conflict of interest.

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Home > Pramonės šaka / sritis > History > Nazi Germany
The period from 1933 to 1945 in German history in which Adolf Hitler and his National Socialist German Workers' Party, commonly known as the Nazi Party were in power. Under Hitler's rule, Germany was transformed into a totalitarian state where nearly all aspects of life were controlled by the state. Nazi Germany ceased to exist after the Allied Forces defeated the Wehrmacht in May 1945, thus ending World War II in Europe.
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