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Putting my teachers aside, this college has taught me to understand, accept, and appreciate this diverse society I live in.

My fellow students have taught me how wonderful it is to be a little different. I have learned the value of an opinion and the merit it holds.

I am no longer afraid to give myself a voice. I no longer cower at criticism. I have grown into a woman who is proud of who she is and where she came from.

It bothers me when I hear rude comments or get odd looks after stating that I go to this all-girls institution.

I feel that it has been the best thing for me. Cedar Crest and its faculty teach students to be proud women and to have no fear in the face of animosity.

I feel sorry for those who laugh at our school. They obviously are not comfortable enough in their own skin to stomach a large number of confident and intelligent women all congregating at one institution.

As my third year comes to a close, I am thankful for everything Cedar Crest has given me. I am looking forward to my last year here with excitement and some sadness.

I will miss the accepting and progressive atmosphere that I have found here at school. Fred Phelps, who I wrote about in an earlier column, is coming to Philadelphia along with Repent America.

Equality Forum is taking donations as a response to their actions to show how much money these anti-hate groups can help raise for equality.

Check them out at www. For further info on this and other happenings in the Lehigh Valley check out www. Stop by and get to know him today and you can make his wishes come true.

It is a cover for one of 7. When My anxiety had reached a fever pitch by you get to the root of your anger, you are able the time we were leaving my home.

My heart to free yourself from an emotional prison. A was pounding and my mind was spinning. I false front not only shuts people out, it shuts had to practice breathing for the two-hour one in.

One woman asked how I overcame my Elizabethtown has a beautiful campus anger at my abusers, and if I had forgiven complete with a chapel by a lake.

My first them for abusing me. I cannot forthe dorms. Males make up one quarter of the give anyone. Only God, or Nature or the student population.

The Dean of Student Universal Energy can do that. I can only forAffairs remarked it was probably due to the give myself for being too small, or weak, or feminine name that male vulnerable.

My boyfriend and was abused. To understand the top bunk. Personal responsibilThe cafeteria was ity has disappeared in this amazing.

Anyone made to order pasta bar, can claim victimship and deli, grill and an enorget carte blanche on their Beth Coulter Crestiad Columnist actions.

Anything they do mous salad bar in the center of the room, not to is forgiven due to their mention the dessert bar A poster from Elizabethtown College, past and present abuse.

People must have perdinner in my honor, in a lovely alcove. There sonal responsibility for their actions. No matwere about twenty people present, including ter what, hurting another is never an approprifaculty and students, and our own Liz Ortiz ate way to express ones own pain.

After the questions were answered, we Guy was kind enough to come out to film my had a candlelight vigil and sharing session.

One of After a wonderful dinner, we headed over the most moving came from a young woman to the before-mentioned chapel on the lake.

After conferring with the by killing herself. I escaped to a little alcove. While watching the was pleased that many of the people there felt rain pour down, I was able to center myself, comfortable enough to speak with me aftercalming my heart and mind.

When asked how wards. I was pleased that they stayed, but always. I explained that I felt we were going to our new suite. After me speaking at them, therefore I needed the speech, we were given the Willy Brandt everyone relaxed.

Suite, a three-room apartment where the forI spoke for about 20 minutes, relaying the mer President of Germany had stayed during a highlights or lowlights of my life.

I talked visit, 40 years ago. It was better than I could about my suicide attempts, my diagnosis of have dreamed, truly a first class experience.

Multiple Personality Disorder and my battle I am so incredibly honored to have been for recovery.

Then I opened it up to questions, given this experience. I can only hope that I and was really able to delve deeper into my touched people enough with my story that they philosophies regarding pain, anger and per- find hope and peace for themselves.

Thanks for reading me this year. There are no gradient levels back in the fall. What hurts more, a broken toe or a broken finger?

Pain is pain and must be respected Beth www2. Kent Fitzgerald and Dr. Richard Kliman were promoted to Associate Professor with tenure, while Dr.

Kim Spiezio, Dr. Alan Hale, and Dr. Allen Richardson were promoted to Full Professor. Allen Richardson. Dorothy Gulbenkian Blaney welcomed all, followed by greetings from Board of Trustees member, Ruth Spira, and an introduction of honorees by Dr.

Carol Pulham. In his presentation he spoke of the connection between an amputation and the ghost sensation of the missing limb.

With this he spoke of the memory connection in psychology and neuroscience. History is important, Fitzgerald warned.

Blaney, Dr. Pulham and Dr. Fitzgerld listen to the presentation of a colleague. The conference, held May fourth and fifth, will also feature performances by Dr.

Alan Hale. He brought up Osama Bin Laden and how Bin Laden is truly a smart man due to the fact that he is considering the use of biological weapons.

Hale made an interesting point on how just a small amount of a disease could cause a catastrophe across the nation, ranging from the area infected to the stock market.

The conference begins with a speaker from the University of Delaware. Paper and poster sessions run from , , and Registration opens at a.

Poster Session 1 Oberkotter Center Lounge p. Lunch, TCC p. Poster Session 2 Oberkotter Center Lounge p. Poster Session 3 Oberkotter Center Lounge www2.

Full-time students must now earn a 3. Many students were curious to know how the faculty chose 3. Faculty members consulted graduation requirements; a 3.

The previous cutoff of 3. Imagine if every Olympic hopeful got to compete! There would be nothing left to strive for, no pride in making the cut.

They were upset that the bar had been raised and eliminated them from the list. Still, she seems glad that the faculty agreed to raise the bar.

She sees the new rules as distinctive between good grades and outstanding grades. Her biggest concern was for students taking an active courseload, such as the night and weekend nursing program, that falls below 12 credits.

In addition to his current responsibilities for media relations, marketing and publications, Traupman will now be the senior officer for planning, coordinating and implementing special events.

An entirely in-house agency, College Relations has its hand in nearly all aspects of the college.

Because of this, there is a consisten- cy in message and tone in all of the work that we do. And as each generation changes, our publications evolve.

But as a unit, and now under the supervision of a new Executive Director, College Relations continues to present an honest and excellent representation of what Cedar Crest College is all about.

Amanda Skelton had just won a beta fish and is choosing her favorite one! Candida Lopez is the club president standing behind the table.

NEASEA has declared that the second week of April should be set aside to recognize the value of the student employees.

According to NEASEA this week, "Is to enhance awareness of student employment and its important role in higher education experience, to recognize students who perform outstanding work while attending college, and to thank the employers who hire students, for part-time positions and make the student employment program such a success.

The event took place in Kelly McCloskey's office. Dean O'Neill and Dean Laffey were also in attendance to watch Serfass receive this award to honor her achievement.

Serfass worked in the athletic department for her four years at Cedar Crest, and she also worked hours over the summer for the athletic office.

Serfass plans to complete the Masters in education program here at Cedar Crest. When Serfass was asked how it felt to receive the award, she responded by saying, "It is a very big honor.

I did not realize it was so big of a deal. I was surprised to be chosen out of all the student workers at Cedar Crest.

The award included a plaque with Serfass' name on it, a certificate, and a savings bond. Jorie Graham continued page important for poets to work through their poems rather than trying to work around them.

English major Erica Fleming agreed. She told us that in her earlier writing, she used to write much shorter lines. I learned a lot from the reading.

When the sun comes up, she said, she realizes that she has written line on top of line and cannot read her poems. See independent dealer for details.

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With this coupon. Some semesters we have taken two teams. This semester we took only one. Teams consist of three students per team.

I serve as "coach" for the team, which makes me the contact point at the contest for the contest officials, and the one who prepares them for the competition.

We attend the MidAtlantic regional competition which consists of about schools extending from North Carolina to Pennsylvania.

I believe my teams have finished in the top half in that contest twice, and all but once have always finished in the top It is a big competition held simultaneously at 5 to 7 sites depending on the year.

This event includes some powerhouse schools including Virginia Tech, North Carolina, Duke, and Drexel to name just a few. The spring competition is a small contest that is locally organized.

I met Prof. He had the idea of organizing a local contest, and I told him I would bring one or two teams, which I did that following spring.

It has been an annual event since. One of the purposes of these small competitions is to give students more competition experience.

To my recollection, Dickinson, Messiah, Lebanon Valley, Shippensburg, and Cedar Crest have attended this contest every year it was held.

There have always been two or three other schools each year as well. This year was Cedar Crest's best finish in this event - 2nd place.

Two years Cedar Crest had its best finish to that time - 4th. Our first year attending, with two teams, we finished 12th and 13th out of An interesting thing about these contests is that they are largely male dominated as is the computer science field.

There are very few female competitors, and an all-female team is quite rare. At this contest there were 12 teams, 36 competitors.

There were only 6 women competing, half of which were the Cedar Crest team. This is typical of the number of women competing at these contests.

The three members of this year's team are all seniors. Vanessa has competed in two contests prior to this one. Marjorie and Heather have competed in probably three or four each prior to this one.

They are all very good programmers, and have really matured in the way they approach these competitions. Younger teams will tend to work together on one problem at a time - sort of like working as one.

These three have reach a point where the know their individual strengths, and trust the others on the team to apply their individual strengths.

As such, they will break out parts of problems, or entire problems, and work on them individually.

Then, come together to complete the problem. They are doing some of the work in parallel, which I think you really need to do to be successful in these competitions.

When the students compete, they use one single workstation. The Cedar Crest team finished in second place.

Twelve teams from seven schools participated in the competition. For the past five years, Cedar Crest has participated in two competitions per year, with this being the best finish in the team's history.

In the contest, the teams were presented with a set of six problems of varying difficulty. The team solving the most problems during the four-hour contest wins.

The Cedar Crest team correctly solved four of the six problems, as did the winner, Lebanon Valley College. The total time taken for each problem is used as a tie-breaker.

Third place went to Hood College with best time to solve three problems. Problems must be solved by writing computer programs.

This room is equipped with Linux workstations, which is the operating environment in which they compete. Take Back the Night continued page 1 Generally being fearful of them did not place you out of character and the support from the guest speakers, the students and the faculty was overwhelming.

Being a victim of domestic violence is nothing to be ashamed about, but knowing how to protect yourself and being informed on what to do if you are attacked are some of the first steps in accepting the fact that violence is out there and it always will be.

This event was a small step towards worldwide prevention and eradication of something that should know longer instill fear in the lives of all women.

People outside of Ethiopian often think of famine, of war, of drought and don't realize the wealth of heritage that this country does have.

The obelisk was constructed around B. It was disassembled once again to transport it by plane back to Ethiopia the mid-section of the obelisk was transported first, and the top and bottom will follow in the next few days.

Ethiopian Abebe Alenayehu, 81, who watched the Romans confiscate the obelisk so many years ago, commented to the Associated Press news agency, "The memory still leaves a bitter taste in my mouth.

Every day for the last 67 years I have thought about the obelisk. Perhaps though, the growing sentiment for returned national treasures will convince Britain to have a change of heart.

The sentiment against looting has spurred many ethical questions, some even stemming from a shipwreck two miles under the sea.

Is it okay to take artifacts from a shipwreck, to take from the dead? Is it okay to take a religious artifact from a warring country?

And where should we draw the line? Perhaps with the return of this obelisk and the creation of a UN committee that looks to return looted treasures to their original countries, a new era can be spurred, piecing back countries missing a part of their heritage.

And maybe, just maybe, the sacredness of different artifacts and their meaning to the deep history and heritage of the country will finally be recognized.

Linda Misiura Photo Editor Students march around the campus in a silent vigil with lit candles. Here are just a few highlights. The first ever specially funded alumnae award was awarded to Becky Cornelious, a social work major who expects to graduate in Cornelious, a single parent with a full time job, described in her application how she coordinated a fundraiser for Thanksgiving baskets to be sent to soldiers in her army unit.

According to Susan Seccombe Cox, Executive Director of Alumnae Affairs, the ring scholarship began last year in response to the drop in the number of students purchasing rings and participating in the Junior Ring Ceremony.

Cox also hopes that there will be two awards given next year, in hopes that other alumnae like Zimmerer will want to fund a ring award.

Galbreath found out about the ring scholarship from flyers posted around the residence halls and on campus. According to Galbreath, who is originally from Dover, Delaware, the financial strain of trying to purchase the ring in her junior year combined with finances for school contributed to her applying for the scholarship.

The application required that the candidates supply a list of activities in the community and at Cedar Crest, a short paragraph describing what characteristics they have in common with Connie Parkes Washburne, what Cedar Crest College and the ring mean to them, and finally, what makes them the best candidate for the Washburne Ring Award.

The application supplied information about Washburne to allow the candidates to get to know the person who would be giving such an important gift to one of them.

She was an active and generous alumna and a tribute to her alma mater and those who knew and loved her.

Cox, who personally called Galbreath to tell her that she was chosen for the award, also helped the recipients make contact with Cyndi Taubler, the Alumnae Affairs administrator, in order to choose all of the details for their rings.

It is time to pack up our stuff and move ourselves home for the summer. While looking forward to lazy days on the beach or summer jobs, we will miss our friends and neighbors here at school.

Although we have worked hard and stressed over exams and papers, we will miss those who have shared the experience with us. We will say goodbye these close friends for the summer.

This transition is often hard because we have changed as people and we will be moving back into an atmosphere that remains unchanged: home.

The first step to moving back is collecting our belongings. It would be a good idea to start about two to three weeks before moving day.

Start packing your winter clothes and stuff you will not be using in the last few weeks. At least start collecting boxes or bags in which you will use to carry your clothes and such.

Saying goodbye to your friends would have to be the next step. For most of us, we will see each other come next August.

Some of us are going on to better lives in the real world of day jobs. Make a point to exchange numbers, addresses, and email addresses if you have not already done so.

This helps you to reach closure if you are graduating, and it gives you a sense of fulfillment to move on to the next step in your life.

For those of us who have at least one more year here left, we will be moving home for just three months.

And, thankfully, density plots are just as easy to generate in R as histograms. In Figure , we create our first density plot of the height data: ggplot heights.

Here the density plot suggests that the data is suspiciously flat at the peak value. Here we use the gender of each point to split up our data into two parts.

Next, in Figure we create a density plot in which there are two densities that get superimposed, but are colored in differently to indicate the gender they represent: ggplot heights.

We might expect to see the same bell curve structure in the weights for both genders. In Figure , we make a new density plot for the weights column of our data set: Figure We can easily see an example of the normal distribution by simply splitting up the plot into two pieces, called facets.

In R, we can build this sort of faceted plot as follows: ggplot heights. But the normal distribution is very important in the Figure On a more abstract level, a normal distribution is just a type of bell curve.

It might be any of the bell curves shown in Figures through In these graphs, two parameters vary: the mean of the distribution, which determines the center of the bell curve, and the variance of the distribution, which determines the width of the bell curve.

You should play around with visualizing various versions of the bell curve by playing with the parameters in the following code until you feel comfortable with how the bell curve looks.

To do that, play with the values of m and s in the code shown here: Figure As you can see from Figures through , the exact shape of the curves will vary, but their overall shape is consistent.

But the mode has a clear visual interpretation: when you make a density plot, the mode of the data is the peak of the bell.

For an example, look at Figure Estimating modes visually is much easier to do with a density plot than with a histo- gram, which is one of the reasons we prefer density plots over histograms.

And modes make sense almost immediately when you look at density plots, whereas they often make very little sense if you try to work with the numbers directly.

In contrast, a graph like the one shown in Figure has two modes, and the graph in Figure has three modes. Figures and show images of symmetric and skewed data to make these terms clear.

A symmetric distribution has the same shape whether you move to the left of the mode or to the right of the mode.

The normal distribution has this property, which tells us Figure In contrast, another bell-shaped distribution called the Cauchy distribution produces Figure The canonical images that are usually used to explain this distinction between the thin- tailed normal and the heavy-tailed Cauchy are shown in Figures and R makes this quite easy, so you should try the following: Figure Normal distribution with its mode highlighted Exploratory Data Visualization 55 set.

The Cauchy is unimodal and sym- metric, and it has a bell shape with heavy tails. After the normal distribution, there are two more canonical images we want to show you before we bring this section on density plots to a close: a mildly skewed distribution called the gamma and a very skewed distribution called the exponential.

Mixture of three normal distributions with three modes highlighted Exploratory Data Visualization 57 gamma. As you can see, the gamma distribution is skewed to the right, which means that the median and the mean can sometimes be quite different.

This real data set looks remarkably like data that could have been produced by a the- oretical gamma distribution. When we describe how to use stochastic optimization tools near the end of this book, having an all-positive distribution will come in very handy.

An example data set drawn from the exponential distribution is shown in Figure This distribution comes up quite a lot when the most frequent value in your data set is zero and only Figure Skewed distribution Exploratory Data Visualization 59 positive values can ever occur.

For example, corporate call centers often find that the length of time between the calls they receive looks like an exponential distribution.

For right now, what you really take away from this section are the simple qual- itative terms that you can use to describe your data to others: unimodal versus multimodal, symmetric versus skewed, and thin-tailed versus heavy-tailed.

This is clearly worth doing: often just seeing a familiar shape in your data tells you a lot about your data.

To do real machine learning, we need Figure Facetted plot of heavy-tailed Cauchy and thin-tailed Normal Visualizing the Relationships Between Columns 61 to find relationships between multiple columns in our data and use those relationships to make sense of our data and to predict things about the future.

The first is the stereotypical regression picture. This is intuitively obvious, but de- scribing general strategies for finding these sorts of patterns will take up the rest of this book.

In this case, the predictions are simply a line, which is shown in blue. As you get more data, these guesses become more accurate and the shaded region shrinks.

Because we already used all of the data, the best way to see this effect is to go in the opposite Figure Scatterplot of heights versus weights Visualizing the Relationships Between Columns 65 direction: remove some of our data, and see how the pattern gets weaker and weaker.

The results are shown in Figures through For classification, Figure is the image you should keep in mind. That makes it clear that we see two distinct groups of people in our data.

To generate this image in ggplot2, we run the following code: ggplot heights. In the classification picture, we make a scatterplot of our data but use a third column to color in the points with different labels.

For our height and weight data, we added a third column, which is the gender Figure Scatterplot of heights versus weights with 20 observations Visualizing the Relationships Between Columns 67 of each person in our data set.

This data set just happens to be particularly easy to work with, which is why we started with it.

As you can see, you need almost no code at all to get pretty impressive results. We used heights and weights to predict whether a person was a man or a woman.

For example, imagine that your data looked like the data set shown in Example This plot might depict people who are at risk for a certain ailment and those who are not.

Above and below the black horizontal lines we might predict that a person is at risk, but inside we would predict good health.

These black lines are thus our decision boundary. Suppose that the blue dots represent healthy people and the red dots rep- resent people who suffer from a disease.

If that were the case, the two black lines would work quite well as a decision boundary for classifying people as either healthy or sick.

At the unprocessed stage, the features are simply the contents of the raw email as plain text. This raw text provides us with our first problem.

We need to transform our raw text data into a set of features that describe qualitative concepts in a quantitative way. Fortunately, the general-purpose text-mining packages available in R will do much of this work for us.

For that reason, much of this chapter will focus on building up your intuition for the types of features that people have used in the past when working with text data.

Feature generation is a major topic in current machine learning research and is still very far from being automated in a general-purpose way.

Just as learning the words of a new language builds up an intuition for what could realistically be a word, learning about the features people have used in the past builds up an intuition for what features could reasonably be helpful in the future.

Table shows the results. This sort of problem comes up quite often when you work with data that contains only a few unique values for one or more of your variables.

As this is a recurring problem, there is a standard graphical solution: we simply add random noise This or That: Binary Classification 75 to the values before we plot.

This addition of noise is called jittering, and is very easy to produce in ggplot2 see Figure Before we can proceed, we should review some basic concepts of conditional proba- bility and discuss how they relate to classifying a message based on its text.

Moving Gently into Conditional Probability At its core, text classification is a 20th-century application of the 18th-century concept of conditional probability.

A conditional probability is the likelihood of observing one thing given some other thing that we already know about.

This is something we can look up in survey results. When a word is noticeably more likely to occur in one context rather than the other, its occurrence can be diagnostic of whether a new message is spam or ham.

If you see many words that are more likely to occur in spam than ham and very few words that are more likely to occur in ham than spam, that should be strong evidence that the email as a whole is spam.

How much more likely a message needs to be to merit being labeled spam depends upon an additional piece of information: the base rate of seeing spam messages.

This base rate information is usually called the prior. When working with email, the prior comes into play because the majority of email sent is spam, which means that even weak evidence that an email is spam can be sufficient to justify labeling it as spam.

In the following section we will elaborate on this logic in some detail as we write a spam classifier. To compute the probability of an email, we will assume that the occurrence counts for every word can be estimated in isolation from all of the other words.

For- mally, this amounts to an assumption often referred to as statistical independence. Taken together, these two traits make our model a Naive Bayes classifier.

Writing Our First Bayesian Spam Classifier As we mentioned earlier in this chapter, we will be using the SpamAssassin public corpus to both train and test our classifier.

For instance, hard ham messages often include HTML tags. Recall that one way to easily identify spam is by the presence of these tags.

To more accurately classify hard ham, we will have to include more information from many more text features.

Extracting these features requires some text mining of the email files and constitutes our initial step in creating a classifier.

All of our raw email files include the headers and the message text. First, the header contains a lot of information about where this email has come from.

In fact, due to size constraints, we included only a portion of the total header in Ex- ample And despite the fact that there is a lot of useful information contained in the headers, we will not be using any of this information in our classifier.

This is not to say that one should always ignore the header or any other information. In fact, all sophisticated modern spam filters utilize information contained in email message headers, such as whether portions of it appear to have been forged, whether the message is from a known spammer, or whether there are bits missing.

Because we are focusing on only the email message body, we need to extract this text from the message files.

If you explore some of the message files contained in this exer- cise, you will notice that the email message always begins after the first full line break in the email file.

To begin building our classifier, we must first create R functions that can access the files and extract the message text by taking advantage of this text convention.

Received: from usw-sf-list1-b. Sender: spamassassin-devel-admin example. Get a new here for FREE!

As is always the case, the first thing to do is to load in the libraries we will use for this exercise. For text classification, we will be using the tm package, which stands for text mining.

Once we have built our classifier and tested it, we will use the ggplot2 package to visually analyze the results.

Another important initial step is to set the path variables for all of the email files. As mentioned, we have three types of messages: easy ham, hard ham, and spam.

In the data file directory for this exercise, you will notice that there are two separate sets of file folders for each type of message.

We will use the first set of files to train the classifier and the second set to test it. To do this, we will write a function that opens each file, finds the first line break, and returns the text below that break as a character vector with a single text element.

The function shown here takes a file path as a string and opens that file in rt mode, which stands for read as text. Also notice that the coding is latin1.

This is because many of the email messages contain non-ASCII characters, and this encoding will allow us to use these files.

The readLines function will return each line of text in the file connection as a separate element of a character vector. As such, once we have read in all of the lines, we want to locate the first empty element of the text and then extract all the elements afterward.

One approach is to create a vector containing all of the messages, such that each element of the vector is a single email. The most straightforward way to accomplish this in R is to use an apply function with our newly created get.

This is not something we want to include in our training data, so we ignore it by keeping only those files that do not have cmds as a filename.

Now spam. To create our vector of spam messages, we use the sapply function, which will apply get. Note that we have to pass an anonymous function to sapply in order to concatenate the filename with the appropriate directory path using the paste function.

This is a very common construction in R. Once you have executed this series of commands, you can use head all. You will note that the name of each vector element corresponds to the filename.

This is one of the advantages of using sapply. The next step is to create a text corpus from our vector of emails using the functions provided by the tm package.

Once we have the text represented as a corpus, we can manipulate the terms in the messages to begin building our feature set for the spam classifier.

A huge advantage of the tm package is that much of the heavy lifting needed to clean and normalize the text is hidden from view. What we will accomplish in a few lines of R code would take many lines of string processing if we had to perform these operations ourselves in a lower-level language.

One way of quantifying the frequency of terms in our spam email is to construct a term document matrix TDM.

The [i, j] cell of this matrix corre- sponds to the number of times term i was found in document j. In our case, we will construct the corpus from a vector of emails, so we will use the VectorSource function.

To see the various other source types that can be used, enter? As is often the case when working with tm, once we have loaded our source text, we will use the Corpus function in conjunction with VectorSource to create a corpus object.

Before we can proceed to creating the TDM, however, we must tell tm how we want it to clean and normalize the text.

To do this we use a control, which is a special list of options specifying how to distill the text. For this exercise we will use four options.

To see the list, type stopwords at the R console. We now have processed the spam emails to the point where we can begin building our classifier.

Specifically, we can use the TDM to build a set of training data for spam. Within the context of R, a good approach to doing this is to construct a data frame that contains all of the observed probabilities for each term, given that we know it is spam.

Just as we did with our female computer science major example, we need to train our classifier to know the probability that an email is spam, given the observation of some term.

Then, using the rowSums command, we can create a vector that contains the total frequency counts for each term across all documents.

Because we will use the data. Next, we will do some housekeeping to set the column names and convert the frequency counts back to a numeric vector.

With the next two steps, we will generate the critical training data. First, we calculate the percentage of documents in which a given term occurs.

Second, we calculate the frequency of each word within the entire corpus. We will not use the frequency in- formation for classification, but it will be useful to see how these numbers compare when we consider how certain words might be affecting our results.

In the final step, we add the spam. We have now generated the training data for spam clas- sification!

To do this, we sort spam. Note, however, that these terms are not the most frequent by raw count.

You can see this for yourself by replacing -occurrence with -frequency in the preceding statement. This is very important in terms of how we define our classifier.

How- ever, we know that not all spam messages are constructed this way. As such, a better approach is to define the conditional probability of a message being spam based on how many messages contain the term.

Writing Our First Bayesian Spam Classifier 83 Now that we have the spam training data, we need to balance it with the ham training data.

As part of the exercise, we will build this training data using only the easy ham messages. Of course, it would be possible to incorporate the hard ham messages into the training set; in fact, that would be advisable if we were building a production system.

We will construct the ham training data in exactly the same way we did the spam, and therefore we will not reprint those commands here.

You may note that there are actually 2, ham emails in this directory. So why are we ignoring four-fifths of the data?

When we construct our first classifier, we will assume that each message has an equal probability of being ham or spam.

As such, it is good practice to ensure that our training data reflects our assumptions. We only have spam messages, so we will limit or ham training set to messages as well.

R file for this chapter. Once the ham training data has been constructed, we can inspect it just as we did the spam for comparison: head easyham.

Already we can begin to see how this variation will allow us to separate spam from ham. If a message contains just one or two terms strongly associated with spam, it will take a lot of nonspam words for the message to be classified as ham.

With both training sets defined, we are now ready to complete our classifier and test it! Fortunately, we have already created most of the functions and generated the data needed to perform this calculation.

Before we can proceed, however, there is one critical complication that we must consider. We need to decide how to handle terms in new emails that match terms in our training set and how to handle terms that do not match terms in our training set see Figure To calculate the probability that an email message is spam or ham, we will need to find the terms that are common between the training data and the message in question.

This is fairly straightforward, but what do we do with the terms from the email being classified that are not in our training data?

To calculate the conditional probability of a message, we combine the probabilities of each term in the training data by taking their product.

For example, if the frequency of seeing html in a spam message is 0. But for those terms in the email that are not in our training data, we have no information about their frequency in either spam or ham messages.

One possible solution would be to assume that because we have not seen a term yet, its probability of occurring in a certain class is zero.

This, however, is very misguided. First, it is foolish to assume that we will never see a term in the entire universe of spam and ham simply because we have not yet seen it.

Moreover, because we calculate con- ditional probabilities using products, if we assigned a zero probability to terms not in our training data, elementary arithmetic tells us that we would calculate zero as the probability of most messages, because we would be multiplying all the other proba- bilities by zero every time we encountered an unknown term.

This would cause catastrophic results for our classifier because many, or even all, messages would be incorrectly assigned a zero probability of being either spam or ham.

For our purposes, we will use a very simple rule: assign a very small probability to terms that are not in the training set.

This is, in fact, a common way of dealing with missing terms in simple text classifiers, and for our purposes it will serve just fine.

In this ex- ercise, by default we will set this probability to 0. In order to return to this problem later, however, we construct the classify.

We are using it in this example, but in others it may be too large or too small, in which case the system you build will not work at all!

We must extract the message text with get. Next, we need to find how the terms in the email message intersect with the terms in our training data, as depicted in Figure To do so, we use the intersect command, passing the terms found in the email message and those in the training data.

What will be returned are those terms in the gray shaded area of Figure The final step of the classification is to determine whether any of the words in the email message are present in the training set, and if so, we use them to calculate the probability that this message is of the class in question.

Assume for now that we are attempting to determine if this email message is spam. If that intersection is empty, then the length of msg.

The result will be a tiny prob- ability of assigning the spam label to the email. Conversely, if this intersection is not empty, we need to find those terms from the email in our training data and look up their occurrence probabilities.

We use these element positions to return the corresponding prob- abilities from the occurrence column, and return those values to match.

We then calculate the product of these values and combine it with our prior belief about the email being spam with the term probabilities and the probabilities of any missing terms.

The result is our Bayesian estimate for the probability that a message is spam given the matching terms in our training data.

As an initial test, we will use our training data from the spam and easy ham messages to classify hard ham emails. We know that all of these emails are ham, so ideally our classifier will assign a higher probability of being ham to all of these messages.

The vectors hardham. We then use the ifelse command to compare the probabilities in each vector. If the value in hardham.

Finally, we use the summary command to inspect the results, listed in Table That is, about one-quarter of the hard ham emails are incorrectly identified as spam.

You may think this is poor performance, and in production we would not want to offer an email platform with these results, but considering how simple our classifier is, it is doing quite well.

Of course, a better test is to see how the classifier performs against not only hard ham, but also easy ham and spam.

Testing the Classifier Against All Email Types The first step is to build a simple function that will do the the probability comparison we did in the previous section all at once for all emails.

If the probability that a mes- sage is spam is greater than its probability of being ham, it returns one; otherwise, it returns zero.

As a final step in this exercise, we will test the second sets of spam, easy ham, and hard ham using our simple classifier.

These steps proceed exactly as they did in previous sections: wrapping the spam. R file starting at line to see how this is done. The new data frame contains the likelihoods of being either spam or ham, the classifi- cation, and the email type for each message in all three data sets.

The new data set is called class. SPAM Pr. HAM Class Type 1 2. Because we have three types of email being classified into two types, our confusion matrix will have three rows and two columns as shown in Table The columns will be the percent predicted as ham or spam, and if our classifier works perfectly, the columns will read [1,1,0] and [0,0,1], respectively.

To get a better sense of how our classifier fared, we can plot the results using a scatterplot, with the predicted probabilities of being ham on the x-axis and spam on the y-axis.

Processing strategy for terms in new emails Figure shows this scatterplot in log-log scale. A log transformation is done because many of the predicted probabilities are very tiny, while others are not.

With this high degree of variance, it is difficult to compare the results directly. Taking logs is a simple way of altering the visual scale to more easily compare values.

All dots above the black diagonal line, therefore, should be spam, and all those below should be ham. Figure also gives some intuition about how the classifier is underperforming with respect to false-positives.

There appear to be two general ways it is failing. First, there are many hard ham messages that have a positive probability of being spam but a near-zero probability of being ham.

These are the points pressed up against the y-axis. Second, there are both easy and hard ham messages that have a much higher relative probability of being ham.

Both of these observations may indicate a weak training data set for ham emails, as there are clearly many more terms that should be associated with ham that currently are not.

Improving the Results In this chapter we have introduced the idea of text classification. To do this, we con- structed a very simple Bayesian classifier using a minimal number of assumptions and features.

At its core, this type of classification is an application of classic conditional probability theory in a contemporary context.

Despite the fact that we trained our classifier with only a fraction of the total available data, this simple method performed reasonably well.

That said, the false-positive and false-negative rates found in the test data are far too high for any production spam filter.

For example, our approach assumes a priori that each email has an equal probability of being ham or spam. One way we might improve results, then, would be to simply alter our prior beliefs to reflect this fact and recalculate the predicted probabilities.

We could rerun the classifier now and compare the results, and we encourage you to do so. These new assumptions, however, violate the distributions of ham and spam messages in our training data.

To be more accurate, we should go back and retrain the classifier with the complete easy ham data set. Recall that we limited our original ham training data to only the first messages so that our training data would reflect the Bayesian assumptions.

In that vein, we must incorporate the full data set in order to reflect our new assumptions. When we rerun the classifier with the new easyham.

What is interesting, however, is that by improving performance in this way, our false-negative results suffer. In essence, what we are doing is moving the decision boundaries recall Example By doing so, we are explicitly trading off false positives for improvement in false negatives.

This is an excellent example of why model speci- fication is critical, and how each assumption and feature choice can affect all results.

As we mentioned at the outset, often it is difficult or impossible to classify observations based on a single decision boundary.

In the next chapter we will explore how to rank emails based on features that are as- sociated with higher priority.

What remain important as we increase the spectrum of classification tasks are the features we decide to include in our model. In many cases, we will be satisfied with an approach that can make such a distinction.

But what if the items in one class are not created equally and we want to rank the items within a class? In short, what if we want to say that one email is the most spammy and another is the second most spammy, or we want to distinguish among them in some other meaningful way?

This is a very common problem in machine learning, and it will be the focus of this chapter. Generating rules for ranking a list of items is an increasingly common task in machine learning, yet you may not have thought of it in these terms.

More likely, you have heard of something like a recommendation system, which implicitly produces a ranking of products.

Some of the most successful ecommerce websites have benefited from leveraging data on their users to generate recommendations for other products their users might be interested in.

For example, if you have ever shopped at Amazon. The problem Amazon faces is simple: what items in their inventory are you most likely to buy?

Likewise, Netflix. In order for those customers to get the most out of the site, Netflix employs a sophisticated recommen- dation system to present people with rental suggestions.

For both companies, these recommendations are based on two kinds of data. First, there is the data pertaining to the inventory itself.

For Amazon, if the product is a television, this data might contain the type e. For Netflix, this data might be the genre of a film, its cast, director, running time, etc.

Second, there is the data related to the browsing and purchasing behavior of the customers. This sort of data can help Amazon understand what acces- sories most people look for when shopping for a new plasma TV and can help Netflix understand which romantic comedies George A.

Romero fans most often rent. For both types of data, the features are well identified. Because we usually have explicit examples of the outputs of interest when doing ranking, this is a type of machine learning problem that is often called supervised learning.

This is in contrast to unsupervised learning, where there are no pre-existing examples of the outputs when we start working with the data.

To better understand the differ- ence, think of supervised learning as a process of learning through instruction.

For example, if you want to teach someone how to bake a cherry pie, you might hand him a recipe and then let him taste the pie that results.

After seeing how the result tastes, he might decide to adjust the ingredients a bit. Having a record of the ingredients he has used i.

Indeed, a common form of unsupervised learning is clustering, where we want to assign items to a fixed number of groups based on commonalities or differences.

If you have already read and worked through the exercise in Chapter 3, then you have already solved a supervised learning problem. For spam classification, we knew the terms associated with spam and ham messages, and we trained our classifier using that recipe.

That was a very simple problem, and so we were able to obtain relatively good classification results using a feature set with only a single element: email message terms.

For ranking, however, we need to assign a unique weight to each item to stratify them in a finer way. First, it is a transaction-based medium.

People send and receive messages over time. As such, in order to determine the importance of an email, we need to focus on the transactions themselves.

Unlike the spam classification task, where we could use static information from all emails to determine their type, to rank emails by importance we must focus on the dynamics of the in- and out-bound transactions.

Specifically, we want to make a determination as to the likelihood a person will interact with a new email once it has been received.

Put differently, given the set of features we have chosen to study, how likely is the reader to perform an action on this email in the immediate future?

The critical new dimension that this problem incorporates is time. In a transaction- based context, in order to rank things by importance, we need to have some concept of time.

A natural way to use time to determine the importance of an email is to measure how long it takes a user to perform some action on an email.

The shorter the average time it takes a user to perform some action on an email, given its set of features, the more important emails of that type may be.

The implicit assumption in this model is that more important emails will be acted on sooner than less important emails. ACHHRA has been active in supporting Federal and State government agencies in managing environmental health risks, through participation in expert advisory panels, preparation of risk assessment guidance documents and peer review of reports.

They also study the impact of environmental conditions on infectious diseases. There is a particular focus on assessing and managing the health impacts of microbial pathogens in conventional water supplies and alternative water sources such as rainwater, greywater, and recycled water.

The Unit is also involved in updating the national water guidelines and in the research program of the national research organisation Water Quality Research Australia.

The Centre coordinates and conducts comprehensive research programs including observational studies, feasibility projects and large multi-centred, interventional, randomised controlled trials.

It has the ability to support small, medium and large clinical trials. The goals of the group include providing high quality research support to existing, new and emerging multicentre studies in anaesthesia, perioperative and pain medicine.

These registries play an important role in the health system and involve high-level interaction with senior industry, government, medical specialists and other professional and academic personnel.

This collaboration has been pivotal to the design and progress of pioneering transfusion research in Australia. TORC has also collaborated in data linkage activities with other large established clinical registries to better understand transfusion practice in trauma, cardiac surgery and ICU and develop models to predict and monitor blood use.

In collaboration with the Epidemiological Modelling and Infectious Diseases Units, TORC is developing a model of clinical demand for blood products, which will enable the study of the impact of major blood shortages, such as during disasters or pandemics.

New activities include the establishment of a registry for haemoglobinopathies such as thalassaemia and sickle cell disease and exploration of the implementation of new national patient blood management guidelines.

The TRU also manages other blood-related activities such as the venous thromboembolism cohort study, and the myeloma and related disorders registry.

The unit has a number of programs including healthy lifestyles, obesity, PCOS and reproductive health, indigenous health, menopause and midlife health, evidence synthesis and guidelines, diabetes and cardiovascular disease, and a clinical trials program.

Translation of research to a range of stakeholders including researchers, clinicians, consumers and policy makers is fundamental to the activities of this unit and is facilitated through links to the NHMRC and NGOs including Jean Hailes.

The unit frequently collaborates with other institutions and hospital departments such as podiatry, infectious diseases, vascular medicine, dietetics, and the emergency departments within Southern Health to promote a multidisciplinary approach to clinical research that can be translated to clinical practice with the aim of improving health outcomes for patients.

Monash Health is committed to evidence-based decision-making and the Centre for Clinical Effectiveness encourages and supports health professionals, managers and policy makers to use the best available evidence to improve healthcare.

CCE provides information, expertise and resources to assist health service personnel in obtaining evidence from research and incorporating it into everyday clinical practice and evaluating the effect on patient care.

This involves using evidence-based practice, evidence-based decision-making and evidence-based change processes. It specifically provides advice on study design, grant writing, power calculations and statistical analysis.

Associate Professor Sophia Zoungas This service has provided over hours of consulting time in the last 12 months to more than 40 clinical research groups based at Southern Health and supports the deliberations of the Human Research and Ethics Committee.

These support programs are expanding into a Centre to support clinical applied research at Southern Health.

A wide range of organisations require people to analyse health data, plan for health needs, and deliver population health programs.

Bachelor of Health Science provides knowledge of the health sector and the factors influencing health, as well as skills in information management and analysis, program development, delivery and evaluation.

The course is a generalist three year degree in health, suitable for a range of students, including those interested in health but unsure of their career pathway, those interested in health promotion and health research careers and those who are using the course as a pathway to clinical roles.

Many graduates go on to further study in health, for example by doing an Honours year with our School in order to develop research skills, or undertaking studies for specific clinical roles such as nursing.

In , we ran the full program of the new Bachelor of Health Science BHSc for the first time, with 28 units running across Semesters 1 and 2, including five third year units which ran for the first time.

While most of the teaching staff moved from Caulfield Campus to the Alfred, we have retained a suite of offices and a reception desk at Caulfield campus, where students come into contact with staff.

An increasing number of academic staff from across the School are now teaching within the program. Bachelor of Health Science Students We experienced the largest ever number of student enrolments in An increasing number of students in other degrees are also undertaking Health Science units, including those enrolled in Nursing, Biomedical Science and other courses within the Faculty 36 students and from outside the Faculty 36 students.

We will follow the progress of these graduates with interest. Many have progressed to further study in our own Honours program, and in a range of health related and other programs.

In the Honours degree of Bachelor of Health Science BHSc Hons which commenced in was relocated from the Caulfield campus to the Alfred precinct, and course content was revised and enhanced.

This move allows students to more readily engage with the very active research environment within the Alfred precinct, and to be located near their supervisors.

Six students undertook the program in , and all successfully completed their Honours degree, with some excellent results obtained.

Enrolments have almost doubled in , consolidating the degree and its significance as a pathway to research careers.

In , Honours students undertook projects supervised by a range of SPHPM and other academic staff, in areas including pulmonary rehabilitation, health promotion program evaluation, access to reproductive health services for women with special needs, and the mental health of prisoners.

A range of projects are available to students in , drawing on the wide research interests represented in SPHPM. Ame is involved in a volunteer organisation, Friends of Baguia, a community organisation that supports the people of Baguia in a number of local projects.

On her second volunteer trip to Timor Leste, Ame helped install water tanks to provide clean drinking water, supported a computer skills training program and taught classes about malaria.

I have acquired a broad range of skills which I can use when I enter the workforce. Dr Helen Ackland coordinates the Health, Knowledge and Society unit in the first semester of the first year and Professor Robin Bell coordinates the Population Health Unit in second semester of the first year.

Ms Penny Robinson coordinates the tutors for the Population Health unit as well as contributing to the lecture program and curriculum development.

Community Based Practice and Health Promotion Second year students at Clayton and Sunway undertake a unit in the community based practice program on health promotion.

This unit introduces students to community based organizations and is their first contact with patients and clients where they learn about their professional role.

A key component in this unit is learning about the place of health promotion in modern health care. Students undertake a group research project with the community based organization.

The Chris Silagy Award is presented each year for the best project. The aim of this program is to provide background knowledge and understanding about health systems and policy issues that affect clinical practice.

Issues covered include the coordination of clinical care through mechanisms such as guidelines and pathways, team work and case management, patient safety, risk management, clinical governance, accountability and strategies to change clinical practice.

Teaching materials are presented online and weekly discussion groups are moderated by senior medical administrators and practitioners Occupational and Environmental Medicine Dr David Goddard and Dr Andrea James coordinate the unit in occupational and environmental medicine in the second semester of Year 3 and Year B of the graduate entry medical course.

The unit aims to equip students with the skills to identify, appraise and integrate the best available evidence to their clinical training and practice.

It covers ten key areas of law relevant to clinical practice and uses over 60 medical lawyers, clinicians to deliver small group tutorials to students in most years of the two medical degrees.

In Associate Professor David Ranson took over the coordination of the medical law tutorial programs. A number of changes to the teaching methods were initiated to update the program including the gradual introduction of on-line methods and more content in the latter years of the medical degrees.

The School hosted 3rd year BBiomedSc students for short-term rotations and research placements, providing exposure to public health and applied clinical research, and to public health practice, learning how health is maintained and how illness is controlled at local, regional and national levels.

Elements of Forensic Medicine For 23 years, the Department of Forensic Medicine and the Faculty of Law have jointly offered teaching to undergraduate law students in the Elements of Forensic Medicine unit, a unique option in the Law curriculum.

Each year Emeritus Professor Louis Waller and Professor Stephen Cordner provide current and relevant information to students using a variety of sources.

The unit continued to be popular with 44 students enrolled in Each year the Victorian Institute of Forensic Medicine Prize for Elements of Forensic Medicine, which is sponsored by the Department of Forensic Medicine, is awarded to the student who achieves the highest mark.

The prize will be announced in Each of the students worked with a senior researcher within the school to undertake a small research project or contribute to current research in applied clinical and public health research.

This year they also spent time with ambulance staff and observed the pressures faced by the pre-hospital and emergency teams in their day to day work.

The overall consensus was that the program provided students with a better idea of the work involved in establishing and completing a research project, as well as honing their statistical and applied research skills.

They were also able to meet with physicians doing both research and clinical practice which demonstrated to students that there are many career options within medicine.

The highlight for many of the students was completing a research paper and being listed as a co-author of a published work. What we hope will be the end resolution, Is maintaining ventricular constitution.

Patients must have a big heart attack, With the left ventricle having copped a fair whack! Recruitment criteria is strict and specific, Troponin rise, ST changes, the list is not terrific!

But to make it happen and chase this dream, Who else better than the Clin Pharm team? Heart attacks can have grave complications, With infarcts upsetting important foundations.

The heart tries to pump with increased vigour, But alas, the ventricle just keeps getting bigger.

But what can be done to stop this outcome, To which the heart will eventually succumb? With patients and doctors all in dismay, Along comes seaweed to save the day!

By forming a structure around the ventricle, The seaweed helps keep the heart symmetrical. Provided we can see the heart silhouette, It is injected within five days of on-set.

It starts off a liquid and becomes a gel, Calcium is what converts it, in a heart-muscle cell. The study is being conducted world-wide, And thankfully no one is yet to have died.

To be involved in this study, I am very grateful, And of promising results, I am very faithful. It especially focuses on developing skills in the quantitative methods of the population-based health sciences and their problem solving application for primary care provision both in Australia and developing countries.

The majority of specialisations offered courses as Graduate Certificate, Graduate Diploma or a Masters program. The department taught over 80 units, mainly in an off campus mode i.

This program, catering for medical and dental practitioners working or proposing to work in the field of forensic medicine, is the only course of its kind in the English speaking world.

The course fills a unique niche in the educational domain of clinical forensic medicine and enables forensic medical practitioners and forensic odontologists to practice within the framework of ethical, medical and legal principles, standards and rules.

To position his organisation as a value added provider to government, he realised that it was vital to conduct epidemiology research to enhance service quality.

Looking at the various offerings, he chose the Monash MPH because of its reputation as a top tier course which teaches the evidence based quantitative skills required to better interface with government and evaluate public health delivery outcomes.

I believe there is a tremendous opportunity for managers and leaders to have a much more rigorous basis for their management and professional practice and the MPH is a fantastic way to get that.

He now feels better able to engage with practitioners on both a strategic and operational level as well as contribute to policy development, work with government and use evidence to evaluate service delivery efficiencies.

The evidence based and epidemiological based approach I have gained from the MPH will really help me and the industry. The program has continued to grow and attracts students from around Australia and globally.

Nineteen PhD students were examined and passed. The students are from many backgrounds, including medicine, science, physiotherapy, public health, psychology and nursing.

The progress of students is facilitated by a very strong research environment, with an extensive program of NHMRC funded research and access to extensive infrastructure.

Support includes assistance in biostatistics, data management, computing and support from experienced staff and a large student body. We also have a coordinator of Good Research Governance, who advises doctoral students on the development of protocols and adherence to strict research guidelines.

Australasian Epidemiological Association: Post graduate student travel award. Mr Matthew Page Thomas C Chalmers Award at the 20th Cochrane Colloquium for delivering the best oral presentation addressing methodological issues related to systematic reviews.

Endeavour Executive Award Systematic review and meta-analysis of available data. Non-chronic communicable diseases are a major issue, both globally and in her region, yet the research was lacking.

Looking for a way to apply my skills to help solve public health problems, I worked as a research assistant and became fascinated with statistics.

This work required Marsha to interact with public health professionals from different countries. Passionate about a career in public health, Marsha finished a second Masters degree, this time in Public Health, and decided to undertake a PhD at Monash University.

As such, a PhD can take you on another path such as business, non-government organisations or, in my case, working at either an NGO or a regional public institution.

My Monash PhD gives the credibility to take my career to a new level where I can influence public health decision making and implement changes that will help my region.

These short courses aim to provide short-term, intensive educational opportunities for those seeking professional development, or as an alternative to full-time studies.

Biostatistics for Clinical and Public Health Researchers Convenor: Dr Baki Billah This course allows students to perform statistical analyses, report their findings and interpret their results as well as critically appraise the statistical aspects of research publications in bioscience.

Ethics and Good Research Practice Convenor: Dr Liz Bishop The aim of this short course is to provide course participants with an understanding of what constitutes good practice in research and what factors make research ethical or unethical.

Health Promotion Introduction to Stata Convenors: Associate Professor Rory Wolfe and Pam Simpson This course provides an introduction to the statistical analysis software program, Stata Release 12, covering basic data management issues and popular epidemiological analyses.

This five-day course for health practitioners develops knowledge and skills for health promotion planning, strategy selection and evaluation.

Students in this course receive a detailed introduction to the methods involved in conducting a systematic review of an intervention, and enables participants to plan and commence a review of their own.

Phty, M. Intl Health, B. M Melb , Grad Dip. Phil, M. Public Sector Management, Grad. Psychology , BSc. Sci Biotech , PhD, R. Ross Bullock, pp.

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Fernandez, R. Edgley, A. Finn, J. Fisher, J. USA, pp. Fooladi, E. Francis, J. French, S. Gilbert, R. Gabbe, B. Glass, D.

Gagnon, C. Gantner, D. Garrett, C.

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