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Medium 9781449360887

2. Instances

Marc Cohen O'Reilly Media ePub

The core capability provided by Google Compute Engine is an instance, also called a virtual machine (VM). An instance is a simulated computer, running in a partitioned environment on a real server in one of Google’s data centers. From the user’s point of view, an instance behaves like a dedicated physical computer, with its own operating system, storage devices, network adapters, and so on.

Virtualization is the process of mapping a virtual machine’s environment and resources onto services provided by real hardware. The software managing the virtualization service is called a hypervisor or virtual machine manager. There are many popular virtualization systems in use today. Google  Compute Engine uses the Linux Kernel-based Virtual Machine (KVM) software.

The KVM hypervisor runs in a standard Linux environment, which means that virtualized and nonvirtualized workloads can run side by side on the same physical hardware. This eliminates the need to manage a separate pool of resources dedicated to the Compute Engine product—the same hardware and software stack used to serve Google search, Gmail, Maps, and other Google services can also provide Compute Engine virtual machines.

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Medium 9781601322395

Sentimental Analysis on Turkish Blogs via Ensemble Classifier

Robert Stahlbock, Gary M. Weiss, Mahmoud Abou-Nasr, and Hamid R. Arabnia CSREA Press PDF


Int'l Conf. Data Mining | DMIN'13 |

Sentimental Analysis on Turkish Blogs via Ensemble


Sadi Evren SEKER

Dept. of Business Administration

Istanbul Medeniyet University


Sentimental analysis on web-mined data has an increasing impact on most of the studies. Sentimental influence of any content on the web is one of the most curios questions by the content creators and publishers. In this study, we have researched the impact of the comments collected from five different web sites in Turkish with more than 2 million comments in total. The web sites are from newspapers; movie reviews, e-marketing web site and a literature web site. We mix all the comments into a single file. The comments also have a like or dislike number, which we use as ground proof of the impact of the comment, as the sentimental of the comment.

We try to correlate the text of comment and the like / dislike grade of the proof. We use three classifiers as support vector machine, k-nearest neighborhood and C4.5 decision tree classifier. On top of them, we add an ensemble classifier based on the majority voting. For the feature extraction from the text, we use the term frequency – inverse document frequency approach and limit the top most features depending on their information gain. The result of study shows that there are about 56% correlation between the blogs and comments and their like / dislike score depending on our classification model.

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Medium 9781574411904

Chapter 16: Brandon—Cerebral Palsy

Naomi Scott University of North Texas Press PDF

Chapter Sixteen

Brandon—Cerebral Palsy

One day while I sat in the reception room to get a respite from the

Texas heat in the arena, the front door opened. A beautiful lady with dark curls and a ready smile entered, pushing a wheelchair in which sat a frail teenager with his arms around a little boy perched in his lap.

Instructor Tracy Winkley1 came in from her office, greeted them and introduced herself.

“I’m Melissa Turner,” the lady replied. “This is my son Brandon

Barnette and his little brother, Nathan.”

“Hi guys,” Tracy said as Nathan slid to the floor and joined his mother on the couch. “Do you think you’d like to ride a horse, Brandon?”

“Umm, yes,” Brandon said tentatively, his eyes wide as he glanced around at his mother and brother.

“How old are you?” Tracy asked.


“My, you’re a tall fellow for your age,” she said, kneeling in front of his chair. “Can you stand on your own?”

Brandon shook his head.

“Not without a lot of help,” Turner said.

“Okay, Brandon, let’s check you over so we can see which one of our horses will suit you best,” Tracy said. She gently tugged one leg to straighten it. “Tell me when you feel this.” She repeated the process with his other leg.

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Medium 9780596005399

A. Standard JSF Tag Libraries

Hans Bergsten O'Reilly Media ePub

This appendix contains reference material for the custom action elements in the standard JSF tag libraries that you can use in JSP pages.

Each action element is described with an overview, a syntax reference, an attribute table, and an example. The syntax reference shows all supported attributes, with optional attributes embedded in square brackets ([]). Mutually exclusive attributes are separated with vertical bars (|). For attributes that accept predefined values, all values are listed separated with vertical bars; the default value (if any) is in boldface. Italics are used for attribute values that don't have a fixed set of accepted values.

The attributes table has an EL expression type column, with the values None, Any, VB, or MB. None means that a static attribute value must be used. Any means that a static value or any type of JSF EL expression can be used, including EL expressions containing any of the EL operators. VB means that the value can be a static value (unless otherwise noted) or a value binding expression, i.e., the EL subset that identifies a read/write bean property, a java.util.List or array element, a java.util.Map value, or a simple scoped variable. MB means that the value must be a method binding, with the method signature described in the description column.

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Medium 9781934009017

Chapter 3: Aligning Standards, Curriculum, and Assessment

Lisa Carter Solution Tree Press ePub

The dog lesson from the introduction is a not-so-subtle reminder of the problems we may encounter when curriculum and assessment are not properly aligned. I really like teaching the dog lesson during my training sessions, and participants seem to enjoy it. They commend me for being prepared, engaging everyone, using visuals, inserting humor into the lesson, teaching to different learning styles, and using effective teaching strategies. This great lesson, however, does not usually bring about great results on the dog test.

What if I added some new teaching innovations to the dog lesson? I could use state-of-the-art technology to teach the lesson, create a brain-compatible environment, use dynamic instructional grouping based on detailed running records—the list goes on and on. But would this impact scores on the dog test? No, because these cutting-edge instructional methods, as important as they are in our classrooms, cannot correct my content errors.

The dog lesson does not yield successful results for one simple reason: I have not aligned what I am teaching and what I am assessing. No matter how well I teach, the best the participants—all college-educated teachers and administrators—typically can do on the dog test is an average score of 50%. New innovations will not increase scores; they can only be effective if I am teaching what I am testing. And this alignment is even more critical than ever before considering the importance of assessment in the lives of today’s students.

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