Final Project : Pog Engine
This webpage contains information for a Game Engine we created as a part of the course CS 4850/5850 Building Game Engines.
Binary file
Please use this link and follow the instructions in the README file to generate the binary files.
Compilation Instructions
- For Windows:
Check TileEditor README and follow instructions
sh mingw64build.sh
python3 main.py -m ./Engine.pyd
- For Linux:
Check TileEditor README and follow instructions
python3 linuxbuild.py
python3 main.py
- For Mac:
Check TileEditor README and follow instructions
python3 macbuild.py
python3 main.py
Additional Instructions
- Navigate to the ./Engine folder.
- run the command: python3 build_tile_editor.py
- From the same folder, run the command: ./bin/tile_editor.exe tilewidth tileheight, where tilewidth and tileheight are integers representing the dimensions of a single tile
- Left click on a tile in the menu to select the tile.
- You can insert multiple tiles quickly by dragging the cursor across the screen whil holding the left mouse button down.
- You can also erase tiles by clicking the grid with the right mouse button
- Use the arrow keys to move the camera
- After you exit the tiel editor, your tilemap is automatically saved and will automatically load when the next game begins.
 We wanted to give our users flexibility to create their own type of game. The users are able to create their own classes (based on GameObject) and add (or not add) various components such as the SpriteComponent, TransformComponent, SoundComponent, etc. The user can also determine if they want their object to be an AnimateObject or a PlayerObject (which they both inherit from the GameObject class). We included a tile editor GUI to let the users quickly and easily create their tilemaps with a spritesheet. In general, we feel that we have created a well structured and flexible game engine that gives great flexibility and freedom to the users on what type of game they want to create.
Our special game feature is the gravitational fields that can be created through our game engine. We created a realistic physics engine that simulates a gravitational pull, such as planet orbit systems. We were able to do this by creating a universal force that attracts GameObjects in a circular/elliptical motion. The user can modify the gravitational radinal strength and area. Additionally, this game engine can be utilized as a simulator for planetary orbits and planetary systems. The gravitational pulls in our physics engine can simulate realistic orbits whilst allowing the user to customize and simulate various scientific experiences.
Some of the hardships we faced were creating a realistic and functional collision between the different types of GameObjects (AnimateObject and PlayerObject as well). We did this by creating callbacks to handle events from python (which the user can modify) for specific objects. Similarly, we utilized callback methodology to set up our controllers. Another hardship was initially figuring out how to create a script based game engine that is easy to program and give users great flexibility. We envisioned the Unity game engine with Component features where the users can add components at free will to create their own unique classes and GameObjects. We thought python/pybind was an easy scripting language that new users can easily utilize as well, especially with all of the methods provided through PyBind.
In general, we think that our game engine was successful. We were able to include various game engine elements and techniques into ours such as the resource manager, tile editor, gameobject components, physics engine, etc. We were able to work effectively as a team (distributing tasks and communicating constantly to update others). However, there are definitely new features that we would like to implement for the future. A feature we would like to implement is the support of 3D games. Our emphasis was on realistic orbits in 2D. We think it will be really exciting (and challenging) to create a 3D physics engine that supports 3D gravitational orbits. This will allow for a more realistic and exciting gameplay and game design.


References
Gupta, Namita T. and Vander Heiden, Jason A. and Uduman, Mohamed and Gadala-Maria, Daniel and Yaari, Gur and Kleinstein, Steven H. (2015). Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data: Table 1. In Bioinformatics, 31 (20), pp. 3356–3358. [doi:10.1093/bioinformatics/btv359][Link]
All, IGA, IGG, IGM and IGE tabs
In these tabs information on the clonal relation of transcripts can be found. To calculate clonal relation Change-O is used (Gupta et al, PMID: 26069265). Transcripts are considered clonally related if they have maximal three nucleotides difference in their CDR3 sequence and the same first V segment (as assigned by IMGT). Results are represented in a table format showing the clone size and the number of clones or sequences with this clone size. Change-O settings used are the nucleotide hamming distance substitution model with a complete distance of maximal three. For clonal assignment the first gene segments were used, and the distances were not normalized. In case of asymmetric distances, the minimal distance was used.
Overlap tab
This tab gives information on with which (sub)classe(s) each unique analyzed region (based on the exact nucleotide sequence of the analyzes region and the CDR3 nucleotide sequence) is found with. This gives information if the combination of the exact same nucleotide sequence of the analyzed region and the CDR3 sequence can be found in multiple (sub)classes.
Please note that this tab is based on all sequences before filter unique sequences and the remove duplicates based on filters are applied. In this table only sequences occuring more than once are included.


I am an international graduate student studying engineering in University of Florida. Before came in US, I spend 4 years in Wuhan University in China and earned my Bachelor degree there.
I enjoying play video games like Deus Ex, Dishonored and Overwatch. I learned programming in Pythin on Coursera and I am planning to learn iOS developement in the future.
This section includes sorting algorithms sit in the domain of $\mathcal{O}(n^2)$. They are usually easy-understanding and have acceptable performance on small data sets. But when dealing with large data sets, the $\mathcal{O}(n^2)$ algorithms are usually not desirable.
The algorithms introduced in this sections are insertion sort and bubble sort.
Insertion sort is one of the most fundamental sorting algorithm. It works like sorting
a hand of card: pick the next card and find the right location to insert it.
One advantage of insertion sort is, when the list is "almost sorted", the instrtion
sort can be very effective. This property can be applied by other sorting algorithms
like Shellsort (Wikipedia page)
The python implementation of insertion sort is:
def insertionSort(data): if data is None or data == []: return [] for i in range(1, len(data)): currdata = data[i] j = i - 1 while j >= 0 and data[j] > currdata: data[j + 1] = data[j] j -= 1 data[j + 1] = currdata return data
Selection sort is similar to insertion sort. At each iteration, it select the minimum
element from the unsorted part of the list and put it in the end of sorted part of the
list.
The python implementation of selection sort is:
def selectionSort(data): if data is None or data == []: return [] for i in range(len(data)): minidx, mindata = i, data[i] for j in range(i, len(data)): if data[j] < mindata: minidx = j mindata = data[j] if minidx >= 0: data[i], data[minidx] = data[minidx], data[i] return data
Bubble sort is also $\mathcal{O}(n^2)$ sorting algorithm.
The algorithm keeps comparing the neighbored elements,
if they are out of order then swap them. Although it has same time complexity as
previous two algorithms, bubble sort is usually much slower
than the previous two algorithms because it only moves elements one step at a time.
The python implementation of bubble sort is:
def bubbleSort(data): if data is None or data ==[]: return [] for i in range(len(data)): for j in range(1, len(data)): if data[j - 1] > data[j]: data[j - 1], data[j] = data[j], data[j - 1] return data
This section includes sorting algorithms sit in the domain of $\mathcal{O}(n\log{}n)$.
These algorithms usually require some brain muscle to be fully understood, but since
they have very good performance, the time investment will be worth it especially when
you are planning to sort gigantic data sets.
Sorting algorithms achieves $\mathcal{O}(n\log{}n)$ complexity usually adopted the
divide and conquer technique. And this has been proved to be the
best a comparing-based sorting algorithm can do.
The algorithms introduced in this sections are merge sort and quick sort.
Merge sort divide the input list into sublists and merge them. If the sublist
has length of 1, then the sort is complete, if the sublist has more than 1 elements,
then merge sort the left sublist and merge sort the right sublist and merge them
recursively. And the whole list is then sorted.
Merge sort is stable and can be applied when random access to the data is not allowed,
like sorting a linked list. But it also requires more memory when sorting.
The python implementation of merge sort is:
def mergeSort(data): if data is None or data == []: return [] if len(data) == 1: return data mid = len(data) // 2 a = mergeSort(data[:mid]) b = mergeSort(data[mid:]) return merge(a, b) def merge(a, b): soln = [] i = 0 j = 0 while i < len(a) and j < len(b): if a[i] <= b[j]: soln.append(a[i]) i += 1 else: soln.append(b[j]) j += 1 if i < len(a): return soln + a[i:] else: return soln + b[j:]
Quick sort adopts the divide and conquer in another way. It recursively partition the
list by selecting a pivot and swap the list so that all elements smaller than the pivot
is on the left side and elements greater than the pivot is on the right. So on and so
for, and the list is sorted.
The quick sort is not stable, but it can be implemented such that only constant extra
memory is needed.
The python implementation of quick sort is:
def quickSort(data, start = 0, end = None): if data is None or data == []: return [] if end is None: end = len(data) - 1 if start < end: pivotIdx = partition(data, start, end) quickSort(data, start, pivotIdx - 1) quickSort(data, pivotIdx + 1, end) return data def partition(data, start, end): pivot = data[start] i, j = start, start + 1 while j <= end: if data[j] <= pivot: data[i + 1], data[j] = data[j], data[i + 1] i += 1 j += 1 data[start], data[i] = data[i], data[start] return i
In this section, I am trying to give you a taste of poorly (or sometime carefully) designed sorting algorithms with terrible performance. I will use stooge sort and the well-known bogo sort as an example.
Stooge sort is usually used as an example of poorly designed sorting algorithm.
The algorithm itself is simple and but fairly inefficient. The time complexity
of stooge sort is
$$\mathcal{O}(n^{\frac{\log{3}}{\log{1.5}}})\approx\mathcal{O}(n^{2.71})$$
which is worse than bubble sort, selection sort and insertion sort.
The algorithm can be described relative easily:
Swap the first and last elements if the last one is smaller
If the length if list is greater or equal to 3, then:
Stooge sort the first two thirds of the list
Stooge sort the last two thirds of the list
Stooge sort the first two thirds of the list again
The python implementation of stooge sort is:
def stoogeSort(data, start=0, end=None): if data is None or data == []: return [] if end is None: end = len(data) - 1 if data[end] < data[start]: data[end], data[start] = data[start], data[end] if (end - start + 1) > 2: step = (end - start + 1) // 3 stoogeSort(data, start, end - step) stoogeSort(data, start + step, end) stoogeSort(data, start, end - step) return data
Bogo sort is a sorting algorithm which the complexity is solely depends on
LUCK. The algorithm it self is easy, it tries to mimic the physical
sorting a deck of card by repeatedly throwing the whole deck in the air, pick them
up and check if they are sorted.
The random
module of python
has built-in function for
shuffling a list. So the code for bogo sort can be easily implemented.
The best case complexity of bogo sort is $\mathcal{O}(n)$. This is trivial because
it is possible to shuffle a list once and it's then sorted. The worst case complexity,
unfortunately, is $\infty$, because you can always shuffle the list and get unsorted result.
And according to
Wikipedia
the average time complexity of bogo sort is $\mathcal{O}((n+1)!)$.
The python implementation of bogo sort is:
from random import shuffle def isSorted(data): for i in range(len(data) - 1): if data[i] > data[i + 1]: return False return True
def bogoSort(data): while not isSorted(data): shuffle(data) return data
To show how quickly the time complexity of bogo sort grows, I tried to bogo sort lists with different lengths 1000 times and the average time consumption for each sort are:
6 elements takes 2.62e-03 sec. 8 elements takes 1.94e-01 sec. 10 elements takes 2.06e+01 sec.
The program was running a linux box with quad-core i7 processor, bogo sort on lists with more than 10 elements for 100 times was not able to finish in 5 hours so I ended up hitting the ^c.
Of course there is one more thing!
This is a sorting algorithm that guaranteed to complete, but can be slower than
arbitrarily functions. If you are really into math, or having an insomnia, please
feel free to read the original research paper. Don't have too much fun!
Research paper: How inefficient can a sort algorithm be?


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Once On Malta(1SON Malta), is a company based in Malta the company required a website that had simplicity in mind and would highlight the companies services and various travel requirments. The main functionality of the website would serve a section where the client/s could view certain events held in Malta and apply for travel during the eventing date and communicate via an simple contact form.
The website was created using Wordpress.


Quote of the Day
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Drag the images from a folder to a selected area ...
Files are not stored on the server Weeby, you see only a preview.


§490:1-206 Presumptions. Whenever this chapter creates a "presumption" with respect to a fact, or provides that a fact is "presumed", the trier of fact shall find the existence of the fact unless evidence is introduced that supports a finding of its nonexistence. [L 2004, c 162, pt of §1]


My own nginx Test Page
Served from my own directory
This is site 1 folder content
Deployed with ansible playbook


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RotationAnimation QML Type
Animates changes in rotation values More...
| Import Statement: | import QtQuick 2.7 |
| Inherits: |
Properties
Detailed Description
RotationAnimation is a specialized PropertyAnimation that gives control over the direction of rotation during an animation.
By default, it rotates in the direction of the numerical change; a rotation from 0 to 240 will rotate 240 degrees clockwise, while a rotation from 240 to 0 will rotate 240 degrees counterclockwise. The direction property can be set to specify the direction in which the rotation should occur.
In the following example we use RotationAnimation to animate the rotation between states via the shortest path:
import QtQuick 2.0 Item { width: 300; height: 300 Rectangle { id: rect width: 150; height: 100; anchors.centerIn: parent color: "red" antialiasing: true states: State { name: "rotated" PropertyChanges { target: rect; rotation: 180 } } transitions: Transition { RotationAnimation { duration: 1000; direction: RotationAnimation.Counterclockwise } } } MouseArea { anchors.fill: parent; onClicked: rect.state = "rotated" } }
Notice the RotationAnimation did not need to set a target
value. As a convenience, when used in a transition, RotationAnimation will rotate all properties named "rotation" or "angle". You can override this by providing your own properties via properties or property.
Also, note the Rectangle will be rotated around its default transformOrigin (which is Item.Center
). To use a different transform origin, set the origin in the PropertyChanges object and apply the change at the start of the animation using PropertyAction. See the PropertyAction documentation for more details.
Like any other animation type, a RotationAnimation can be applied in a number of ways, including transitions, behaviors and property value sources. The Animation and Transitions in Qt Quick documentation shows a variety of methods for creating animations.
See also Animation and Transitions in Qt Quick and Qt Quick Examples - Animation.
Property Documentation
This property holds the direction of the rotation.
Possible values are:
- RotationAnimation.Numerical (default) - Rotate by linearly interpolating between the two numbers. A rotation from 10 to 350 will rotate 340 degrees clockwise.
- RotationAnimation.Clockwise - Rotate clockwise between the two values
- RotationAnimation.Counterclockwise - Rotate counterclockwise between the two values
- RotationAnimation.Shortest - Rotate in the direction that produces the shortest animation path. A rotation from 10 to 350 will rotate 20 degrees counterclockwise.
This property holds the starting value for the animation.
For example, the following animation is not applied until the angle
value has reached 100:
Item { states: [ // ... ] transition: Transition { RotationAnimation { properties: "angle"; from: 100; duration: 2000 } } }
If the RotationAnimation is defined within a Transition or Behavior, this value defaults to the value defined in the starting state of the Transition, or the current value of the property at the moment the Behavior is triggered.
See also Animation and Transitions in Qt Quick.
This property holds the end value for the animation..
If the RotationAnimation is defined within a Transition or Behavior, this value defaults to the value defined in the end state of the Transition, or the value of the property change that triggered the Behavior.
See also Animation and Transitions in Qt Quick.


{% extends "layout.html" %} {% block content %}
pwncheck is a project that leverages the Have I Been Pwned Pwned Passwords API to check your password against a list of over 613 million leaked passwords from known data breaches. You simply enter your password, or a password you'd like to use, and see if it's been listed in a data breach.
NOTE: Passwords are NOT stored in this list, but rather stored as a partial SHA1 hash of the original leaked password. This method ensures that the leaked passwords cannot be seen in plain text or by reversing the hashing algorithm, making it a secure way to check for known leaked passwords.
Once again, be aware that your password is NOT stored in any way by the API or by this server. Privacy is highly important and therefore this utility was developed in such a way as to ensure complete privacy on the part of the user(s).
The way pwncheck works is to generate a SHA1 hash of the password you enter. This hash is then split into 2 parts; a prefix and a suffix. The prefix is used to look up the hash stored in the Pwned Passwords API. The API only contains the suffix of the hash that gets generated on password submission, and not the full password hash. Also contained with the hash suffix is a count of the times that the submitted password has been seen in data breaches. To test it out, try entering the word 'password' in the search bar and see how many times it was discovered.
For more information check out this blog post by Troy Hunt, the author of Have I Been Pwned.
{% endblock content %}


§605-15.1 Standing. The attorney general or any bar association in this State may maintain an action for violations of section 605-14. [L 1975, c 45, pt of §2]
Case Notes
Only bar association or attorney general has standing to bring action for violation of §605-14. 501 F. Supp. 830 (1980).


Click and drag with
left mouse to rotate a layer
Click and drag with
left mouse outside cube to rotate the Rubik's Cube
Parameters (in url, e.g. ?type=solver):
stickers CROSS, FL, F2L, PLL, OLL, FULL
alg initial algorithm to perform
initscript invisible initial algorithm to perform
type generator, or solver
<param>N specific cube params, e.g. alg2=... for second cube


Flot Charts
Flot is a pure JavaScript plotting library for jQuery, with a focus on simple usage, attractive looks and interactive features. Please checkout their full documentation.
Flot is a pure JavaScript plotting library for jQuery, with a focus on simple usage, attractive looks and interactive features. Please checkout their full documentation.


Diptychs, the Ancient iPads Nov 03
An ancient iPad?
Sort of. It's a diptych – a hinged device with two wax-coated plates used for temporary notes by scribes.
The really strange thing is that wax tablets were used for writing all the way until the 19th century (for fish markets in France, apparently).
It makes sense, what other writing surface has these traits:
- requires no electricity
- wipes clean by hand
- waterproof
Modern electronics offer stunning capabilities, but can you imagine an iPad ever fulfilling all three of these design criteria? As advanced as our algorithms and electronics get, there's probably always going to be a situation that calls for a wax tablet.
While this is a bit of an exaggeration, it does illustrate that when you zoom out of the focus on consumer software and electronics, there are quite a stunning variety of demands of technological capabilities.
(from Wiki Diptych and Wax Tablet)


class: center, middle ### W4995 Applied Machine Learning # Word Embeddings 04/15/20 Andreas C. Müller ??? today we'll talk about word embeddings word embeddings are the logical next step in doing text analysis after working with topic models on Monday. FIXME move glove before paragraph vectors FIXME: all the bullet points?! FIXME: redo paragraph vector experiments FIXME more on CBOW and skip-grams? FIXME: state of the art? FIXME: word embeddings with tensorflow? FIXME for CBOW and skip-gram shared weights for context FIXME for CBOW, which do we keep? FIXME SGDClassifier screenshots FIXME steal from http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/ ? FIXME the way I do the word vectors is confusing (using inverse_transform) FIXME run through complete code for word vectors, how to deal with out-of-vocabulary words --- class: spacious # Beyond Bags of Words Limitations of bag of words: - Semantics of words not captured - Synonymous words not represented - Very distributed representation of documents ??? Last time we started talking about limitations of bag of words, such as semantics of words are not captured synonymous words are not represented - if I use two synonymous words, for example film and movie, they are represented in completely different ways and there is no connection between them. and we also have a very distributed representation of documents, which might be hard to understand or hard to learn from. --- class: spacious # Last Time - Latent Semantic Analysis - Non-negative Matrix Factorization - Latent Dirichlet Allocation - All embed documents into a continuous, corpus specific space. - Today: Embed words in a “general” space (mostly). ??? Last time we looked at latent semanic analysis, non-negative matrix factorization and latent dirichlet allocation. Each of them embedds the documents into a continuous, corpus specific space of a dimension of our choosing, using an unsupervised objective. The first two are matrix factorization, the third is a probabilistic generative model. Today we'll be talking about a more general embedding, which tries to embedd individual words, not documents. The goal is to learn an embedding that's generally useful, and not necessarily tied to a particular corpus. --- class: spacious # Idea - Unsupervised extraction of semantics using large corpus (wikipedia etc) - Input: one-hot representation of word (as in BoW). - Use auxiliary task to learn continuous representation. ??? We'll talk about several methods, but they'll all use a very large corpus to create a relatively generic embedding. So we might use something like wikipedia or google news or a big corpus of books. It's an unsupervised task, but the way it is phrased is using an auxiliary supervised task to learn the embedding. And what I mean by that will be more clear shortly. The starting principle is a one-hot representation of each word, as in Bag of words, which basically just means each word is identified by some index. --- class: spacious # Skip-Gram models - Given a word, predict surrounding word - Supervised task, each document yields many examples - Not interested in performance for this task, just want to learn representations. ??? A very commonly used auxiliary task is the skip-gram model. Here the task is, given a word, predict the surrounding words. This is a supervised task, with each document providing many examples, basically as many as there are words in the document. I'm saying this is an auxiliary task, because we're not really interested in actually solving this problem of predicting surrounding words, we just want to use this task to learn useful representations. --- class: left # Example ``` [“What is my purpose?”, “You pass the butter.”] ``` .center[ ![:scale 50%](images/context_window.png) ] Using context windows of size 1 (in practice 5 or 10): ??? Here's an example of what this task looks like. Let's say we have a corpus of two documents, "what is my purpose" and "you pass the butter". We specify a context window size, let's say one. Then, for each word that has enough context, we generate a training example. So for "is", we have a context of "what" and "my", for "my" we have "is" and "purpose", and so on. So the input would be "is" and the goal would be to predict that the word before is "what" and the word after is "my". that assigns a high probability to the outputs "what" and "my". In practice larger context windows are used, often 5-10 words in either direction. Cearly it's impossible to actually solve this task, because each word can appear in many different contexts. The goal is to learn which contexts are likely, and which are not. --- .left-column[ ![:scale 120%](images/skip_gram_net_arch.png) .smallest[http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/] ] -- .right-column[ ![:scale 100%](images/cbow_skipgram_1.png) ] ??? Here’s the architecture that we will be using for this. It's kind of like neural network only, this is completely linear. In the left-hand side, we have the input layer with one hot encoding services in V-dimensional. V represents the size of vocabulary. And basically, only one of them will be 1 and the others will be 0. Then we have a weight matrix connecting it to the n-dimensional hidden layer. From there, we have multiple outputs that correspond to the previous word, previous 2 words, and previous 3 words and so on. And then the next word, next 2 words, next 3 words and so on. Each output is, again, one hot encoding, which allows you to predict each possible version of vocabulary. These layers here are very large in size. Behind the hidden layer is basically a logistic regression. So this is just a linear classifier with a softmax activation. So each of these Ws will try to predict what the words are before and after.  The reason why you have these two matrix multiplication is basically you want to create a bottleneck so if you had a very wide vector, you could do one matrix multiplication, basically, you want this matrix to have a lower rank. So you want to compress a representation to something small. Each row of W corresponds to a single word. We train this whole thing in a supervised fashion to minimize the log loss. But we’re only interested in is getting weights w. --- #Softmax Training .center[ ![:scale 70%](images/softmax.png) ] .smallest[
Mikolov et. al. - Distributed Representations of Words and Phrases and their Compositionality (2013)
] ??? The problem is you sum over all the outputs. All the outputs are all the words in the vocabulary. If you have a million words in your vocabulary, this is going to be a lot of work. This is a step that you need to do every time when you want to optimize it. And so what most of the algorithms do is they just approximate the sum by sampling some words. So it just normalizes by some words, but not by all words. This is the model that's most commonly used. This is called skip grams. --- # CBOW vs Skip-gram .center[ ![:scale 70%](images/cbow_skipgram.png) ] .smallest[ Efficient Estimation of Word Representations in Vector Space https://arxiv.org/pdf/1301.3781.pdf ] ??? This is the skip-gram model, here you can see it on the right. So we start with the one-hot-encoding of the word, and we have a matrix multiplication into a latent space. A matrix multiplication with a one-hot vector just means that each row in that embedding matrix represents one word. From this latent space, we run logistic regression, trying to predict what the word is for each particular offset, so here in this picture we're trying to predict the word two to the left, one to the left, one to the right, and two to the right. So these are basically four classifiers running on the same input. Another approach is shown on the left, the continuous bag of words model, or CBOW. This is something that seems a bit more natural to me, where given the context, we are trying to predict the word in the center. So here we would be given four context words and try to figure out what's the most likely word to be in between those. The skip grams work better. CBOW might capture some things like synonymous words because I can switch the synonyms word for another word, and it’ll still make sense in the same context. So if I say, I watched a movie or I watched the film, it doesn't really matter. --- class: spacious # Implementations - Gensim - Word2vec - Tensorflow - fasttext - ... - Don't train yourself ??? Word2vec uses both the skip grams and continues bag of words. You can train this using TensorFlow. If you actually want to train it yourself, it's probably the best idea because then you can do it on GPU. These are usually like large matrices, you have a large corpus which will take forever. With TensorFlow, you can do it on a GPU and, and it makes things much more bearable. But generally, just don't train it yourself. You can download several of these word2vec models from Google, for example. --- # Quick intro to spaCy Comes with pretrained models, we'll need the "large model": ``` python -m spacy download en_core_web_lg ``` ```python import spacy nlp = spacy.load("en_core_web_lg") doc = nlp("What is my purpose?") for token in doc: print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_, token.shape_, token.is_alpha, token.is_stop) ``` ``` What what NOUN WP attr Xxxx True False is be VERB VBZ ROOT xx True False my -PRON- ADJ PRP$ poss xx True False purpose purpose NOUN NN nsubj xxxx True False ? ? PUNCT . punct ? False False ``` --- class: spacious # Word2Vec with spaCy ```python doc = nlp("What is my purpose?") for token in doc: print(token.text, token.has_vector, token.vector.shape) ``` ``` What True (300,) is True (300,) my True (300,) purpose True (300,) ? True (300,) ``` ```python nlp.vocab.vectors.shape ``` ``` (684831, 300) ``` ??? There are 2 benefits to using these prebuilt models. A) It takes a long time to run to train these B) You probably don't have the corpus, you can download Wikipedia, but I don't think you can actually download the Google News corpus. There are 3,000,000 word vectors in total stored in this model. So there’s a vocabulary of size 3,000,000. Each of the words in the vocabulary has a 300-dimensional vector. I can only apply this to words that are within this vocabulary. If I have any words that are not in this vocabulary, then this model is useless. The first thing that we want to do is to check whether these representations actually semantic in some way. To do this, first, we need to define how can we measure similarity in this space. And the way that similarities usually measured between words is using the cosine similarity. --- # Cosine Similarity .padding-top[ `$$\Large \text{similarity}(v, w)=\cos(\theta) = \frac{v^Tw}{\lVert v\rVert \lVert w \rVert}$$` ] ??? This is quite related to Euclidean distance but basically, it normalizes the weight, the length of the vector. This is a set of a distance matrix that’s very commonly used in NLP. For example, if you want to compare if the two documents are similar. For documents in the bag of words representation, this might be quite intuitive, since it just sort of normalizes the length of document away. --- class: smaller # Inspecting Semantics ```python queries = [w for w in nlp.vocab if w.is_lower and w.prob >= -15] def most_similar(word, count=10): by_similarity = sorted(queries, key=lambda w: word.similarity(w), reverse=True) return [w.orth_ for w in by_similarity[:count]] most_similar(nlp("movie")) ``` ``` ['movie', 'movies', 'film', 'films', 'flick', 'starring', 'soundtrack', 'trailer', 'cinema', 'remake'] ``` ??? Now that we know how we can measure similarities in the space, we can find similar words. And so let's start with ‘movie’ and the top 5 matches for it (ordered from most similar to least similar top 5) This computes the cosine similarities between the 300-dimensional vector for movie and all the other 300-dimensional vectors for the 3 million other words and gives the 5 closest one. In this case, they are ‘film’, ‘movies’, ‘films’, ‘moive’, ‘Movie’. I guess ‘moive’ is a common misspelling. --- class: smaller ```python most_similar(nlp("good"), count=15) ``` ??? Trying the same for the word ‘good’ gives ‘great’, ‘bad’, ‘terrific’, ‘decent’ and ‘nice’. ‘bad’ is an antonym not a synonym for ‘good’, this makes sense since sometimes if we replace ‘good’ with ‘bad’, it will still make sense. -- ``` ['good', 'great', 'better', 'very', 'nice', 'really', 'excellent', 'decent', 'well', 'but', 'much', 'too', 'bad', 'enough', 'kind'] ``` -- ```python most_similar(nlp("cute dog")) ``` ``` ['cute', 'dog', 'adorable', 'puppy', 'cat', 'kitty', 'dogs', 'kitten', 'bunny', 'pet'] ``` -- ```python doc = nlp("cute dog") doc.vector.shape ``` ``` (300,) ``` ```python np.all(doc.vector == (nlp("cute").vector + nlp("dog").vector) / 2) ``` ``` True ``` ??? You can also look at multiple vectors and take the average and then look what's close to that. I’ve computed for ‘cute’ and ‘dog’. --- # Prepare document (IMDB) .smallest[ ```python print(text_train_sub[0].decode()) ``` ``` Maybe it's just because I have an intense fear of hospitals and medical stuff, but this one got under my skin (pardon the pun). This piece is brave, not afraid to go over the top and as satisfying as they come in terms of revenge movies. Not only did I find myself feeling lots of hatred for the screwer and lots of sympathy towards the "screwee", I felt myself cringe and feel pangs of disgust at certain junctures which is really a rare and delightful thing for a somewhat jaded horror viewer like myself. Some parts are very reminiscant of "Hellraiser", but come off as tribute rather than imitation. It's a heavy handed piece that does not offer the viewer much to consider, but I enjoy being assaulted by a film once and awhile. This piece brings it and doesn't appologize. I liked this one a lot. Do NOT watch whilst eating pudding. ``` ```python # skip some annotations for faster processing nlp = spacy.load("en_core_web_lg", disable=["tagger", "parser", "ner"]) docs_train = [nlp(d.decode()).vector for d in text_train_sub] X_train = np.vstack(docs_train) X_train.shape ``` ``` (18750, 300) ``` ] ??? This is a silly way to tokenize the input and using only words that appear in the vocabulary used in the pretrained model. Doing the same for movie review. I’m tokenizing this in a way that only the words that appear in the vocabulary are actually here because the other words can’t be encoded in Word2vec. So I get the count vectorizer, I use the vocabulary in the Word2vec model and then I transform and inverse transform and I get these results where each of them has a 300-dimensional vector So now we have the list of tokens for all the movie reviews. And the most common way to use this to represent documents is just using the average. --- # Classification of average vectors .smaller[ ```python lr_w2v = LogisticRegression().fit(X_train, y_train_sub) lr_w2v.score(X_train, y_train_sub) ``` ``` 0.867 ``` ```python docs_val = [nlp(d.decode()).vector for d in text_val] X_val = np.vstack(docs_val) lr_w2v.score(X_val, y_val) ``` ``` 0.857 ``` ] ??? Right now, each of the tokens is replaced by a 300-dimensional vector. And so I can represent each review by the mean of all the 300-dimensional vectors of the words. So previously, I had 18750 lists of 300-dimensional vectors for the training dataset and now I only have one 300-dimensional vector. After computing, we can see that we didn't really gain a lot, but definitely, there's still a lot of information available to do the sentiment analysis. I think it's better than using LSA or LDA.  You can actually also do embedding for bigrams and trigrams. One of the thing that people found, which was quite interesting is that you can actually do some sort of semantic algebra with it. --- # Analogues and Relationships .center[ ![:scale 100%](images/king_queen.png) ] .smaller[ Answer “King is to Kings as Queen is to ?”:
Find closest vector to vec(“Queen”) + (vec(“Kings”) - vec(“King”)) ] .smallest[
Mikolov et. al. Linguistic Regularities in Continuous Space Word Representations (2013)
] ??? You can use these vectors to find relationships. So people found that if they look at the difference between men, and women, it looks very similar to the difference between uncle and aunt or king and queen. They also found that, for example, the difference between King and kings is the same as between queen and Queens. If you want to ask a question, what is to Queen as kings are to King? You can take vector representation of Queen and add the vector representation of kings, and subtract the vector representation of King. And if you look at the closest point to that, you'll find it's going to be queens. People did this with a lot of different sort of semantic pairs. --- .center[ ![:scale 80%](images/analogues.png) ] .smallest[
Mikolov et. al. - Distributed Representations of Words and Phrases and their Compositionality (2013)
] ??? One thing that's quite interesting is people embedded countries and their capitals using a 300-dimensional space and then did the PCA on it. This is projecting down to 2-dimensional space, all different countries and their capitals. Even after this projection, they all look pretty peril. And so basically, the thing that goes from Poland to Warsaw is basically the same that goes from Japan to Tokyo. You can use this to discover analogies or relations. --- # Finding Relations
$$\large a : b : : c : ?$$
$$\large d = \text{arg}\max_i\frac{\left(\text{vec}(b) - \text{vec}(a) + \text{vec}(c)\right)^T vec_i}{\lVert\text{vec}(b) - \text{vec}(a) + \text{vec}(c)\rVert\lVert \text{vec}_i\rVert}$$ ??? --- class: middle .center[ ![:scale 70%](images/table_cities_states.png) ] .smallest[
Stanford CS 224D: Deep Learning for NLP
] ??? The model is able to figure out what the relationship of what does it mean for a city to be in a state. So now we have a space in which vector additions have semantic meaning. When people found about this, they were really excited. --- # Examples with spaCy .smaller[ ```python def most_similar_vec(vec, count=10): by_similarity = sorted(queries, key=lambda w: cos_sim(w.vector, vec), reverse=True) return [w.orth_ for w in by_similarity[:count]] vec = nlp('woman').vector + nlp('king').vector - nlp("man").vector most_similar_vec(vec) ``` ``` ['king', 'queen', 'prince', 'kings', 'princess', 'royal', 'throne', 'queens', 'monarch', 'kingdom'] ``` ```python vec = nlp('woman').vector - nlp("man").vector + nlp('he').vector most_similar_vec(vec) ``` ``` ['she', 'woman', 'he', 'her', 'herself', 'mother', 'wife', 'who', 'told', 'when'] ``` ```python vec = nlp('paris').vector - nlp('berlin').vector + nlp('germany').vector most_similar_vec(vec) ``` ``` ['france', 'paris', 'europe', 'germany', 'italy', 'spain', 'japan', 'european', 'poland', 'usa'] ``` ] ??? In the first example, I'm computing ‘woman’ + ‘king’ – ‘man’ and the top 3 results are ‘queen’, ‘monarch’ and ‘princess’. Queen is the highest since ‘man’ is to ‘king’ as ‘women’ is to ‘queen’. So relationships that are found here definitely depend a lot on the corpus that was used. --- # GloVe: Global Vectors for Word Representation $X_{ij}$ = How often does work j appear in context of word i `$$ J = \sum\limits_{i,j=1}^{V} f(X_{ij}) (w_i^T \tilde{w_j} + b_i + \tilde{b_j} - \log X_{ij})^2$$` `$$ f(x) = \begin{cases} (x/x_\text{max})^\alpha & \text{if } x < x_\text{max} \\ 1 & \text{otherwise} \\ \end{cases} $$` .smaller[ https://nlp.stanford.edu/projects/glove/ ] ??? Here X_ij is the coocurrance count matrix, counting how often word I and j appear in the same context – say a 5 word window. We are learning a factorization of log(X_ij) into w_i and ~w_j, where w_i are the word-vectors we want to extract. Very infrequent word-pairs are less important to get right, and are down-weighted using f(X_ij). alpha is 3/4 empirically. x_max is 100. --- class: center, spacious # GloVe Weighting function f ![:scale 90%](images/glove_weighting.png) ??? If you're larger than X max, it's just weighted by 1. If all words appear at least 100 times then the loss is weighted fully and if words appear less than 100 times, then you take x divided by 100 to the power of alpha, and they found alpha equal to 0.75. So it works better than linear. So in the beginning, you have to compute this big sparse matrix once, but then you can do gradient descent just on this given statistics. --- # Word analogies .center[ ![:scale 45%](images/word_analogies.png) ] ??? Comparison of CBOW, SGD, skip-gram and Glove on the semantic (ie. state→capital) and syntactic (ie singular→ plural) word analogy tasks --- class: center
.center[ ![:scale 70%](images/paper_title.png) ]
.smaller[ $\overrightarrow{man} - \overrightarrow{woman} \approx \overrightarrow{king} - \overrightarrow{queen}$
$\overrightarrow{man} - \overrightarrow{woman} \approx \overrightarrow{computer programmer} - \overrightarrow{homemaker}$ ] ??? This relation is actually in there. If you do ‘man is to computer programmer’ as ‘women is to x as to x, you get ‘homemaker’ and we might not be very happy about this. --- # Going along he-she direction:
.center[ ![:scale 90%](images/he_she.png) ] ??? They did an analysis of this particular embedding. And established that we don’t want these gender stereotypes in our model and gender appropriate he-she analogies Some of them are clearly something that we would not want. And this is just one particular instance. As with all machine learning models, this learns all the biases of the data that you give it, if you give it a lot of text, then it uses that to learn all the biases of human society. --- class: middle # Paragaph Vectors ??? Alright, so the next thing I want to talk about is a slightly smarter way to look at embedding documents, which is paragraph vectors. Instead of just taking averages we're trying to do a little bit better. Fixme rerun experiments from doc2vec paper to show it works? --- # Doc2Vec
.center[ ![:scale 70%](images/doc2vec.png) ] .smallest[
Le, Mikolov: Distributed Representations of Sentences and Documents (2014)
] ??? based on cbow, not skip-grams Add a vector for each paragraph / document, also randomly initialized. To infer for new paragraph: keep weights fixed, do stochastic gradient descent on the representation D, sampling context examples from this paragraph. FIXME better explanation. There is no gradient descent at encoding time for word2vec! The most common approach here is Doc2vec. This here on the right-hand side would be the continuous bag of word model where you get the context, and where you want to predict the word from the context. What paragraph vector now does is it adds in another thing, which is a paragraph specific ID. It’s basically like adding a unique word to a vocabulary that’s just unique to this paragraph and encodes its presence in this paragraph. And you do not only learn the embedding of all the words, but you also learn embedding for each paragraph. And so basically, this tries to make up for what the words cannot predict. So you have the standard CBOW in the middle layer that tries to make a prediction but then basically trying to correct this prediction by having a paragraph specific ID. This maybe not sorts of the most intuitive way. But it actually seemed to work quite well. This is actually in practice quite a bit trickier than just looking at the word vectors since how are you going to do this if you get a new document? If you get a new document for the word vectors, you just look for each word, if it's not in the vocabulary you’re lost out anyway, if it’s in the vocabulary, you can just take the vector for this word that you have computed already. If you get a new paragraph, how do you get the paragraph vector? Since you have a new paragraph that has never seen before, the paragraph is not in the training set. And so what you do then you look at the paragraph and you sample training samples from this paragraph, so your sample context and words. And you fix all the word vectors because you learned them on the training sets. You're basically learning this paragraph ID by doing gradient descent. So you initialize this paragraph vector randomly and to transform a new paragraph, you basically learn the paragraph vector for this paragraph, fixing all the word vectors. In this sense, it’s more of trying to correct what the model could not figure out with the word vectors. So now, you have a vector that represents everything in the paragraph that was not already encoded in the context, or it might encode like in a wider context or something like that. And usually, it's just the same dimensionality as the other vectors. So here, we would get like a 300-dimensional vector that encodes each paragraph. But to encode a new paragraph you need to do gradient descent. --- #Doc2Vec with gensim .smaller[ ```python def read_corpus(text, tokens_only=False): for i, line in enumerate(text): if tokens_only: yield gensim.utils.simple_preprocess(line) else: # For training data, add tags yield gensim.models.doc2vec.TaggedDocument( gensim.utils.simple_preprocess(line), [i]) train_corpus = list(read_corpus(text_train_sub)) test_corpus = list(read_corpus(text_val, tokens_only=True)) model = gensim.models.doc2vec.Doc2Vec(vector_size=50, min_count=2) model.build_vocab(train_corpus) model.train(train_corpus, total_examples=model.corpus_count, epochs=55) ``` ] .smallest[ https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/doc2vec-lee.ipynb ] ??? fixme example of nearest neighbors to see if this learned something useful! Should use the unlabeled reviews! So here, basically, I started completely fresh from no model at all. And I learned embedding for all of the words and for all the paragraphs. So I did a small validation to see if the word vectors that I learned are meaningful. --- # Validation of Word Vectors ```python model.wv.most_similar("movie") ``` ``` [('film', 0.948), ('flick', 0.822), ('series', 0.715), ('programme', 0.703), ('sequel', 0.693), ('story', 0.677), ('show', 0.655), ('documentary', 0.653), ('picture', 0.642), ('thriller', 0.630)] ``` ??? I looked at the most similar ones to ‘movie’ and we get these results. I like this better than the ones from the pre-built one. These are pretty good but it's maybe not as surprising as ‘movie’ is sort of the most common word in the whole corpus. --- #Encoding using doc2vec .smaller[ ```python vectors = [model.infer_vector(train_corpus[doc_id].words) for doc_id in range(len(train_corpus))] X_train = np.vstack(vectors) X_train.shape ``` ``` (18759, 50) ``` ```python test_vectors = [model.infer_vector(test_corpus[doc_id]) for doc_id in range(len(test_corpus))] X_test = np.vstack(test_vectors) ``` ] ??? Now I can encode my training corpus. I get a 50-dimensional vector for each movie review in the training dataset. If I want to do classification I can run a logistic regression on this vector. So model.infer_vector does the gradient descent to compute the paragraph vectors for each document. In the paper, they used much more data from the same corpus and they used 400-dimensions and probably they also iterated longer. In the paper, they didn't train a logistic regression on this. They trained a neural network on this, this probably makes a difference --- ```python from sklearn.linear_model import LogisticRegression lr = LogisticRegression(C=100).fit(X_train, y_train_sub) lr.score(X_train, y_train_sub) ``` ``` 0.817 ``` ```python lr.score(X_val, y_val) ``` ``` 0.803 ``` ??? Not working well here. Either not enough training data, sgd didn’t converge yet, or too low dimension. FIXME combine with word vectors? In the paper: they actually get 0.926% 400 dimensions (And using unsupervised data) --- class: center, middle # Transformer-based feature extraction BERT, GPT2, ... Bi-directional language models: Using context + word order! .center[ ![:scale 60%](images/bert_high_level.png) ] Huge compute effort and training data (multiple days on TPUs) --- # Sentiment analysis using ![](images/huggingface_logo.svg) Off-the-shelf model on aclImdb (GPU suggested) ```python from transformers import pipeline nlp = pipeline("sentiment-analysis") res = [nlp(t.decode()) for t in text_val] ``` ``` # my laptop couldn't handle it ``` --- # BERT for feature extraction ```python from transformers import pipeline nlp = pipeline("feature-extraction") X_train = [nlp(t.decode() for t in text_train] ``` Use similar to word vectors (can train logistic regression etc) Better: fine tuning (later in the semester?) --- class: middle # Questions ?


What’s a *good* goal?

– What makes the client money is a better goal (if you* can identify it)
– Problems arise in actually identifying this. What’s important to testers vs client vs bad guys…
– Best goal, testing client’s ability to detect & respond to various levels of attackers
Finding stuff internally
--------------------------------
GOOD
• Nmap
• Ping
• Nessus
• Nexpose
• Angry IP Scanner?
• “net view /domain”
BETTER
• OSQL
– osql –L Lists all MS SQL servers
• DSQUERY / DSGET (Annoying)
– dsquery computer -limit 0 current domain
– dsquery user -limit 0
– dsquery computer -limit 0 "DC=company,DC=net"
– dsquery user -limit 0 "DC=company,DC=net“ other domain
– (Joeware) ADFind (less annoying)
• nltest
– Keeps trying to get you info if one path fails
BEST
• Use the underlying API
• Railgun
• Demo
– Route
– ARPTable
– TCPTable
User discovery
--------------------------------------
GOOD
• net group “domain admins” /domain
• net group “domain admins” /domain:DM
• net localgroup Administrators
• net group localgroup Administrators /domain
• net user domainadmin_username /domain
• net user username /domain
BETTER
• Rpcclient
– Enumerate users
BEST
• Explain UserDiscovery
• Demo UserDiscovery
• Explain DisplaySessions
• Demo DisplaySessions
• PVE-Find-AD-User
– https://www.corelan.be/index.php/my-freetools/ad-cs/pve-find-ad-user/
Priv Escalation
-------------------------------------------
GOOD
• getsystem
• Post modules
– Keyboard layout
– Bypassuac
• Core Impact / Canvas ship with locals
– Honestly a big lacking area for MSF
BETTER?
• Explain DomainDrop
BEST
• Just ask for it…
• Explain ‘Ask’ module
• Looking for the user that has the $stuff
• Tasklist
– tasklist /V /S $IP /U $user /P $password
for /F "skip=3 delims=\ " %A in ('net view') do tasklist /V /S %A /U $user /P $password
Searching for Loot
---------------------------------------------
Searching for Gold (Good)
• Dir /s “My Documents”
• Dir /s “Desktop”
• Dir /s *.pcf
• ListDrives
Searching for Gold (Better)
• Dumplinks
• GetFirefoxCreds
• GetPidginCreds
• Outlook, IE, Chrome, RDP
Password Extraction
– Basically the whole ‘credentials’
post module section
• SharePoint
• Intranet.company.com
Searching for Gold (Best)
• OpenDLP
• Fiction’s Database Searcher
• Search in Meterpreter
– Uses windows indexing i.e. outlook email
• Dir /s $share > filetosearchoffline.txt
– Findstr too
– Do what works for you…click scripts rule
Pivoting
-----------------------------------------------
Portforwarding
– Meterpreter portfwd
– Route
– Sock4a module + meterpreter session
– Pro VPN Pivot?
• Portproxy
– Built into Windows
– netsh interface portproxy>add v4tov4
listenport=25 connectaddress=<IP_ADDRESS>
connectport=80 protocol=tcp
• Legitimate Access via VPN, Term Server, Citrix, etc
Persistence
-----------------------------------------------------
Smartlocker -> lockout_recorder
– http://blog.metasploit.com/2010/12/capturing-windows-logonswith.html
• Fxsst.dll
– https://blog.mandiant.com/archives/1786
– http://www.room362.com/blog/2011/6/27/fxsstdll-persistence-theevil-fax-machine.html
• Explain gpo_dropper hbgary
– http://www.hbgary.com/malware-using-local-group-policy
• Explain IPv6 Dropper
– http://hak5.org/hack/ipv6-from-the-pentesters-perspective


As a dual degree graduate, in technology and Business, I have always been passionate about how business problems can be addressed with the right technological solutions.
2008, was when I announced my arrival to the IT world and since, I would say that my 7 years of journey in the IT space couldn't have been more fulfilling
All these years, I have contributed to organization success in different roles - Business/ Data Analyst and Quality Analyst
I would love to talk more about my past accomplishments and how I have been able to created value at the workplace in different ways
Besides work, I love to share my views on trending topics (Technology, Sports) as part of my company’s newsletter.
And with that, I would end talking and begin throwing some light on my Skills and Work Experience .
Business/Functional Requirement Documents, FlowCharts, Interview Sessions. Tools: MS Office Suite, Jira
Functional and Automation Testing, Agile/ Waterfall Methodology, System/INT/UAT Testing. Tools: HP-ALM, Jira, Selenium WebDriver,QTP
Project Initiation, Scope Planning, Design, Execution, Change Management, Risk Management, Reporting. Tools: SharePoint, Slack, Trello
Data Warehouse Design, ETL Operations, Data Reporting, Tools Used : SAS Enterprise Guide, SSIS, SSRS, Oracle 10G
Porter's 5 Forces Analysis, VRIME Model, Strategic Alliances, Corporate Governance, Mergers and Acquisitions, Strategic Leadership
Disruptive Innovation, Licencing and IP, Angel and Seed Financing, Market Research
Agile Methodology, Cost/benefit analysis, UML, Domain-driven desgin, Process modeling
Creation of charts and graphs along with analysis of data using Tableau
Click on the titles to learn more


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public class Choices
extends java.lang.Object
| Constructor and Description |
|---|
Choices() |
Choices(Match match,
VirtualView view,
Payment payment,
AtomicActions atomicActions,
SingleActionManager singleActionManager)
customize constructor
|
| Modifier and Type | Method and Description |
|---|---|
void |
afterEffectHandler()
handles moves after shoot and powerups options
called after the effect execution, it shows the other effects available or continue the turn if the player cannot
perform any other effect
|
void |
applyAllTargetEffect()
apply the damage in cells chosen for all target effects
|
void |
applyEffect()
by reading the target class this method apply the exact number of damage and mark to every target
|
void |
applyForcedChoiceEffect()
apply the damage to the players for weapon effects that don't need the user to choose someone
|
java.util.List<Character> |
availablePlayersToMove()
method used for the vortex move effect
|
java.lang.Boolean |
canIshoot()
tells if the player can do a shoot action, useful after a player uses a powerup
|
void |
damagedTargets()
this method doSomething to the view the list of the available players that can be targeted by Targeting Scope powerup
|
void |
effectAnalyzer()
called after the payment session ( if the effect is not free), analyze the effect and see if there is a moveBefore inside it
called the first time I select the effect
|
void |
effectHandler()
apply the effect and continue the turn
called by moveBeforeShoot after the move,
by SingleActionManager, by effectAnalyzer
doSomething to the view the possible selections
|
Effect |
getCurrentEffect() |
Cell |
getMoveCell() |
java.util.List<Player> |
getMovedPlayers() |
Payment |
getPayment() |
Player |
getSelectedPlayer() |
Powerup |
getSelectedPowerup() |
Weapon |
getSelectedWeapon() |
java.util.List<Player> |
getShootedPlayers() |
java.util.List<Effect> |
getUsedEffect() |
void |
moveBeforeShootHandler(Message message)
this method handles the selection of a player in a move before shoot action
|
void |
playersWithCounterAttackPowerup()
this method check the damaged players and sends a message to the view with the ones that have tagback grenade powerup to ask
them if they want to use it
|
void |
powerupEffectHandler(Message message)
this method analyzes the powerup selected and sends messages to the view basing on the kind of powerup the player is using
|
void |
powerupsAfterShoot()
this method handle the possible use of powerups after a shooting action
|
void |
reset()
This method resets all the attributes to avoid errors the next time I receive a new payment message
I have to reset everything when a new effect is used, but I don't have to reset the selectedWeapon, cause
it is overwritten every time a player select one (selectedWeapon has to be set to null after the player ends the shoot action)
|
void |
resetEverything()
this method reset all the choices attributes in the class
|
void |
selectionCardHandler(Message message)
method that filters the card selection messages
|
void |
selectionHandler(Message message)
this method filters all the selection kind messages
|
void |
selectionPlayerHandler(Message message)
method that filters the player selection
|
void |
setCurrentEffect(Effect currentEffect) |
void |
setMoveCell(Cell moveCell) |
void |
setMovedPlayers(java.util.List<Player> movedPlayers) |
void |
setSelectedPlayer(Player selectedPlayer) |
void |
setSelectedPowerup(Powerup selectedPowerup) |
void |
setSelectedWeapon(Weapon selectedWeapon) |
void |
setShootedPlayers(java.util.List<Player> shootedPlayers) |
void |
updateVisibilityAllTarget()
this method sends new options to the player by watching the other players chosen and the effect that is being executed
done for all target effect (options are Cell)
|
void |
updateVisibilityMultiple()
this method sends new options to the player by watching the other players chosen and the effect that is being executed
done for multiple target effect (options are players)
|
void |
usePowerupHandler(Message message)
this method handles the selection of a player in an use powerup action
|
void |
weaponHandler(Message message)
Method that receive the weapon and analyze it to doSomething options to the view
|
public Choices(Match match, VirtualView view, Payment payment, AtomicActions atomicActions, SingleActionManager singleActionManager)
match
- match classview
- reference to the virtual viewpayment
- payment classatomicActions
- atomic action classsingleActionManager
- single action manager classpublic Choices()
public java.util.List<Player> getShootedPlayers()
public java.util.List<Effect> getUsedEffect()
public Weapon getSelectedWeapon()
public void setSelectedWeapon(Weapon selectedWeapon)
public Effect getCurrentEffect()
public void setMoveCell(Cell moveCell)
public Cell getMoveCell()
public Player getSelectedPlayer()
public java.util.List<Player> getMovedPlayers()
public Powerup getSelectedPowerup()
public void setSelectedPowerup(Powerup selectedPowerup)
public void setSelectedPlayer(Player selectedPlayer)
public void setCurrentEffect(Effect currentEffect)
public Payment getPayment()
public void setShootedPlayers(java.util.List<Player> shootedPlayers)
public void setMovedPlayers(java.util.List<Player> movedPlayers)
public void reset()
public void resetEverything()
public void selectionHandler(Message message)
message
- sent to the method that reads the messagepublic void selectionCardHandler(Message message)
message
- sent to the method that read the messagepublic void selectionPlayerHandler(Message message)
message
- sent to the method that reads the messagepublic void usePowerupHandler(Message message)
message
- message receivedpublic void moveBeforeShootHandler(Message message)
message
- message receivedpublic void powerupEffectHandler(Message message)
message
- with the selected poweruppublic void damagedTargets()
public void weaponHandler(Message message)
message
- message receivedpublic void afterEffectHandler()
public void powerupsAfterShoot()
public void effectHandler()
public void updateVisibilityMultiple()
public void updateVisibilityAllTarget()
public void applyAllTargetEffect()
public void applyForcedChoiceEffect()
public void applyEffect()
public void effectAnalyzer()
public java.util.List<Character> availablePlayersToMove()
public void playersWithCounterAttackPowerup()
public java.lang.Boolean canIshoot()


Junggu-heating operates the MIC(bixby) app as a free app. The service provides help to search timetables of movies or performances, and give recommandation of movies or performances.
This page is used to inform the app users regarding our policies with the collection, use, and disclosure of Personal Information if anyone decided to use our Service.
If you choose to use our Service, then you agree to the collection and use of information in relation with this policy. The Personal Information that we collect are used for providing and improving the Service. We will not use or share your information with anyone except as described in this Privacy Policy.
The terms used in this Privacy Policy have the same meanings as in our Terms and Conditions, which is accessible at MIC(bixby), unless otherwise defined in this Privacy Policy.
For a better experience while using our Service, we may require you to provide us with certain personally identifiable information, including but not limited to your non-continuous geolocation. The information that I request will be retained on your device and is not collected by me in any way.
The app does use third party services that may collect information used to identify you.
Link to privacy policy of third party service providers used by the app
Log Data
I want to inform you that whenever you use my Service, in a case of an error in the app I collect data and information (through third party products) on your phone called Log Data. This Log Data may include information such as your device Internet Protocol (“IP”) address, device name, operating system version, the configuration of the app when utilizing my Service, the time and date of your use of the Service, and other statistics.
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Our website uses these "cookies" to collection information and to improve our Service. You have the option to either accept or refuse these cookies, and know when a cookie is being sent to your computer. If you choose to refuse our cookies, you may not be able to use some portions of our Service.
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We value your trust in providing us your Personal Information, thus we are striving to use commercially acceptable means of protecting it. But remember that no method of transmission over the internet, or method of electronic storage is 100% secure and reliable, and we cannot guarantee its absolute security.
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Children’s Privacy
Our Services do not address anyone under the age of 13. We do not knowingly collect personal identifiable information from children under 13. In the case we discover that a child under 13 has provided us with personal information, we immediately delete this from our servers. If you are a parent or guardian and you are aware that your child has provided us with personal information, please contact us so that we will be able to do necessary actions.
We may update our Privacy Policy from time to time. Thus, we advise you to review this page periodically for any changes. We will notify you of any changes by posting the new Privacy Policy on this page. These changes are effective immediately, after they are posted on this page.
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Cameo is a semi-soft, Camembert style cheese from Redwood Hill Farms, Sebastopol, California. This bloomy rind cheese is made using pasteurized goat's milk. The interior paste is creamy and smooth with slightly goaty taste. The rind is covered with fresh herbs and peppercorns that add herbal and floral flavour and also makes the cheese stand out on a cheese platter.
Other American cheeses:


Telerik Numeric TextBox for ASP.NET MVC Key Features
- Light HTML output
- Seventeen built-in themes
- Keyboard navigation
- Filtration of the entered data
- Support for ASP.NET MVC build-in server and client validation
- Client API and event handling
- Copy/Paste support
- Globalization
- There are also CurrencyTextBox, PercentTextBox and IntegerTextBox components
except NumericTextBox.


Computing: Information Technology
Mission
The Computer Science Department at Slippery Rock University offers a concentration in Information Technology degree that supports the university's mission by providing a dynamic computing program tailored to the needs of a growing market. The Department of Computer Science is committed to providing an all-around and in-depth education that qualifies its majors for a wide range of careers in computing and/or advanced studies in computer-related fiels. To fulfill its commitment, the department has designed a curricula which places an emphasis on developing both information management and decision-making skills, a sense of professional responsibility, and developing depth and breadth of the subject matter.
Careers
Today's rapid growth in information technology has led to the emergence of a dynamic computer-driven field that utilizes Web-based applications as a means of communication. Graduates of the IT program will be prepared for positions in the IT department of any business. Courses selected for this program were determined by faculty in the Computer Science Department after researching the needs of business and examining the many courses that corporations require of their employees. Corporations are continually retraining their employees to keep abreast of rapid changes in the computer industry. Hence, graduates of the IT program are expected to have a variety of employment opportunities. Graduates will also be prepared for graduate work in information science and technology, management information science and other computer/information systems disciplines. Our graduates have consistently enjoyed excellent job placement.
Department
The Computer Science Department has twelve faculty members who are highly credentialed and dedicated to providing excellence in the teaching and learning process. Eleven of the faculty members hold doctoral degrees. Scholarly research is currently conducted and partially funded by several local, state, and national agencies in the areas of robotics/artificial intelligence, parallel computing and program optimization. While the primary goal of the faculty members is excellence in teaching, their research and scholarly interests play an important role in keeping our curricula on the cutting edge of the discipline. In addition, these interests provide research opportunities to interested students. The majors strengths of the department are: availability of instructors outside the classroom for additional help, small class size, one-on-one academic advising and strong faculty support for student activities.
Student Activities
The department supports student activities in many ways. Faculty members are actively interested in functions sponsored by the Computer Technology Club. Every major in the department is automatically a member of the club. The department has a chaper of Upsilon Pi Epsilon, a national honor society in computing. All students are encouraged to join the Association of Computing Machinery, the best-known international professional organization in computing. Every year the department sponsors teams for participation in at least two programming contests, one held by the ACM and the other by the Pennsylvania Association for Computer and Information Sciences Educators during professional meetings.
Computing Facilities
The department maintains five computer laboratories exclusively for the use of students in major courses. The laboratories are equipped with UNIX X-Window workstations, Workstations running the Windows XP operating system, a B-12 mobile robot, a vision system and robotic arms, and servers and workstations for hands-on training in computer networking. All software used in course work is available in these labs and in other selected campus labs. The university provides full Internet access to all students. In addition, the department allows each major to maintain his/her own World Wide Web page for academic purposes. Students are not required to own a computer.
Futher Information:
You may contact us in various ways:
Slippery Rock University
Department of Computer Science
Slippery Rock, PA 16057
Phone:<PHONE_NUMBER>
Fax:<PHONE_NUMBER>
E-Mail: Vicki Sparano - vicki.sparano@sru.edu


Getting started
Beforehand, it is important to know that this library does not provide a standalone solution. As such, some amount of PHP knowledge is required, and ideally a Web framework should encapsulate the GraphQL endpoint (such as Laravel, Slim or Symfony). All you truly need is a way to read and write PSR-7 objects from/to the client. Afterwards, you can handle incoming HTTP requests as GraphQL requests.
Requirements
- A project with an initialized composer.json
- A project with an initialized package.json and Gruntfile.js
- Some way to read and write PSR-7 request objects to/from the client
- An endpoint dedicated to GraphQL
Implementation
Assuming you have all three things above, the first thing to do would be to include both the GraphQL Generator and the GraphQL Framework.
composer require kronostechnologies/graphql-framework
composer require --dev kronostechnologies/graphql-generator
Once this is done, your PHP dependencies are setup. Next thing, we need a package from npm in order to generate the GraphQL schema for us:
npm install --save-dev grunt-graphql-php-generator
Setting up a schema watcher
Now that all the dependencies are installed, you can create your first .graphqls
schema. Let's create it at a specific location to simplify the example, under graphql/schema.graphqls
:
scalar DateTime
scalar Cursor
interface Identifiable {
id: ID
}
enum Color {
BLUE,
RED,
YELLOW,
GREEN,
PURPLE,
CYAN
}
type Item implements Identifiable {
id: ID,
name: String,
color: Color
}
type Query {
item(id: ID): Item!
items(page: Int, perPage: Int): [Item!]!
}
Once created, you will need to adjust your Gruntfile to tell it from which file to generate the schema from/to. Add this to your Gruntfile.js
:
'autogen-schema': {
options: {
source: './graphql/schema.graphqls',
destination: './[BaseFolder]/GraphQL/Schema/',
generatorCmdPath: './vendor/bin/graphqlgen',
namespace: '[BaseNamespace]',
deleteAndRecreate: true,
runPHPCSFixer: true
}
}
Now, you can run a Grunt command to generate the files required by the framework for you:
grunt autogen-schema
Entry point
The entry point requires access to a PSR-7 request object, and it will respond in a PSR-7 response. The core requirement to handle a GraphQL is the following:
$configuration = GraphQLConfiguration::create()
->setControllersDirectory(__DIR__ . '\\[BaseNamespace]\\GraphQL\\Controllers')
->setGeneratedSchemaDirectory(__DIR__ . '\\[BaseNamespace]\\GraphQL\\GeneratedSchema');
// Assume $request contains the PSR-7 request.
$entryPoint = new HttpEntryPoint($configuration);
$response = $entryPoint->executeRequest($request);
// $response contains the PSR-7 response
You should now be able to query the GraphQL entry point. It will give out its introspection result, but it won't execute any query successfully since we have defined no controller yet.
Query controller
Let's define a sample query controller to get a single item. It should be located under [BaseDirectory]\GraphQL\Controllers
. Since we want to get the item
field in the Query
type defined in the schema higher up, we need a QueryController
:
<?php
class QueryController extends BaseController {
public function getItem() {
return $this->hydrator->fromSimpleArray(ItemDTO::class, [
'id' => $this->context->getArgument('id'),
];
}
}
Here, we simply return an ItemDTO
, which is the representation of what querying Item
in an object. These DTOs are made by the generator to aid in development mostly.
Now, the following query should work:
query {
item(id: 1) {
id
}
}


1: <?php
2: /*
3: * The MIT License
4: *
5: * Copyright 2017 David Schoenbauer<EMAIL_ADDRESS>6: *
7: * Permission is hereby granted, free of charge, to any person obtaining a copy
8: * of this software and associated documentation files (the "Software"), to deal
9: * in the Software without restriction, including without limitation the rights
10: * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
11: * copies of the Software, and to permit persons to whom the Software is
12: * furnished to do so, subject to the following conditions:
13: *
14: * The above copyright notice and this permission notice shall be included in
15: * all copies or substantial portions of the Software.
16: *
17: * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
18: * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
19: * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
20: * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
21: * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
22: * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
23: * THE SOFTWARE.
24: */
25: namespace DSchoenbauer\Sql\Command;
26:
27: use DSchoenbauer\Sql\Exception\ExecutionErrorException;
28: use DSchoenbauer\Sql\Where\WhereStatementInterface;
29: use PDO;
30:
31: /**
32: * removes records from a PDO connected resource
33: *
34: * @author David Schoenbauer<EMAIL_ADDRESS>35: */
36: class Delete implements CommandInterface
37: {
38:
39: private $table;
40:
41: use WhereTrait;
42:
43: /**
44: * @param string $table table with which you wish to remove records from
45: * @param WhereStatementInterface $where an object that is designed to
46: * return a where statement to limit the data that is affected by the delete
47: * @since v1.0.0
48: */
49: public function __construct($table, WhereStatementInterface $where = null)
50: {
51: $this->setTable($table)->setWhere($where);
52: }
53:
54: /**
55: * takes the SQL and the data provided and executes the query with the data
56: * @param PDO $pdo a connection object that defines where the connection is to be executed
57: * @return bool TRUE on success or FALSE on failure.
58: * @throws ExecutionErrorException thrown when any exception or SQL failure occurs
59: * @since v1.0.0
60: */
61: public function execute(PDO $pdo)
62: {
63: try {
64: $stmt = $pdo->prepare($this->getSql());
65: if (count($this->getData()) > 0) {
66: return $stmt->execute($this->getData());
67: }
68: return $stmt->execute();
69: } catch (\Exception $exc) {
70: throw new ExecutionErrorException($exc->getMessage());
71: }
72: }
73:
74: /**
75: * retrieves the data that is uses to fulfill the requirements of a prepared
76: * statement
77: * @return array a single level associative array containing keys that
78: * represent the fields and values that represent items to fulfill the
79: * requirements of a prepared statement
80: * @since v1.0.0
81: */
82: public function getData()
83: {
84: return $this->getWhereData();
85: }
86:
87: /**
88: * Generates a SQL statement ready to be prepared for execution with the intent of removing data
89: * @return string a string that represents a delete statement ready to be prepared by PDO
90: * @since v1.0.0
91: */
92: public function getSql()
93: {
94: $sqlTemplate = 'DELETE FROM %1$s %2$s';
95: return trim(sprintf($sqlTemplate, $this->getTable(), $this->getWhereStatement()));
96: }
97:
98: /**
99: * retrieves the table with which you wish to remove from
100: * @return string table with which you wish to remove from
101: * @since v1.0.0
102: */
103: public function getTable()
104: {
105: return $this->table;
106: }
107:
108: /**
109: * defines a table with which you wish to remove from
110: * @param string $table table with which you wish to remove from
111: * @return Delete for method chaining
112: * @since v1.0.0
113: */
114: public function setTable($table)
115: {
116: $this->table = $table;
117: return $this;
118: }
119: }
120: