The goal of this analysis is to build a color palette of my haiku dataset, in the same vein as a 2017 PyCon talk titled Gothic Colors: Using Python to understand color in nineteenth century literature. This talk was the first exposure I can recall of the application of (hard) computer science to a (soft) natural science, and that made a lasting impression. The field of natural computing is eminently fascinating to me, and this conference talk was, in part, the inspiration behind my entire haiku generation project, going back even before trumperator, my first attempt at text generation. As an aside, after this project is good and finished, and I've settled into gainfully-employed life as a software engineer, I'd like to return to the generation of Trump tweets, using the same strategy as I've used here for haiku.
I'm getting carried away, however. Here, all I want to do, is parse the usage of color from the haiku in an intelligent manner—one that is aware that the word "rose" has three distinct meanings in the sentences
In the context of color parsing, it makes sense to treat the first two both as uses of the color "rose". In order to perform this parsing, we must part-of-speech tag the haiku corpus. Fortunately, doing so is not a new problem, so we use an out-of-the-box POS tagger.
import collections
import colorsys
import itertools
import matplotlib.pyplot as plt
import nltk
import numpy as np
import pandas as pd
import seaborn as sns
import webcolors
from IPython.display import Image
import haikulib.eda.colors
from haikulib import data, nlp, utils
sns.set(style="whitegrid")
It's often useful to implement a simpler version of a feature before implementing the full functionality. So before performing POS-tagging and more intelligent color identification, we simply look for color names in the corpus. We do so by looking for any n-grams that are in our master list of color names. We use 1, 2, and 3-grams.
df = data.get_df()
corpus = []
for haiku in df["haiku"]:
corpus.append(" ".join(haiku.split("/")))
color_names = {r["color"]: r["hex"] for _, r in haikulib.eda.colors.get_colors().iterrows()}
naive_colors = collections.Counter()
for haiku in corpus:
# Update the color counts for this haiku.
naive_colors.update(nlp.count_tokens_from(haiku, color_names, ngrams=[1, 2, 3]))
For ease of use in visualization, I build a data frame of the color occurrences.
naive_color_counts = pd.DataFrame({
"color": list(naive_colors.keys()),
"count": list(naive_colors.values()),
"hex": [color_names[c] for c in naive_colors],
})
total_color_count = sum(row["count"] for index, row in naive_color_counts.iterrows())
print(f"There are {total_color_count} occurences of color in the corpus")
print(f"There are {len(naive_color_counts)} unique colors")
naive_color_counts.head(10)
| # | color | count | hex |
|---|---|---|---|
| 0 | green | 415 | #3aa655 |
| 1 | snow | 1981 | #fffafa |
| 2 | dusk | 482 | #4e5481 |
| 3 | sea | 679 | #3c9992 |
| 4 | watermelon | 27 | #fd4659 |
| 5 | sky | 1232 | #82cafc |
| 6 | stone | 275 | #ada587 |
| 7 | jasmine | 66 | #f8de7e |
| 8 | sand | 300 | #c2b280 |
| 9 | rust | 27 | #b7410e |
These analyses were originally done in Jupyter Notebooks, and converted to HTML (by hand, because I'm not good enough with pandoc or nbconvert to match my website's custom CSS and boostrap classes).
Rather than implement the color parsing as a part of the notebook, and then copy paste the
implementation to do analysis later, the color parsing was done in the
haikulib.eda
module. But for the sake of self-contained notebooks, I wanted a way to
show the source of an arbitrary function so I'm not copy-pasting out-of-sync versions of function
around. So I wrote a small introspective helper function to grab the source code of a function and
render it in syntax-highlighted HTML in the Jupyter notebook.
# Use display_source() to display its own source. This feels dirty.
utils.display_source("haikulib.utils", "display_source")
def display_source(module: str, function: str) -> IPython.display.Code:
"""Display the source of the provided function in a notebook.
:param module: The module containing function. Must be importable.
:param function: The function whose source we wish to display.
"""
__module = importlib.import_module(module)
__methods = dict(inspect.getmembers(__module, inspect.isfunction))
def _jupyterlab_repr_html_(self):
from pygments import highlight
from pygments.formatters import HtmlFormatter
fmt = HtmlFormatter()
style = "<style>{}\n{}</style>".format(
fmt.get_style_defs(".output_html"), fmt.get_style_defs(".jp-RenderedHTML")
)
return style + highlight(self.data, self._get_lexer(), fmt)
# Replace the _repr_html_() method with our own that also adds the `jp-RenderedHTML` class
# to fix https://github.com/jupyterlab/jupyterlab/issues/6376.
# Mmmm. Slimy.
IPython.display.Code._repr_html_ = _jupyterlab_repr_html_
return IPython.display.Code(data=inspect.getsource(__methods[function]), language="python3")
However, I haven't figured out a good (minimal) way to style individual notebook cells, so I'll break from the real notebook and continue as if the color parsing functions are defined in the notebook itself.
We can determine if a word is a color simply by checking if it is contained in our color master list, and by checking if it is an adjective or a noun.
def __get_colors() -> pd.DataFrame:
"""Get a DataFrame of color -> HTML colors.
Note that this CSV file uses hex RGB color codes for many of the colors, but falls back to using
HTML named colors for colors without an RGB value.
The colors with RGB values came from https://xkcd.com/color/rgb/ while the colors with the named
values came from
https://medium.com/@eleanorstrib/python-nltk-and-the-digital-humanities-finding-patterns-in-gothic-literature-aca84639ceeb
"""
return pd.read_csv(data.get_data_dir() / "colors.csv", index_col=0)
def __get_colors_dict() -> dict:
"""Get a dictionary of color -> HTML color mappings."""
df = __get_colors()
return {row["color"]: row["hex"] for index, row in df.iterrows()}
COLORS = __get_colors_dict()
COLOR_POS_TAGS = frozenset({"JJ", "NN"})
def is_color(tagged_word: Tuple[str, str]) -> bool:
"""Determine if the given word is a color based on its part-of-speech.
:param tagged_word: A word that's been tagged with nltk.pos_tag()
"""
word, pos = tagged_word
return pos in COLOR_POS_TAGS and word in COLORS
However, this requires on each work in the corpus being tagged with its part of speech. This too is simple
def pos_tag(text: str) -> Iterable[Tuple[str, str]]:
"""Part-of-speech tag the given text.
"""
return nltk.pos_tag([w for w in text.split() if w not in {"/"}])
Now we can quite simply find the colors in a particular haiku as follows.
# A made-up haiku for testing purposes.
haiku = "dark blue lines / in a light olive green sea salt / dreams"
haiku_colors = [
tagged_word[0]
for tagged_word in nlp.pos_tag(haiku)
if haikulib.eda.colors.is_color(tagged_word)
]
print(haiku_colors)
this gives
['dark', 'blue', 'olive', 'green', 'sea']
But wait a minute! What about the color "dark blue", or "olive green"? In order to find multi-word colors, we need to parse and test n-grams from the haiku. Unfortunately it was quite late when I implemented this, so I was frustrated, and made it "just work" with no care for doing it right. It shows.
def find_colors(text: Iterable[Tuple[str, str]]) -> List[str]:
"""Return an unordered list of colors from the given POS-tagged text.
Check for 1, 2, and 3-gram colors like "dark blue".
Attempt to make the 1, 2, 3-grams exclusive so that a text containing "light olive green"
(#a4be5c) will return just
["light olive green"]
instead of
["light", "olive", "green", "olive green", "light olive green"]
:param text: The POS-tagged text to search for colors.
:return: A list of colors appearing in the provided text.
"""
colors = []
# Pad the right of any text that is too short to prevent a nasty crash.
ngrams = nltk.ngrams(text, n=3, pad_right=True, right_pad_symbol=("?", "??"))
for ngram in ngrams:
word = " ".join(w[0] for w in ngram)
# Check the 3-gram
if word in COLORS:
colors.append(word)
# Skip over the rest of this ngram.
next(ngrams)
next(ngrams)
# If the 3-gram wasn't a color, check the 2-gram.
else:
word = " ".join(w[0] for w in ngram[:2])
if word in COLORS:
colors.append(word)
# Skip over the rest of this ngram.
next(ngrams)
# If the 2-gram wasn't a color, check the 1-gram, using the tagged part-of-speech.
elif is_color(ngram[0]):
colors.append(ngram[0][0])
try:
# Check the last 2-gram and the last two 1-grams by hand (skipped by loop)
if ngram[1:] in COLORS:
word = " ".join(w[0] for w in ngram[1:])
colors.append(word)
else:
if is_color(ngram[-2]):
colors.append(ngram[-2][0])
if is_color(ngram[-1]):
colors.append(ngram[-1][0])
except UnboundLocalError:
# As with life, problems are best left ignored.
pass
return colors
Notice that we only use is_color()
for checking if 1-grams are colors. 2-grams and 3-grams
use a simple containment check against the color master list. Further, notice that there is
soul-crushing logic used to parse the colors ["light olive green", "sea"]
from the string
"light olive green sea"
instead of the colors
["olive", "green", "sea", "olive green", "light olive green"]
.
It even works on our test haiku that we defined.
find_colors(nlp.pos_tag(haiku))
gives us ['dark blue', 'light olive green', 'sea']
.
Then we parse the colors from the haiku during the repository data initialization step so that they are
stored in the data/haiku.csv
file. If we record the color usage for each haiku
individually, that enables spatial exploration of the colors.
df = data.get_df()
df.tail(6)
| # |
haiku | colors |
lines | syllables | total_syllables |
|---|---|---|---|---|---|
| 55357 | shadows a window ledge / just big enough to hold / two doves | [] | 3 | (7, 6, 3) | 16 |
| 55358 | sparks / in the heat of blue / dragonflies | [blue] | 3 | (1, 5, 3) | 9 |
| 55359 | half moon / only broken crackers / in the tin | [] | 3 | (2, 6, 3) | 11 |
| 55360 | revision notes / brushing sand / into the gaps | [sand] | 3 | (5, 3, 4) | 12 |
| 55361 | the first dusk of may / it's suddenly there / a pale orange poppy | [dusk, pale orange] | 3 | (5, 6, 7) | 18 |
| 55362 | dandelions / two drinkers unzip / by the path | [] | 3 | (3, 5, 3) | 11 |
| 55363 | scrunched up clouds / a blue plastic bag / in the nettles | [blue] | 3 | (4, 5, 4) | 13 |
| 55364 | early spring / one morning the clang of / scaffolding poles | [] | 3 | (3, 7, 5) | 15 |
| 55365 | platform 9 / through the glass roof the shapes / of swooping gulls | [glass] | 3 | (3, 7, 4) | 14 |
| 55366 | sneaking out / thru' the bathroom window / steam meets fog | [] | 3 | (3, 6, 3) | 12 |
As above, we also produce a data frame containing the corpus color counts and HTML color codes.
pos_tagging_color_counts = haikulib.eda.colors.get_colors()
total_color_count = pos_tagging_color_counts["count"].sum()
used_color_count = pos_tagging_color_counts["count"].astype(bool).sum(axis=0)
print(f"There are {total_color_count} occurences of color in the corpus")
print(f"There are {used_color_count} unique colors")
pos_tagging_color_counts[["color", "count", "hex"]].head(10)
| # | color |
count |
hex |
|---|---|---|---|
| 0 | cloudy blue | 0 | #acc2d9 |
| 1 | dark pastel green | 0 | #03c03c |
| 2 | dust | 207 | #b2996e |
| 3 | electric lime | 0 | #ccff00 |
| 4 | fresh green | 0 | #69d84f |
| 5 | light eggplant | 0 | #894585 |
| 6 | nasty green | 0 | #70b23f |
| 7 | really light blue | 0 | #d4ffff |
| 8 | tea | 246 | #65ab7c |
| 9 | warm purple | 0 | #952e8f |
Compare the POS-tagging results with those from the naive approach, summarized again below. Notice that we pruned over twenty color names from the corpus, and pruned over three thousand occurrences of colors.
total_color_count = naive_color_counts["count"].sum()
print(f"There are {total_color_count} occurences of color in the corpus")
print(f"There are {len(naive_color_counts)} unique colors")
naive_color_counts.head(10)
| # | color | count | hex |
|---|---|---|---|
| 0 | green | 415 | #3aa655 |
| 1 | snow | 1981 | #fffafa |
| 2 | dusk | 482 | #4e5481 |
| 3 | sea | 679 | #3c9992 |
| 4 | watermelon | 27 | #fd4659 |
| 5 | sky | 1232 | #82cafc |
| 6 | stone | 275 | #ada587 |
| 7 | jasmine | 66 | #f8de7e |
| 8 | sand | 300 | #c2b280 |
| 9 | rust | 27 | #b7410e |
Now that we've extracted the uses of color from the corpus, there are a number of techniques we can use to visualize that data. We will use the following methods:
See Word Clouds for a discussion of how to generate the word clouds for the haiku vocabulary in the general case, as well as restricted to flora, fauna, and colors. The color word cloud is reproduced below.
The color histogram, sorted by frequency, is show below.
colors = haikulib.eda.colors.get_colors()
colors.sort_values(by=["hsv", "count"], ascending=False, inplace=True)
used_colors = colors.loc[colors["count"] != 0].copy()
used_colors.sort_values(by="count", ascending=False, inplace=True)
_ = plt.bar(
range(len(used_colors)),
used_colors["count"],
color=used_colors["rgb"],
width=1,
linewidth=0,
log=True,
)
plt.show()
However, there are other ways we might display the same information.
used_colors.sort_values(by="hsv", ascending=False, inplace=True)
_ = plt.bar(
range(len(used_colors)),
used_colors["count"],
color=used_colors["rgb"],
width=1,
linewidth=0,
log=True,
)
plt.show()
background = plt.bar(
range(len(colors)),
height=12 ** 3,
width=1,
linewidth=0,
color=colors["rgb"],
log=True,
alpha=0.8,
)
foreground = plt.bar(
range(len(colors)),
height=colors["count"],
width=3,
linewidth=0,
color="black",
log=True,
)
plt.show()
We have several options for using a polar histogram:
def pairwise_difference(seq):
for l, r in utils.pairwise(seq):
yield r - l
# Loop back around to the front.
yield 2 * np.pi - seq[-1]
def accumulate(seq):
_sum = 0
for s in seq:
yield _sum
_sum += s
First, we'll sort the colors by their frequency and display them radially.
used_colors.sort_values(by="count", ascending=False, inplace=True)
ax = plt.subplot(111, projection="polar")
thetas = 2 * np.pi * used_colors["count"] / used_colors["count"].sum()
thetas = np.array(list(accumulate(thetas)))
widths = np.array(list(pairwise_difference(thetas)))
radii = np.log(used_colors["count"])
_ = ax.bar(
x=thetas,
height=radii,
width=widths,
color=used_colors["rgb"],
linewidth=0,
align="edge",
)
plt.show()
ax = plt.subplot(111, projection="polar")
_ = ax.bar(
x=thetas,
# Plot the same information with a fixed height.
height=1,
width=widths,
color=used_colors["rgb"],
linewidth=0,
align="edge",
)
plt.show()
I wanted to get a sense of the color palette in general, so let's try to sort the colors by hue. Unfortunately, this is a hard problem in general, because colors are three-dimensional, and we are attempting to sort them in a single dimension! There are techniques that utilize Hilbert curves to sort colors in two dimensions, but for now sorting by hue is sufficient.
ax = plt.subplot(111, projection="polar")
thetas = np.linspace(0, 2 * np.pi, len(used_colors), endpoint=False)
widths = 4 * np.pi / len(used_colors)
radii = np.log(used_colors["count"])
_ = ax.bar(
x=thetas,
height=radii,
width=widths,
color=used_colors["rgb"],
linewidth=0,
align="edge",
)
plt.show()
ax = plt.subplot(111, projection="polar")
thetas = 2 * np.pi * used_colors["count"] / used_colors["count"].sum()
thetas = np.array(list(accumulate(thetas)))
widths = np.array(list(pairwise_difference(thetas)))
radii = np.log(used_colors["count"])
_ = ax.bar(
x=thetas,
height=1,
width=widths,
color=used_colors["rgb"],
linewidth=0,
align="edge",
)
plt.show()
ax = plt.subplot(111, projection="polar")
_ = ax.bar(
x=thetas,
height=radii,
width=widths,
color=used_colors["rgb"],
linewidth=0,
align="edge",
)
plt.show()
I have no real conclusion, other than people who know how to do real color visualization are a lot smarter than I am. I thought this was cool, but ultimately I wasn't able to make any conclusions.


Reveal JS is a framework for doing slides presentations with plain html and JS
It is an alternative to presentation softwares
It:
Fork git@github.com:hakimel/reveal.js.git
Enable static content on settings
Start editing index.html
(...Ignore code before...)

Presentation title

Your name

Some really cool title

Some content

Ex: Using svg on react
React app embedded on slide


@NgModule
. These
decorators are similar to annotations in Java. Example: @Override
.AppModule
in Angular applications.Below is an example of an Angular2 module. To make it easy to understand I have made the below code a bit verbose by adding comments.


System Architect Symbol details for "Temporal Resource Act"
Activity (Class)
- Description:
Purposeful and intentional "Event"
- Comment:
-
Previous documented definitions providing the source for the above are:
An exertion of energy initiated by an "Agent" (CBS v2) - this definition is followed by a definition
of its subtypes.
Conscious, directed and purposeful exertion of energy.
"Event" occurring through the volition of an "Agent".
Definitions Used:
Quantity (Class Attribute)
Activity For Subject (Class)
- Description:
"Activity" directed towards a single "Subject".
- Comment:
-
Previous definitions from which the above have been distilled:
An act which changes either the state, or the knowledge of the state, of a "Subject" (CBS v2).
Activity/Direction (Generalization Node)
- Base Class:
Activity
- Definitions Used:
-
Activity (Class)
resource (Association)
- From Class:
"Temporal Resource"
- To Class:
"Temporal Resource Act"
- From Class:
"Temporal Resource"
- To Class:
"Temporal Resource Act"
source (Association)
- From Class:
"Temporal Resource Act"
- To Class:
"Temporal Resource For Activity"
- From Class:
"Temporal Resource Act"
- To Class:
"Temporal Resource For Activity"
State Change Activity (Class)
- Description:
"Activity" which changes the state of an "Object".
- Comment:
The "to" state is deemed to take effect simultaneously with the completion of the state change
activity. There will of course be occasions when the decision to make a state change is made well in
advance of this and that this decision may be communicated to interested parties.
Temporal Resource (Class)
- Description:
"Resource Held" whose availability may be expressed as a rate.
- Comment:
-
Temporal Resources are available only through the "Resource Activities" in which they are used.
Examples of these are:
Shift rotas (for people)
Booking rotas for buildings, rooms etc.
Temporal Resources may be composed of other Temporal Resources.
- Definitions Used:
-
Rate Of Availability (Class Attribute)
Temporal Resource Act (Class)
- Description:
"Activity" making a "Temporal Resource" available for a specified period.
- Comment:
-
Examples include:
Staff roster
Room schedule
Operating theatre session list.
Temporal Resource For Activity (Class)
- Description:
"Actual Resource For Activity" using a specific "Temporal Resource".


Indian Army’s Bomb Squad Successfully Defuses Major IED Bomb In Jammu And Kashmir
by Swarajya StaffThe Indian Army on Friday (22 November) successfully defused a major Improvised Explosive Device (IED) bomb on the National Highway-11 situated in the Union Territory (UT) of Jammu and Kashmir (J&K), reports Asian News International (ANI).
As the troops detected the bomb during a clearance, the forces immediately cordoned off the area, following which the Indian Army's bomb disposal squads rushed into the spot and proactively preventing the bomb blast.
Upon the development, Union Minister of State (MoS) in Prime Minister's Office, Jitendra Singh who also hails from the UT of J&K heaped praises on the Indian Army and said, "The way it was detected on time and was defused, the Army deserves congratulations for that."
MoS Singh also added, "Certainly, from the past few days, the way decisive action is being taken from the Army and security forces is appreciable." He also expressed confidence that this marked the end of terrorism in J&K.


When you asked to borrow a barrow from your neighbor, you were surprised to find he had already burrowed it away for safe-keeping. [G|D]
The farmer plowed the field with a harrow, creating deep furrows and providing a perfect environment for his farrow of pigs to explore and thrive. [G|D]
After searching through the narrow passageway, they stumbled across a secret chamber that contained a mysterious marrow that foretold the events of the morrow. [G|D]
When the young girl heard the sorrowful song of a sparrow perched in the yarrow, she was overwhelmed with a wave of emotion that filled her heart. [G|D]


This webpage retrives country wise coronavirus statistics from COVIS 19 API, a free API that sources data from Johns Hopkins CSSE. Thus, the
developer of this webpage is not responsible for data-accuracy.
Also, the developer couldn't manage to find a reliable data-source / api that returns
statistics for the number of tests from date to date. Who knows if the situation gets
worse
and lockdown continues then he might build one API himself on a lazy evening.


Software development refers to a set of computer science activities dedicated to the process of creating, designing, deploying and supporting software.”
AI system that can self-learn based on the algorithm. Systems that get smarter and smarter over time without human intervention is ML. Deep Learning (DL) is a machine learning (ML) applied to large data sets.
Internet of Things software enables creation of connected application ecosystems withing the enterprise.
At a high level, digital marketing refers to advertising delivered through digital channels such as search engines, websites, social media, email, and mobile apps.
Kittivaasal in on its mission to connect talented and aspiring workforce with the constantly evolving competent world. Our forever young team provides full service from market analysis, concept, design and development of appications, testing and marketing.
Kittivaasal's objective is to development & commercialization of Smart & Automations in IoT, Applications, Web services to create better futuristic development & marketing ecosystem.
Unique social platform for meme creator/trolls.
Intergrated manegement tool system for product/services startup/retails.
Fully automated iot product to collect/source seregation and space utilization for dry waste.
B2B software to connect waste management system and to track, monitor & manage the waste supply chain.
Next-Gen Insights And Technologies Designed To Enable Smart Operations.
Real-time Monitoring
Real-time monitoring of plant efficiency with user defined KPIs
Role-based Visualization
Role-based customizable view for CxO, Plant Manager, and Line Operator
Predictive Maintenance
Prediction feature for machine breakdown and line availability
Cost Analysis of OEE
Visualizing the financial impact of Availability loss, Performance loss, and Quality loss
Actionable Recommendations
Recommended actions on top of data to improve manufacturing productivity
Co-creating the smarter shop-floor and top-floor, powered by IoT
Reduce Downtime
Timely prediction of faults to better plan further actions.
Reduce Scrap Loss
Get real-time updates on output’s quality to take appropriate actions.
Reduce Inventory Carrying Cost
Increased visibility on real-time finished goods inventory.
Increase Productivity
Enables sync between actual production and planned production
Reduce Costs
Enables predictive maintenance instead of preventive maintenance, thus reducing machine maintenance cost.


I won my first art competition in the 4th grade, went on to show my work in several juried shows and I completed a BFA in illustration & design. My experience ranges from design to marketing with lots of odd jobs in the mix. I thrive in a creative enviornment & enjoy working with like minded individuals.
Most recently, I've decided to throw myself into the world of programing/web development. I aspire to experience as much as I can as well as continuing to grow and evolve.
Outside of work I devote my time to learning poledance, yoga, painting and all things outdoors. I love a challenge and new experiences.


Client:
Bumblebee Flower Farm
Info:
Fast moving physical business with rapid growth.
Purpose:
Redesign old site and incorporate ecommerce with a modified checkout flow.
Tools Used:
React, Next JS, Typescript, Sanity.io & Shopify
Bumblebee Flower Farm
Fast moving physical business with rapid growth.
Redesign old site and incorporate ecommerce with a modified checkout flow.
React, Next JS, Typescript, Sanity.io & Shopify
Donagh Long
Composer and Songwriter from Ireland.
Redesign old portfolio to be up to date with current web standards
React, Next JS, Typescript, Sanity.io & Stripe
Auratyk
Audio/Visual Artist Based in Berlin
Creative portfolio for Audio/Visual Artist
Vue, Nuxt JS, Notion.so & Three JS
Accountable
Freelancer Services
Digitization of complex German form needed to register as self-employed.
React, CRA, React-hook-form


subject
DOCUMENTS DIRECTORY KRSG1GXYX
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| SOCI-Sociology (TN eCampus) |
| C |
16954 |
SOCI |
1010 |
R01 |
96R |
3.000 |
Introduction to Sociology |
|
TBA |
0 |
1 |
-1 |
0 |
0 |
0 |
RODP Faculty (P) |
01/17-03/03 |
TBA TBA |
TN eCampus (formerly RODP) and GE Current: Social/Behavioral and TN eCampus-RODP Accelerated 1 and Credit from 4-Year College |
| C |
12803 |
SOCI |
1020 |
R50 |
96R |
3.000 |
Social Problems |
|
TBA |
0 |
1 |
-1 |
0 |
0 |
0 |
RODP Faculty (P) |
01/17-05/03 |
TBA TBA |
TN eCampus (formerly RODP) and GE Current: Social/Behavioral and Credit from 4-Year College |
| C |
12016 |
SOCI |
1120 |
R50 |
23R |
3.000 |
Intro to Cultural Anthropology |
|
TBA |
0 |
0 |
0 |
0 |
0 |
0 |
RODP Faculty (P) |
01/17-05/03 |
TBA TBA |
TN eCampus (formerly RODP) and GE Current: Social/Behavioral and Credit from 4-Year College |
| C |
12804 |
SOCI |
2010 |
R50 |
73R |
3.000 |
Marriage and Family |
|
TBA |
0 |
1 |
-1 |
0 |
0 |
0 |
RODP Faculty (P) |
01/17-05/03 |
TBA TBA |
TN eCampus (formerly RODP) and Credit from 4-Year College |
| C |
13732 |
SOCI |
217 |
R50 |
65R |
3.000 |
Sociology of Aging |
|
TBA |
0 |
1 |
-1 |
0 |
0 |
0 |
RODP Faculty (P) |
01/17-05/03 |
TBA TBA |
TN eCampus (formerly RODP) and Credit from 4-Year College |
| C |
12019 |
SOCI |
4510 |
R50 |
45R |
3.000 |
Introduction to Social Research |
|
TBA |
0 |
21 |
-21 |
0 |
0 |
0 |
RODP Faculty (P) |
01/17-05/03 |
TBA TBA |
TN eCampus (formerly RODP) and Credit from 4-Year College and Upper-Division Work |


Trader Drumond:
What would you like to do?
Buy equipment for Fighters.
Buy equipment for Wizards.
Buy a Shadow item.


| After serving jail time for a mysterious crime, Bill and Karl get out of jail and become preoccupied with figuring out who turned them in to the police. On top of that, the "family business" is on the rocks, and the motley crew of criminals who operate out of Down Terrace aren't feeling terribly trusting of one another. It might look like an ordinary house, but at Down Terrace, the walls are closing in.. | |
| Genres: | Comedy, Crime, Drama |
| Director: | Ben Wheatley |
| Writer: | Ben Wheatley, Robin Hill |
Cast: | Karl - Robin Hill Bill - Robert Hill Maggie - Julia Deakin Eric - David Schaal Valda - Kerry Peacock Garvey - Tony Way Berman - Mark Kempner Pringle - Michael Smiley Jon - Gareth Tunley Helen Garvey - Kali Peacock Child - Kitty Blue Spitz - Luke Hartney Bill's Band Member #1 - Simon Smith Bill's Band Member #2 - Paul George Bill's Band Member #3 - Simon Walker Mrs. Pringle - Janet Hill Radio Reporter - Sara Dee |


--- layout: post title: "CTF Practice" date: 2013-09-08 23:45:22 +0000 permalink: /2013/09/08/ctf-practice/ categories: Computer,Security redirect_from: - /blog/ctf-practice/ ---
Those who know me know that I might play in the occasional CTF competition. It's a good way to improve my skills, keep my mind sharp, and it's just plain fun. From a defensive security perspective, it's quite amazing to see how code that looks perfectly reasonable is, in fact, quite often very broken. If you've never done a CTF, you should watch @rogueclown's "If You Can Open A Terminal, You Can Capture the Flag."
I do some extra practice between CTFs -- I'm currently working my way through the challenges on OverTheWire.org, and they've recently added support for scoring via WeChall, a scoring site for a variety of CTF/challenge sites. In doing those, I've come across some good reading for anyone doing reversing/challenges/CTFs/etc:


amber$ ls
[CSS] position:absolute doesn't work!.html [CSS] height:100% doesn't work!.html [CSS] height or min-height?.html [CSS] how to make border of the element collapse?.html [CSS] how to set height and width for tag a!.html [CSS] Using fr instead of percentage in grid.html


the and that {background are for masks from margin {border-style The px;} #ffffff;} eye respiratory would mask was with â€". face have protection transmission use which they Figure not used well were been filtered goggles px; respirators then this very via {font-family COVID- However, font-size plague surgical virus will Maxcy This double; effective first infected into many masks, min more pandemic particle results time Doust blocking border-color border-width can current during has infection nose particles, prevent problem respirator solid; such there through transmission. {font-style {font-weight â€" Century For Infect. Lyon Public Respiratory Syndrome They against also bacteria being block bold but courier; donor droplets efficacy efficiency em; entry fogging given infections influenza, made mouth other particles pt;} reduce required sealed showing two valved while


--- layout: post status: publish published: true title: Funky Objects author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 586 wordpress_url: http://winterdom.com/2003/12/funkyobjects date: '2003-12-24 14:12:11 +0000' date_gmt: '2003-12-24 14:12:11 +0000' categories: - ".NET" tags: [] comments: [] ---
I always thought this was just pretty funky:
namespace System
{
class Object
{
}
class A
{
public static void Main()
{
System.Console.WriteLine("Hola");
}
}
}
Hint: If you haven't cought it yet, compile, then check the resulting code with ILDASM.


Heya Fellas!
This is just a casual blog maintained by me. There are also some posts by guest authors and the credits are provided per se. If you are interested in contributing, feel free to contact me. Hope you find some posts intriguing. P.S : If you find some grammatical errors, please message me so i can rectify them :/


Dongliang Chang (常东良)
Postdoctoral Researcher, Tsinghua University, Beijing, China
I am currently a Postdoc in the IVG Group at Tsinghua University, working under the supervision of Prof. Jiwen Lu. I received my Ph.D. Degree at Beijing University of Posts and Telecommunications (BUPT), where I was fortunate to be advised by Prof. Zhanyu Ma and Prof. Yi-Zhe Song. Moreover, I enriched my research experience as a Visiting Ph.D. Student at SketchX Lab. of the University of Surrey between 2022 and 2023.
My research interests primarily revolve around the field of fine-grained visual understanding, focusing specifically on image classification, change detection and forgery detection.
Latest News
Awarded Outstanding Graduate (Beijing & Beijing University of Posts and Telecommunications)!
Defended my PhD Thesis!
One paper accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence! (SCI, CCF A, Impact Factor: 24.314).
One paper (GPCA) has been newly selected as ESI Highly Cited Papers (Top 1% of papers in the academic field).
Two papers accepted by CVPR 2023! (CCF A Top Conference)
Our Fine-grained-Visual-Analysis-Library is released. [Code] [Document]


 Green foods are safe to eat as a regular part of a diet.
Category Quotes
Guideline related quotes related to Beverages and Green throughout Geoff Bond's publications.
One cup is one serving of water, milk, or nut milk. ~Paleo in a Nutshell p.95
Focus on “Green” and “ Green-Amber” beverages. ~Paleo in a Nutshell p.103
Additional Quotes
Specific references to Black Tea throughout Geoff Bond's publications.
While our forager forebears had no way of boiling water or making infusions, in this regard we agree with the USDA's advice: Suitable fluids can include tea, herbal tea, and coffee in moderation. In the great scheme of things, caffeine is not a huge issue, but it is wise to go easy on it, since it does interfere with blood sugar metabolism and can irritate many other metabolic functions. ~Paleo in a Nutshell p.50
Do not forget that tea, herbal teas, and coffee are valid thirst quenchers. Cocoa is fine too, but make it with 100 percent cocoa powder (not chocolate mix) combined with water or almond milk. If you like, use a "Green" sweetener or honey. ~Paleo in a Nutshell p.80
Caffeine in coffee and tea is sometimes demonized in health circles. Nevertheless, almost all vegetation contains some caffeine and the body is quite capable of processing it without distress. It is only in certain plants that the concentrations reach mind-altering proportions. In modest quantities, caffeine can give increased mental performance and improve mood. Increasing the dosage can cause some unpleasant symptoms, such as irritability, anxiety, jitteriness, headaches, and insomnia. In addition, at higher doses, caffeine drives up insulin levels, increases insulin resistance, and makes it harder to lose weight. However, the harmful effects of caffeine are reduced when it is present in tea or coffee. Scientists speculate that the other beneficial nutrients in these beverages compensate. Researchers have also found that a high consumption of micronutrient-rich plant food mitigates the harmful effects of caffeine on its own. ~Paleo in a Nutshell p.80
The average cup of American coffee contains 100 mg caffeine, whereas coffeehouse strength can be 180 mg per cup. The caffeine content of tea varies, but it averages about 40-50 mg per cup. There are also about 40 mg of caffeine in a 12-ounce can of cola. In a cocoa drink (2 teaspoons of pure cocoa powder), there are only 10 mg caffeine. Our view is that caffeine in modest amounts is well within the normal range for human consumption. (See "What Is 'Modest' Caffeine Consumption?" below.) For a 165- pound adult, that works out to four cups of American coffee per day or eight cups of tea. Be sensible about it, watch how your caffeine intake affects your mood, and avoid overdosing. If you are diabetic, caffeine in the form of moderate tea or coffee drinking might even be helpful. ~Paleo in a Nutshell p.80
Water should still be the main drink; try carbonated water with a twist of lemon. Unsweetened tea, iced or otherwise, is also okay. ~Paleo in a Nutshell p.107


Test purpose
- Check that the canvas completely fills the stack's dimensions on initial display
- Check the various fit modes by which the base (hidden) canvas can be copied over to its display canvas
- Check the canvas responds to changes in its container stack's dimensions, with no flickering as the dimensions change
- Check that the darker blue 'cell' canvas correctly tracks the mouse cursor when it hovers over the stack element, with no errors in positioning whatever the setting of the canvas object's 'fit' attribute
See Demo Canvas-036 for additional Cell functionality tests.
Touch test: should work as expected
Annotated code


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Hello World
I'm hosted with GitHub Pages. This is where my blog will go at some point in the future.


Fixed In 7 7 5 System Error Api Ms Win Crt Locale L1 1 0 Dll Is Missing Message Bug Reports For V7 Mybot Run
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| Option | Description |
| delay | Delay duration between two slides in micro seconds. Example: 5000 |
| animationSpeed | Fading Speed in micro seconds. Example: 2000 |
| animationType | fading/sliding - Default value is fading. |
| showNav | true/false - Default value is true. It will show/hide navigation icon |
| numericNav | true/false - Default value is false. If true, navigation will be numeric |
| autoSlide | true/false - Default value is true. Automatically start slideshow |
| showNextPrev | true/false - Default value is false. Either it will show next/prev button or not |
| showPlayButton | true/false - Default value is false. Either it will show play/pause button or not |
| pauseOnHover | true/false - Default value is false. Pause sliding on mouse hover |
Click here to download SKDSlider Joomla Module
Thanks :)


What is Meteor.js? What makes it different
- Meteor is an Open-source Fullstack Javascript Framework/Platform.
- An application built with Meteor runs on both the client
- Easily ported to Browser and Mobile Apps
- Live updates to the database with web sockets
- Unified package system with Atmosphere.js
- One of the first frameworks to essentially encompass the entire MEAN stack
MEAN stack VS. Meteor
| MEAN |
Meteor |
| MongoDB |
MongoDB (and MiniMongo) |
| Angular |
HandleBars (templating) |
| Node.js |
Built on top of Node.js |
| Express |
Examples
curl https://install.meteor.com/ | sh
Installs Meteor
meteor create APP-NAME-HERE
Creates Meteor App
meteor
Run App
Localhost
Running on Native Mobile
meteor install-sdk (android OR ios)
meteor add-platform (android OR ios)
meteor run (android OR ios)


Mike Denver column: Christmas music should be banned from shops and radio stations until December 8
It’s time to call a halt before Christmas turns into a 12-month commercially-driven affair
by Mike DenverChristmas music should be banned from shops and radio stations until December 8, that’s the traditional date for the start of the festive season, known to many as the farmers’ shopping day.
Those of us from outside the pale are known as culchies to the Dubs and for decades we used to descend on the capital on the church holiday when kids were off school on that date.
The Christmas kick-off moved back eight days to December 1 and more recently it has started in September!
It’s time to call a halt before it turns into a 12-month commercially-driven affair.
Call me Scrooge if you want, but the Christmas songs that are already playing in the shops will be on repeat until the first week of January.
This waters down the sense of occasion and makes the season monotonous and boring.
I could cope with an overdose of Shane McGowan’s Fairytale Of New York but when you hear White Christmas 400 times, it gets a bit ridiculous.
Everybody has a set amount of money to spend and all the Black Fridays, Cyber Mondays, Christmas songs or advertising in the world won’t increase that.
Maybe it’s time for people power, with a conscious effort not to go Christmas shopping until December.
Think of how much more simple life would be, particularly for parents.
Liz and I were shopping last week when a member of staff told us the Christmas music has been playing in the store for the past six weeks.
So spare a thought for retail staff for they are the unsung heroes of the festive season.
How to follow the Irish Mirror on social media
You can like our main Facebook page here.
Our Irish Mirror Sports page - which brings you all your Irish and UK sports news - can be found here.
You can also check out our Irish Mirror GAA sports page.
The Irish Mirror Twitter account is @IrishMirror while our sports account is @MirrorSportIE.
Our Instagram account can be found here.


Try out our Premium Member services: Virtual Legal Assistant, Query Alert Service and an ad-free experience. Free for one month and pay only if you like it.
HIGH COURT OF JUDICATURE FOR RAJASTHAN BENCH AT JAIPUR D.B. Civil Writ Petition(PIL) No. 2467 / 2014 Raaj Pal Chaudhary, Advocate, aged about 49 years, S/o Dr. D.S. Chaudhary, R/o D-29, Lal Bahadur Nagar, Near Fortis Escorts, J.L.N. Marg, Jaipur. ----Petitioner Versus 1. Chairman, State Pollution Control Board, 4, Jhalana Institutional Area, Jaipur. 2. Commissioner, Jaipur Development Authority, J.L.N. Marg, Jaipur. 3. Jaipur Nagar Nigam, Lalkothi, Jaipur. 4. The M.D. Jaipur Dairy, Zila Dugdha Utpadak Sahakari Sangh Ltd. Near Vidhya Ashram School, Jaipur. 5. The Chairman and Managing Director (C.M.D.) Rajasthan Co- operative Dairy Federation Ltd. J.L.N. Marg, Jaipur. 6. Managing Director, Jaipur City Transport Service Ltd. (JCTSL), Behind Nehru Place, Lal Kothi Subji Mandi, Jaipur. ----Respondents
_____________________________________________________ For Petitioner(s) : None present For Respondent(s) : Mr. Rupin Kala Mr. Akhil Simlote Mr. Sandeep Pathak Mr. Saurabh Jain on behalf of Mr. Anuroop Singhi Mr. Arafat Hussain on behalf of Mrs. Naina Saraf _____________________________________________________ HON'BLE THE CHIEF JUSTICE HON'BLE MR. JUSTICE DINESH CHANDRA SOMANI Order 18/12/2017
1. On 15.01.2015 limited notice was issued in the instant Public Interest Litigation relatable to prayer (b). The same is that (2 of 2) [CW-2467/2014] the respondents may be directed to cover the sewage treatment plant by pucca construction and put an exit on the same height as mentioned in the Pollution Control Act.
2. Issue concerns Jaipur Dairy and sewerage treatment plant installed in the precincts of the dairy.
3. By way of Application No.47710/2016, Jaipur Dairy has filed documents showing that it sought permission from Jaipur Nagar Nigam to lay down a pucca sewerage line and permission was granted on 27.06.2016. After completing the works, the Jaipur Nagar Nigam was informed on 04.07.2016 that the needful has been done. Thereafter the Rajasthan State Pollution Control Board inspected the precincts of Jaipur Dairy and on 31.08.2016 renewed the consent which was given for the sewerage treatment plant. The certificate by the Rajasthan State Pollution Control Board has been annexed as Annexure R-8/4 and the same evinces that the consent is till 30.04.2019.
4. Since the respondents have complied with what would have been the directions issued concerning prayer (b), the writ petition is disposed of as infructuous. (DINESH CHANDRA SOMANI),J. (PRADEEP NANDRAJOG),C.J. KKC/47


Wrap-up
If you’re doing it right, you will continue working to improve your
writing your whole career. We can only cover a bit in one week. Here are some tips for
things to do to continue:
- Read a lot. Ultimately, you will have to read a lot of journal articles in your
field to internalize the style of your field. By “style” here, I mean things like
what the Introduction normally covers (and in what depth), how limitations of the
study are discussed, how much detail should be included in the Methods, and how
materials are split between the main text and the Supplemental Online Material for
manuscripts. As a student, you will need to read a lot anyway to become familiar with
the literature. In later stages of your career, you will read a lot (of varying quality)
through service roles, like as a peer reviewer. At all stages, you will learn more about
what works and what doesn’t, what’s expected and what isn’t, in your field if you read
a lot of journal articles in that field.
- Find some favorite authors.
For environmental epidemiology, one of my favorite authors is Ben Armstrong. He can
explain very complex methodological approaches very clearly. For R programming, I love
reading things that have been written by Jenny Bryan and Richie Cotton.
- Work on grammar and usage or find a good copyeditor.
- Try out writing groups and sessions.
- Explore brainstorming, planning, and organizing tools for writing.
- Learn LaTeX.
- Find your favorite text editor and learn to use it well.
- Learn more about rhetoric.


- Feature implementation and maintenance on the centaur.ag platform.
George Koskeridis
Full-stack engineer
Volos
<EMAIL_ADDRESS>+30<PHONE_NUMBER>
Skills
Languages
Work Experience
- Engineered, designed and developed a dashboard web-app from scratch, that comes with a fully-responsive SPA front-end and a web-server with RESTful API endpoints, JWT Auth, role-based authorization, HTTP caching and more.
- Various features of the dashboard are support for widgets with dynamic filtering and layout, dark/light theming support, notifications and localization.
- The app includes an auditing system that connects to a 3DNetMedical PACS database and allows the hospital's security officer to audit all 3DNetMedical app activity and traffic. Moreover, there's an option to export audited results to CSV/JSON formats.
- Developed a WebExtensions browser addon that adds multi-monitor support to the Biotronics 3DNetMedical web-app. The add-on includes a custom algorithm that detects all monitors and their resolutions recursively through the browser APIs (the WebExtensions standard doesn't offer monitor/display support APIs).
Open-source
TODO: add open-source projects and contributions
Education
- Studying computer and electrical engineering with a focus on software engineering.


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