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Feature Requests for Tutanota

This is just an aggregate post with some feature requests I wanted to put out there. Some are petty, but they're just my very subjective suggestions. Sorry that it's a bit long. ^-^'
  1. I think it'd be nice as an organization administrator, to be able to define custom signatures, or signatures templates that can appear in the dropdown list of users in the organization.
For example, as the admin, I could add custom signature template which standardizes how users have their name, position, and contact information in the bottom of emails, or alternatively, a signature that includes a link to our pages like GitLab, Mastodon, and Matrix.
It would be even better if the administrator can also choose the default signature for any users in the organization.
  1. Where Tutanota instructs us on the records to add to our DNS; I think it would be more friendly to put an (i) next to each record with an explanation on why the record is required and what it does. I'm unsure if I should've been aware of MTA-STS for example, but I had never heard of the term. I appreciate there is a dedicated page which provides some information, but putting this in the app instead would be much more intuitive and quicker to access on demand.
  2. Tutanota should allow a list of trusted media sources. Currently, Tutanota blocks all images from all emails by default, including emails where the images have been displayed before. Clients like Thunderbird do this also by default, but also allow exceptions to be configured for example by sender address, by sender domain, or by resource location.
I'd like to be able to click the image icon, but instead of the dialog appearing, have a dropdown instead with options like:
Automatic image loading has been blocked to protect your privacy. * Unblock just this once. * Always unblock for emails sent from this user. * Always unblock for emails send from this domain. * Always unblock for the following resource locations: xyz.com, xyz.art, xyz.org
  1. As an administrator of an organization, I'd like to get a visual representation of the storage consumed by users. We're capable of seeing the total storage used, and the total storage used by users one at a time. It'd be more useful to get a pie chart that shows all users at once. It would be even better if the pie chart could have nested data to show where storage consumed is centralized in the archive that could be deleted to save up space. This is especially useful for redundant emails with binary files lost in archived emails.
  2. Currently, when looking in the Subscription settings, the "Storage Capacity" section can show the total used storage in different units, for example: "110.3 KB used of 1 GB". This can be tedious to read as usually, it's nicer to have relative figures, or a percentage. I think it would be much better if it displayed as "0.0001 GB used of 1 GB", or alternatively "0.01103% of storage used", or even both. This suggestion is only for using the same unit, or percentile. I don't know the right number of decimal places or significant figures for optimal UI/UX.
submitted by SethsUtopia to tutanota [link] [comments]

[OC] Predicting the 2019-20 Coach of the Year

For those interested, this is part of a very long blog post here where I explain my entire thought process and methodology.
This post also contains a series of charts linked to here.

Introduction

Machine Learning models have been used to predict everything in basketball from the All Star Starters to James Harden’s next play. One model that has never been made is a successful Coach of the Year Predictor. The goal of this project is to create such a model.
Of course, creating such a model is challenging because, ultimately, the COY is awarded via voting and inherently adds a human element. As we will discover in this post, accounting for these human elements (e.g. recency bias, weighing storylines, climate around the team) makes it quite challenging. Having said this, I demonstrate how we can gain insight into what voters have valued in the past, allowing us to propose the most likely candidates quite accurately.

Methods

Data Aggregation

First, I created a database of all the coaches referred to in Basketball Reference's coachs index
Coach statistics were acquired from the following template url:
f'https://www.basketball- reference.com/leagues/NBA_{season_end_year}_coaches.html'
Team statistics were acquired from the following template url:
f'https://www.basketball- reference.com/teams/{team_abbreviation}/{season_end_year}.html'
I leveraged the new basketball-reference-scraper Python module to simplify the process.
After some data engineering that I describe completely in the post, I settled on the following features.
Non numerical data Coach Statistics Team Data
COACH SEASONS WITH FRANCHISE SEASON
TEAM SEASONS OVERALL FG
CURRENT SEASON GAMES FGA
CURRENT SEASON WINS FG%
FRANCHISE SEASON GAMES 3P
FRANCHISE SEASON WINS 3PA
CAREER SEASON GAMES 3P%
CAREER SEASON WINS 2P
FRANCHISE PLAYOFF GAMES 2PA
FRANCHISE PLAYOFF WINS 2P%
CAREER PLAYOFF GAMES FT
CAREER PLAYOFF WINS FTA
COY FT%
ORB
DRB
TRB
AST
STL
BLK
TOV
PF
PTS
OPP_G
OPP_FG
OPP_FGA
OPP_FG%
OPP_3P
OPP_3PA
OPP_3P%
OPP_2P
OPP_2PA
OPP_2P%
OPP_FT
OPP_FTA
OPP_FT%
OPP_ORB
OPP_DRB
OPP_TRB
OPP_AST
OPP_STL
OPP_BLK
OPP_TOV
OPP_PF
OPP_PTS
AGE
PW
PL
MOV
SOS
SRS
ORtg
DRtg
NRtg
PACE
FTr
TS%
eFG%
TOV%
ORB%
FT/FGA
OPP_eFG%
OPP_TOV%
DRB%
OPP_FT/FGA
For obtaining a full description of each statistic, please refer to Basketball Reference's glossary.

Data Exploration

First, I computed the correlation between the COY label and all the other features and sorted them. Here are some of the top statistics that correlate with the award along with their Pearson correlation coefficient.
Statistic Pearson coefficient
CURRENT SEASON WINS 0.21764609944203592
SRS 0.20748396385759718
MOV 0.20740447792956693
NRtg 0.20613382194841318
PW 0.20282119218684597
PL -0.19850434198291064
DRtg -0.12967106743277185
ORtg 0.11896730313375109
As expected, the one of the most important features appears to be CURRENT SEASON WINS.
It is interesting the PW and PL correlate so much. This correlation indicates that not only does performance matter, but disparity between expected performance and reality matters significantly as well.
The weight put towards SRS, MOV, and NRtg also provide insight into how the COY is selected. Apparently, not only does it matter whether a team wins or not, but it also matters how they win. For example, the Bucks are typically winning games at an average of ~13 ppg this year, which would heavily favor them.
The high weight toward SRS(defined as a rating that takes into account average point differential and strength of schedule) indicates that it is even more important how a team performs against other challenging opponents. For example, no one should and does care about the Bucks crushing the Warriors, but they should care if they beat the Lakers.
Let's explore the CURRENT SEASON WINS statistic a little more using a box plot.
Box Plot
It appears coaches need to win ~50+ games for an 82 game season in order to be eligible. The exception being Mike Dunleavy’s minimum win season, there were only 50 games since it was a lockout season. Hence, that explains the outlier case.
Another interesting data point is the unfortunate coach who won the most games, but did not win the award. This turned out to be Phil Jackson, one year after his 72 win season in 1995-96 appeared to underperform by winning only 69 games. This, once again, indicates that the COY award takes into account historical performance. Who won instead? Pat Riley, with 61 wins.
Here are some histograms of the MOV and SRS where the blue plots indicate COY's and orange plots indicate NON-COY's.
As expected, COY’s are expected to dominate their teams and not just defeat them.

Oversampling

Before we begin, there is one key flaw in our dataset to look into. Namely, the two classes are not balanced at all.
Looking into the counts we have 1686 non-COY's and 43 COY's (as expected). This disparity can lead to a bad model, so how did I fix this?

SMOTE Oversampling

SMOTE (Synthetic Minority Over-sampling Technique) is a method of oversampling to even the distribution of the two classes. SMOTE takes a random sample from the minority class (COY=1 in our case) and computes it k-nearest neighbors. It chooses one of the neighbors and computes the vector between the sample and the neighbor. Next, it multiplies this vector by a random number between 0 and 1 and adds the vector to the original random sample to obtain a new data point.
See more details here.

Model Selection and Metrics

For this binary classification problem, we'll use 5 different models. Each model had its hyperparameters fine tuned using Grid Search Cross Validation to provide the best metrics. Here are all the models with a short description of each one: * Decision Tree Classifier - with Shannon's entropy as the criterion and a maximum depth of 37. * Random Forest Classifier - using the gini index as the criterion, maximum depth of 35 and maximum number of features of 5. * Logistic Classifier - using the simple ordinary least squares method * Support Vector Machine - with a linear kernel and C=1000 * Neural Network - a simple 6 layer network consisting of 80, 40, 20, 10, 5, 1 nodes, respectively (chosen to correspond with the number of features). I also used early stopping and 20% dropouts on each layer to prevent overfitting.
The metrics that will be used to evaluate our models are: NOTE that: TP=True Positives (Predicted COY and was a COY), TN=True Negatives (Predicted Not COY and was Not COY), FP=False Positives (Predicted COY and was Not COY), FN=False Negatives (Predicted Not COY and was COY)
  • Accuracy - % of correctly categorized instances ; Accuracy = (TP+TN)/(TP+TN+FP+FN)
  • Recall - Ability to categorize (+) class (COY) ; Recall = TP/(TP+FN)
  • Precision - How many of TP were correct ; Precision = TP/(TP+FP)
  • F1 - Balances Precision and Recall ; F1 = 2(Precision * Recall) / (Precision + Recall)

Results

Model Accuracy Recall Precision F1
Decision Tree 0.963 0.977 0.952 0.964
Random Forest 0.985 0.997 0.974 0.986
Logistic 0.920 0.980 0.870 0.922
SVC 0.959 0.991 0.932 0.960
Neural Network 0.898 1.0 0.833 0.909
In terms of all metrics the Random Forest outperforms all. Moreover, the Random Forest boasts an extremely high recall which is our most important metric. When predicting the Coach of the Year, we want to be able to predict the positive class best, which is indicated by a high recall.

Confusion Matrices

Confusion Matrices are another way of visualization our models' performances. Confusion Matrices are nxn matrices where the columns represent the actual class and the rows represent the class predicted by the model.
In the case of a binary classification problem, we obtain a 2x2 matrix with the true positives (bottom right), true negatives (top left), false positive (top right), and false negatives (bottom left).
Here are the confusion matrices for the Decision Tree, Random Forest, Logistic Classifier, SVC, and Neural Network.
Looking at the confusion matrices we can clearly see the disparity between the Random Forest Classifier and other classifiers. Evidently, the Random Forest Classifier is the best option.

Random Forest Evaluation

So what made the Random Forest so good? What features did it use that enabled it to make such accurate predictions?
I charted the feature importances of the Random Forest and plotted them in order here.
Here are some explicit numbers:
Feature % Contribution
CURRENT SEASON WINS 6.569329857214043
SRS 6.368785568654217
PW 6.059094690243399
NRtg 5.5519116066060175
MOV 4.473122672559081
PL 3.643349558354282
... ...
See more in my blog post.
I found it, once again, interesting that SRS such an important feature. It appears that the Random Forest took the correlation predicted earlier into account.
However, we see that other statistics matter significantly too, like CURRENT SEASON WINS, NRtg, and MOV as we predicted.
Something one wouldn’t anticipate is the contribution of factors outside of this season like FRANCHISE and CAREER features. Along these lines, one wouldn’t expect PW or PL to matter too much, but this model indicates that it is one of the most important features.
Let’s also take a look at where the random forest failed. If you recall from the confusion matrix, there was one instance where a COY was classified as NOT COY.
The point is the 1976 COY who was categorized as not COY. This individual was coach Bill Fitch of the 1975-76 Cleveland Cavaliers. He had a modest win record of 49-33 during an overall down year where the top record was the 54-28 Lakers. Looking at the modern era where 60 win records and obscene statistics are put up on a regular basis, I would say that this is not a terrible error on our model's part.
The reason the model may have classified this as a NOT COY instance is due to the fact that the team's statistics aren't all that impressive, but impressive with respect to THAT year. This lack of incorporating other team performances during the year may be the biggest flaw in our model.

Predicting the next Coach of the Year

Unfortunately, we do not have all the statistics for the current year, but we will obtain what we can and modify the data as we did earlier.
Note that all our data is PER GAME, so for all of these statistics, we will just use the PER GAME statistics up to this point (1/21/20)
The only unrealistic statistic is, then, CURRENT SEASON statistics. We will assume CURRENT SEASON GAMES will be 82 for all coaches and obtain CURRENT SEASON WINS from 538's ELO projections on 1/21/20.
Once again, all other stats were acquired via the basketball_reference_scraper Python package.
Team Probability to win COY
MIL 0.49
TOR 0.46
LAC 0.36
BOS 0.31
HOU 0.23
LAL 0.22
DAL 0.22
MIA 0.17
DEN 0.16
IND 0.13
UTA 0.12
PHI 0.12
DET 0.09
NOP 0.07
WAS 0.05
SAS 0.04
ORL 0.04
CHI 0.04
BRK 0.04
POR 0.03
PHO 0.03
OKC 0.03
CHO 0.03
NYK 0.02
SAC 0.01
MIN 0.01
GSW 0.01
ATL 0.01
MEM 0.0
CLE 0.0
This shows the probability of each coach to win COY in the current season. Let's take a look at each of the candidates in order:
1) Milwaukee Bucks & Mike Budenholzer (49%)
Mike Budenholzer was the COY in the 2018-19 season and, objectively, the top candidate for COY this year as well. The Bucks are on a nearly 70-win pace which would automatically elevate him to the top spot.
However, the model is purely objective and fails to incorporate human elements such as the fact that individuals look at the Bucks skeptically as a 'regular season team'. Voters will likely avoid Budenholzer until there is more playoff success.
Moreover, Budenholzer won last year and voters almost never vote for the same candidate twice in a row. In fact, a repeat performance has never occurred in the COY award.
We see here the flaw in the model to not weight the human elements of recency bias against previous COY's and playoff success sufficiently.
2) Toronto Raptors & Nick Nurse (46%)
The Raptors are truly an incredible story this year. No one expected them to be this good. Even the ELO ratings put them at an expected 56 wins this season and be tied for the 3rd best record in the league behind the Lakers and Bucks.
The disparity between what people expected of the Raptors and what has actually transpired (despite injuries to significant players such as Lowry and Siakam) indicates that Nurse would be a viable candidate for COY.
3) Los Angeles Clippers & Doc Rivers (36%)
Despite the model favoring Doc Rivers, I believe it is unlikely that he wins COY due to the current stories circulating around the Clippers.
Everyone came into the season expecting the Clippers to blow everyone out of the water in the playoffs. No one expects the Clippers to exceed expectations during the regular season, especially with their superstars Kawhi Leonard and Paul George being the role models of load management.
4) Boston Celtics & Brad Stevens (31%)
Brad Stevens is another likely candidate for the COY. Not ony are the Celtics objectively impressive, but they also have the narrative on their side. After last year's disappointing performance, people questioned Stevens, but their newfound success without Kyrie Irving has pushed the blame onto Irving over Stevens. Moreover, significant strides have been made by their young players Jaylen Brown and Jayson Tatum vaulting them into Eastern Conference champion contention.
5) Los Angeles Lakers & Frank Vogel (22%)
Being in tune with the current basketball landscape through podcasts and articles, I can tell that Frank Vogel's campaign for the COY is quite strong. Over and over again we hear praises from players like Anthony Davis and Danny Green on the recent Lowe Post on how happy the Lakers are.
With the gaudy record, spotlight and percolating positive energy around the Lakers, Vogel is a very viable pick for the COY.
6) Dallas Mavericks & Rick Carlisle (22%)
Tied with Vogel is Rick Carlisle and the Dallas Mavericks. The Dallas Mavericks, along with the Raptors, are perhaps the most unexpected successful team this season. Looking at their roster, no one stands out except for Porzingis and Doncic, but they still tout a predicted record of 50-32.
Once again, the disparity between expectations and reality puts Carlisle in high contention of the COY.

Conclusion

Overall, I'm quite pleased with the Random Forest model's metrics. The predictions made by the model for the current 2019-20 appear on point as well. The model appears to favor the disparity between what people expected of teams and their performance on the court quite well. However, the flaw in the model is the lack of weighing recent events properly as we saw with coach Budenholzer.
Once again, predicting the COY is a challenging task and we cannot expect the model to be perfect. Yet, we can gain insight into what voters have valued in the past, allowing us to propose the most likely candidates quite accurately.
submitted by vagartha to nba [link] [comments]

[OC] Predicting the 2019-20 Coach of the Year

For those interested, this is part of a very long blog post here where I explain my entire thought process and methodology.
This post also contains a series of charts linked to here.

Introduction

Machine Learning models have been used to predict everything in basketball from the All Star Starters to James Harden’s next play. One model that has never been made is a successful Coach of the Year Predictor. The goal of this project is to create such a model.
Of course, creating such a model is challenging because, ultimately, the COY is awarded via voting and inherently adds a human element. As we will discover in this post, accounting for these human elements (e.g. recency bias, weighing storylines, climate around the team) makes it quite challenging. Having said this, I demonstrate how we can gain insight into what voters have valued in the past, allowing us to propose the most likely candidates quite accurately.

Methods

Data Aggregation

First, I created a database of all the coaches referred to in Basketball Reference's coachs index
Coach statistics were acquired from the following template url:
f'https://www.basketball- reference.com/leagues/NBA_{season_end_year}_coaches.html'
Team statistics were acquired from the following template url:
f'https://www.basketball- reference.com/teams/{team_abbreviation}/{season_end_year}.html'
I leveraged the new basketball-reference-scraper Python module to simplify the process.
After some data engineering that I describe completely in the post, I settled on the following features.
Non numerical data Coach Statistics Team Data
COACH SEASONS WITH FRANCHISE SEASON
TEAM SEASONS OVERALL FG
CURRENT SEASON GAMES FGA
CURRENT SEASON WINS FG%
FRANCHISE SEASON GAMES 3P
FRANCHISE SEASON WINS 3PA
CAREER SEASON GAMES 3P%
CAREER SEASON WINS 2P
FRANCHISE PLAYOFF GAMES 2PA
FRANCHISE PLAYOFF WINS 2P%
CAREER PLAYOFF GAMES FT
CAREER PLAYOFF WINS FTA
COY FT%
ORB
DRB
TRB
AST
STL
BLK
TOV
PF
PTS
OPP_G
OPP_FG
OPP_FGA
OPP_FG%
OPP_3P
OPP_3PA
OPP_3P%
OPP_2P
OPP_2PA
OPP_2P%
OPP_FT
OPP_FTA
OPP_FT%
OPP_ORB
OPP_DRB
OPP_TRB
OPP_AST
OPP_STL
OPP_BLK
OPP_TOV
OPP_PF
OPP_PTS
AGE
PW
PL
MOV
SOS
SRS
ORtg
DRtg
NRtg
PACE
FTr
TS%
eFG%
TOV%
ORB%
FT/FGA
OPP_eFG%
OPP_TOV%
DRB%
OPP_FT/FGA
For obtaining a full description of each statistic, please refer to Basketball Reference's glossary.

Data Exploration

First, I computed the correlation between the COY label and all the other features and sorted them. Here are some of the top statistics that correlate with the award along with their Pearson correlation coefficient. |Statistic|Pearson coefficient| |--|--| |CURRENT SEASON WINS|0.21764609944203592| |SRS|0.20748396385759718| |MOV|0.20740447792956693| |NRtg|0.20613382194841318| |PW|0.20282119218684597| |PL|-0.19850434198291064| |DRtg|-0.12967106743277185| |ORtg|0.11896730313375109|
As expected, the one of the most important features appears to be CURRENT SEASON WINS.
It is interesting the PW and PL correlate so much. This correlation indicates that not only does performance matter, but disparity between expected performance and reality matters significantly as well.
The weight put towards SRS, MOV, and NRtg also provide insight into how the COY is selected. Apparently, not only does it matter whether a team wins or not, but it also matters how they win. For example, the Bucks are typically winning games at an average of ~13 ppg this year, which would heavily favor them.
The high weight toward SRS(defined as a rating that takes into account average point differential and strength of schedule) indicates that it is even more important how a team performs against other challenging opponents. For example, no one should and does care about the Bucks crushing the Warriors, but they should care if they beat the Lakers.
Let's explore the CURRENT SEASON WINS statistic a little more using a box plot.
Box Plot
It appears coaches need to win ~50+ games for an 82 game season in order to be eligible. The exception being Mike Dunleavy’s minimum win season, there were only 50 games since it was a lockout season. Hence, that explains the outlier case.
Another interesting data point is the unfortunate coach who won the most games, but did not win the award. This turned out to be Phil Jackson, one year after his 72 win season in 1995-96 appeared to underperform by winning only 69 games. This, once again, indicates that the COY award takes into account historical performance. Who won instead? Pat Riley, with 61 wins.
Here are some histograms of the MOV and SRS where the blue plots indicate COY's and orange plots indicate NON-COY's.
As expected, COY’s are expected to dominate their teams and not just defeat them.

Oversampling

Before we begin, there is one key flaw in our dataset to look into. Namely, the two classes are not balanced at all.
Looking into the counts we have 1686 non-COY's and 43 COY's (as expected). This disparity can lead to a bad model, so how did I fix this?

SMOTE Oversampling

SMOTE (Synthetic Minority Over-sampling Technique) is a method of oversampling to even the distribution of the two classes. SMOTE takes a random sample from the minority class (COY=1 in our case) and computes it k-nearest neighbors. It chooses one of the neighbors and computes the vector between the sample and the neighbor. Next, it multiplies this vector by a random number between 0 and 1 and adds the vector to the original random sample to obtain a new data point.
See more details here.

Model Selection and Metrics

For this binary classification problem, we'll use 5 different models. Each model had its hyperparameters fine tuned using Grid Search Cross Validation to provide the best metrics. Here are all the models with a short description of each one: * Decision Tree Classifier - with Shannon's entropy as the criterion and a maximum depth of 37. * Random Forest Classifier - using the gini index as the criterion, maximum depth of 35 and maximum number of features of 5. * Logistic Classifier - using the simple ordinary least squares method * Support Vector Machine - with a linear kernel and C=1000 * Neural Network - a simple 6 layer network consisting of 80, 40, 20, 10, 5, 1 nodes, respectively (chosen to correspond with the number of features). I also used early stopping and 20% dropouts on each layer to prevent overfitting.
The metrics that will be used to evaluate our models are: NOTE that: TP=True Positives (Predicted COY and was a COY), TN=True Negatives (Predicted Not COY and was Not COY), FP=False Positives (Predicted COY and was Not COY), FN=False Negatives (Predicted Not COY and was COY)
  • Accuracy - % of correctly categorized instances ; Accuracy = (TP+TN)/(TP+TN+FP+FN)
  • Recall - Ability to categorize (+) class (COY) ; Recall = TP/(TP+FN)
  • Precision - How many of TP were correct ; Precision = TP/(TP+FP)
  • F1 - Balances Precision and Recall ; F1 = 2(Precision * Recall) / (Precision + Recall)

Results

Model Accuracy Recall Precision F1
Decision Tree 0.963 0.977 0.952 0.964
Random Forest 0.985 0.997 0.974 0.986
Logistic 0.920 0.980 0.870 0.922
SVC 0.959 0.991 0.932 0.960
Neural Network 0.898 1.0 0.833 0.909
In terms of all metrics the Random Forest outperforms all. Moreover, the Random Forest boasts an extremely high recall which is our most important metric. When predicting the Coach of the Year, we want to be able to predict the positive class best, which is indicated by a high recall.

Confusion Matrices

Confusion Matrices are another way of visualization our models' performances. Confusion Matrices are nxn matrices where the columns represent the actual class and the rows represent the class predicted by the model.
In the case of a binary classification problem, we obtain a 2x2 matrix with the true positives (bottom right), true negatives (top left), false positive (top right), and false negatives (bottom left).
Here are the confusion matrices for the Decision Tree, Random Forest, Logistic Classifier, SVC, and Neural Network.
Looking at the confusion matrices we can clearly see the disparity between the Random Forest Classifier and other classifiers. Evidently, the Random Forest Classifier is the best option.

Random Forest Evaluation

So what made the Random Forest so good? What features did it use that enabled it to make such accurate predictions?
I charted the feature importances of the Random Forest and plotted them in order here.
Here are some explicit numbers:
Feature % Contribution
CURRENT SEASON WINS 6.569329857214043
SRS 6.368785568654217
PW 6.059094690243399
NRtg 5.5519116066060175
MOV 4.473122672559081
PL 3.643349558354282
... ...
See more in my blog post.
I found it, once again, interesting that SRS such an important feature. It appears that the Random Forest took the correlation predicted earlier into account.
However, we see that other statistics matter significantly too, like CURRENT SEASON WINS, NRtg, and MOV as we predicted.
Something one wouldn’t anticipate is the contribution of factors outside of this season like FRANCHISE and CAREER features. Along these lines, one wouldn’t expect PW or PL to matter too much, but this model indicates that it is one of the most important features.
Let’s also take a look at where the random forest failed. If you recall from the confusion matrix, there was one instance where a COY was classified as NOT COY.
The point is the 1976 COY who was categorized as not COY. This individual was coach Bill Fitch of the 1975-76 Cleveland Cavaliers. He had a modest win record of 49-33 during an overall down year where the top record was the 54-28 Lakers. Looking at the modern era where 60 win records and obscene statistics are put up on a regular basis, I would say that this is not a terrible error on our model's part.
The reason the model may have classified this as a NOT COY instance is due to the fact that the team's statistics aren't all that impressive, but impressive with respect to THAT year. This lack of incorporating other team performances during the year may be the biggest flaw in our model.

Predicting the next Coach of the Year

Unfortunately, we do not have all the statistics for the current year, but we will obtain what we can and modify the data as we did earlier.
Note that all our data is PER GAME, so for all of these statistics, we will just use the PER GAME statistics up to this point (1/21/20)
The only unrealistic statistic is, then, CURRENT SEASON statistics. We will assume CURRENT SEASON GAMES will be 82 for all coaches and obtain CURRENT SEASON WINS from 538's ELO projections on 1/21/20.
Once again, all other stats were acquired via the basketball_reference_scraper Python package.
Team Probability to win COY
MIL 0.49
TOR 0.46
LAC 0.36
BOS 0.31
HOU 0.23
LAL 0.22
DAL 0.22
MIA 0.17
DEN 0.16
IND 0.13
UTA 0.12
PHI 0.12
DET 0.09
NOP 0.07
WAS 0.05
SAS 0.04
ORL 0.04
CHI 0.04
BRK 0.04
POR 0.03
PHO 0.03
OKC 0.03
CHO 0.03
NYK 0.02
SAC 0.01
MIN 0.01
GSW 0.01
ATL 0.01
MEM 0.0
CLE 0.0
This shows the probability of each coach to win COY in the current season. Let's take a look at each of the candidates in order:
1) Milwaukee Bucks & Mike Budenholzer (49%)
Mike Budenholzer was the COY in the 2018-19 season and, objectively, the top candidate for COY this year as well. The Bucks are on a nearly 70-win pace which would automatically elevate him to the top spot.
However, the model is purely objective and fails to incorporate human elements such as the fact that individuals look at the Bucks skeptically as a 'regular season team'. Voters will likely avoid Budenholzer until there is more playoff success.
Moreover, Budenholzer won last year and voters almost never vote for the same candidate twice in a row. In fact, a repeat performance has never occurred in the COY award.
We see here the flaw in the model to not weight the human elements of recency bias against previous COY's and playoff success sufficiently.
2) Toronto Raptors & Nick Nurse (46%)
The Raptors are truly an incredible story this year. No one expected them to be this good. Even the ELO ratings put them at an expected 56 wins this season and be tied for the 3rd best record in the league behind the Lakers and Bucks.
The disparity between what people expected of the Raptors and what has actually transpired (despite injuries to significant players such as Lowry and Siakam) indicates that Nurse would be a viable candidate for COY.
3) Los Angeles Clippers & Doc Rivers (36%)
Despite the model favoring Doc Rivers, I believe it is unlikely that he wins COY due to the current stories circulating around the Clippers.
Everyone came into the season expecting the Clippers to blow everyone out of the water in the playoffs. No one expects the Clippers to exceed expectations during the regular season, especially with their superstars Kawhi Leonard and Paul George being the role models of load management.
4) Boston Celtics & Brad Stevens (31%)
Brad Stevens is another likely candidate for the COY. Not ony are the Celtics objectively impressive, but they also have the narrative on their side. After last year's disappointing performance, people questioned Stevens, but their newfound success without Kyrie Irving has pushed the blame onto Irving over Stevens. Moreover, significant strides have been made by their young players Jaylen Brown and Jayson Tatum vaulting them into Eastern Conference champion contention.
5) Los Angeles Lakers & Frank Vogel (22%)
Being in tune with the current basketball landscape through podcasts and articles, I can tell that Frank Vogel's campaign for the COY is quite strong. Over and over again we hear praises from players like Anthony Davis and Danny Green on the recent Lowe Post on how happy the Lakers are.
With the gaudy record, spotlight and percolating positive energy around the Lakers, Vogel is a very viable pick for the COY.
6) Dallas Mavericks & Rick Carlisle (22%)
Tied with Vogel is Rick Carlisle and the Dallas Mavericks. The Dallas Mavericks, along with the Raptors, are perhaps the most unexpected successful team this season. Looking at their roster, no one stands out except for Porzingis and Doncic, but they still tout a predicted record of 50-32.
Once again, the disparity between expectations and reality puts Carlisle in high contention of the COY.

Conclusion

Overall, I'm quite pleased with the Random Forest model's metrics. The predictions made by the model for the current 2019-20 appear on point as well. The model appears to favor the disparity between what people expected of teams and their performance on the court quite well. However, the flaw in the model is the lack of weighing recent events properly as we saw with coach Budenholzer.
Once again, predicting the COY is a challenging task and we cannot expect the model to be perfect. Yet, we can gain insight into what voters have valued in the past, allowing us to propose the most likely candidates quite accurately.
submitted by vagartha to nbadiscussion [link] [comments]

Testing out the Apocalypse Oracle by playing There Is No Spoon [Session 2]

When we last left off Zero, he infiltrating the Horakthy, a hoverbarge. He noticed that its owner, Ra, has turned it into his own personal kitchen and cooking up humans. Ra kicked Zero’s ass and has left him to be cooked. As I said before, this will launch the next phase of the playtest, which explores the four tables I didn’t touch previously.
Two of these tables are for Dungeon and Hex Crawling, while the other two generate NPCs and Plot Points. For the context of this game, the Dungeon is the Horakthy and the Hexes are everything going on outside of it.
First is the Hex. Now, this is mostly reserved for hex maps, but I’m going to try and doodle one out. The Hex and Dungeon section have four tables that are to be rolled on. For Hex, it’s Terrain, Contents, Features (if the roll calls for one) and an Event. For the most part, Terrain and Contents are rolled.
To save you about seven rolls, the most noteworthy stuff is that we’re currently over a ‘farm’ and the Machines have taken noticed and have sent some Sentinels out to take care of the encroachers. We’re also near an active pit of molten steel.
Next up is the “dungeon”. Like before, four tables to roll on: Location, Encounter, Objects, and Exits. In this case, I rolled up that I was in the kitchen with an interesting item… I’ll conclude that it’s a butcher’s knife. With that, I begin the game.
I’m setting up the scene so that my character is going to grab the knife and sneak out. The scene complication I rolled is 4, Behaviour, an NPC acts suddenly… This is our chance to generate an NPC for us to encounter.
Three tables for an NPC are rolled by a D6, while one of them is drawn. The three that are to be rolled are Social Position, Notable Feature, and Attitude. First, I’ll ask the Oracle if the NPC in question is the butcher. The odds are likely. It’s a yes, so we don’t need to know the Social Position. His notable feature is an obvious physical trait while his attitude is withdrawn. I suspect a bulge on his forehead that makes it difficult to see straight ahead. The final table is Conversation Focus, but because he’s not too keen on talking, I’ll skip that for now.
I’ll ask the oracle if I have the knife. Odds are even. It’s a 4 & 6. Yes and I have advantage in stabbing him. Unfortunately, our successes cancel each other out (the additional D6 I rolled was over my Knife-Fu score of 3) and we roll again. He knocks me down again and…
{sigh}
Okay, I get it. My character’s outnumbered. This is the third time he’s lost a fight and is at the mercy of the villains. There’s absolutely no way he could overcome this. The only salvation I have is to invoke deus ex machina…
The Sentinels attack the Horakthy and the impact knocked the butcher out just as he was about to finish Zero off. He runs off before the butcher can come to. I’ll also roll a Pacing and a Soft move, since the situation calls for both a “what now” and a consequence. 5 & 4 show Advance the plot and Advance a Threat so… Yeah.
Scene 2 happens. Set up is as follows. The Horakthy moves north while the Sentinels follow. This expands further into the farmlands. Meanwhile, my character advances further into the dungeon. He goes into a room with a special feature, no encounter, an interesting item or clue, and three exits, with one of them connecting to the existing area. I’m going to ask the Description table what the special feature is. I drew a four of spades, which reveals to be old in operation… Hmm… The engine! The scene will have my character jam the knife right into the engine.
However, I rolled a 1, revealing that the butcher has returned. Gonna smash his head against the engine, then stab him to it. I fail and he grabs a hold of me. Soft Move time. 5, reveal an unwelcome truth… Plot Hook Generation time.
Okay, this isn’t exactly the best way to use this, since the Plot Hook generator is meant to be used to kick start the adventure rather than be used in the adventure, but I’m going to use it the best way I can… There are four tables, like with the NPC Generator. Three of which rolled with dice, the last drawn as a card. The dice will reveal the objective, adversaries, and rewards, while the card reveals the plot focus.
In order, the dice and cards reveal that the objective is to escort or deliver something that’s advanced in nature, overcoming outlaws for the reward of a powerful item. To translate this as a plot twist, it turns out that the Horakthy are transporting a new means for Hovercrafts to move around, as the engine is revealed to be a person plugged into the Matrix, having rigged a simulation and programmed it to also pilot the hovercraft. The “outlaws” in question are Zero and his brother.
He really shouldn’t have revealed that to a person who has a knife. Zero tries to stab the cord that ties the pilot into the Matrix and succeeds, severing the pilot prematurely and killing him, causing the hovercraft to lose control. With advantage, Zero tries to finish off the butcher and finally, with a 2 overcoming the 6, stabs the butcher right into the neck and finishes him for good…
With the hovercraft now going out of control, Zero goes to confront Ra once and for all. Another scene, another update. The hovercraft is going to crash into the desert of the real. As we head to the room, our encounter die results in a six, which is the encounter we want: Ra. No objects, no exits, Fox only, final destination!
In the midst of the battle, the Sentinel force manage to catch up with the Horakhty, their mecha-tentacles wrapping themselves around the hovercraft. Eventually, Zero manages to run Ra through with the butcher knife, utilizing it like he would his katana, kill Ra, then rush over to jack into the Matrix and have his brother hack him back into his hovercraft.
Asking the chart, I roll a 6 with is a hard-locked yes. Zero escapes into the Matrix using the spare pilot seat (and needing to fix the cord he severed), which is him entering the Matrix, waiting for the phone call, picking it up, and going back to his base, all while the machines take hold of the Horakthy and return the stolen pods back to their farm. Zero jokes about how he’ll never raid a hovercraft again and asks Binary if he can load in some data on how to fight hand to hand. Unknowingly, he had abused the Matrix and thus, fulfilled his fate. His Matrix stat goes up.
And that ends this two-part session of the Apocalypse Oracle playtest. Now I shall give my full thoughts on the Oracle: It’s good. It does what it says on paper effectively and efficiently and is a good oracle to have on hand for quick games. And, despite its simplistic nature, it manages to include everything a regular soloist would need to facilitate their game while adding more to the solo experience.
What do I mean? Well, compared to Mythic and CRGE, there’s a lot more to do in regards to setting up scenes and moving the story along. For Mythic, you basically roll a D10 and if its number is within the chaos factor, the scene is altered. If you play the game without your characters losing control, you’ll have about a 2-in-10 chance of a scene going wrong, if you count 0s as its own number. Apocalypse Oracle, however, has a 1-in-6 chance of the scene going right. The Oracle assumes something will go wrong, which sets up a lot more chaos than Mythic does.
The other bit is the three moves: Pacing, Soft, and Hard. These tables were tables I kept going back to when my character was failing his battle against Ra and the Butcher, since it was a lot better than saying “my character fails again”.
One table I didn’t use a lot was the NPC and Enemy Moves table, though I felt as though it wouldn’t have spiced up any of the action. Using the fight with Ra as an example, the obvious actions were to kill Zero, they’re not in the Matrix so using a special ability wouldn’t work, and I haven’t set up any sort of personality trait outside of “cannibal” and “bitch in sheep’s clothing”. This leaves “seek an advantage” and “does something unexpected” as the viable routes that could have changed up how the fight goes. It is a pretty good table, and seeing the example of play that was shown, it’s clear it’s more for non-combat purposes.
Though, by far, I think the best type of table has to be the unique Card drawing tables. It’s probably the first time I’ve seen a unique style of oracle that describes something and has a lot of different interpretations based off what it does. This means you can literally have 52 different meanings, which, while it isn’t much compared to Mythic’s 10,000 combinations though its Event Meaning page, offers a lot more variation thanks in part to the minimalist wording of the options followed by how each of the suits have at least two different meanings, not to mention the X factor that is context.
Overall, this is a great oracle to have when you just need to get into playing a game. Would this replace Mythic or CRGE? No, but it definitely earns a spot in the go-to oracles I can use when those two tire me, or when the situation calls for me using it. Such as the case with an idea of making a sequel session to this Matrix game or even a game of Swords & Six-Siders. In any case, this was a good Oracle and is worth a checking out.
Thanks again to u/archon1024 for allowing me to playtest the engine.
submitted by Psyga315 to Solo_Roleplaying [link] [comments]

netdata, the open-source real-time performance and health monitoring, released v1.17 !

Hi all,
Release v1.17.0 contains 38 bug fixes, 33 improvements, and 20 documentation updates.
At a glance
You can now change the data collection frequency at will, without losing previously collected values. A major improvement to the new database engine allows you not only to store metrics at variable granularity, but also to autoscale the time axis of the charts, depending on the data collection frequencies used during the presented time.
You can also now monitor VM performance from one or more vCenter servers with a new VSphere collector. In addition, the proc plugin now also collects ZRAM device performance metrics and the apps plugin monitors process uptime for the defined process groups.
Continuing our efforts to integrate with as many existing solutions as possible, you can now directly archive metrics from Netdata to MongoDB via a new backend.
Netdata badges now support international (UTF8) characters! We also made our URL parser smarter, not only for international character support, but also for other strange API queries.
We also added .DEB packages to our binary distribution repositories at Packagecloud, a new collector for Linux zram device metrics, and support for plain text email notifications.
This release includes several fixes and improvements to the TLS encryption feature we introduced in v1.16.0. First, encryption slave-to-master streaming connections wasn't working as intended. And second, our community helped us discover cases where HTTP requests were not correctly redirected to HTTPS with TLS enabled. This release mitigates those issues and improves TLS support overall.
Finally, we improved the way Netdata displays charts with no metrics. By default, Netdata displays charts for disks, memory, and networks only when the associated metrics are not zero. Users could enable these charts permanently using the corresponding configuration options, but they would need to change more than 200 options. With this new improvement, users can enable all charts with zero values using a single, global configuration parameter.
Improvements
Check the release log at github.
If you are new to netdata, check a few live demos at its home page and the project home at github.
Netdata is FOSS (Free Open Source Software), released under GPLv3+.
Enjoy real-time performance and health monitoring!
submitted by ktsaou to devops [link] [comments]

C964 - Computer Science Capstone - Task 2, Part C

If you need a topic, look through Kaggle https://www.kaggle.com/ or Driven Data https://www.drivendata.org/competitions/ ... There are a lot of data competitions there and the datasets are often taken from elsewhere. I got my idea off Kaggle and cited the original data source which was in the UCI Machine Learning repository. From start to finish, I completed capstone in just under 2 months, except I had experience with data analytics so I didn't have to learn that from scratch (small favors, lol).
I recommend starting off with Task 2: Part C because if you end up not getting it to work or decide to change your topic, you'll have to redo Task 1. It took me 4 tries to settle into the topic I ended up with.
WARNING: Project requirements change and it can change A LOT which is why I don't normally go through each part like this for performance assessments. But because there is so little help out there for capstone I figure I'll chance it. Please let me know if something doesn't match your capstone so I can modify this (or at least take the conflicting info out).
one descriptive method and one non-descriptive (predictive or prescriptive) method
collected or available datasets
decision-support functionality
ability to support featurizing, parsing, cleaning, and wrangling datasets
methods and algorithms supporting data exploration and preparation
data visualization functionalities for data exploration and inspection
implementation of interactive queries
implementation of machine-learning methods and algorithms
functionalities to evaluate the accuracy of the data product
industry-appropriate security features
tools to monitor and maintain the product
a user-friendly, functional dashboard that includes at least three visualization types
I'll be writing these up in the order I did them (hopefully at least one a day).
Yes, I'm still on slack; check the subreddit sticky for other options. https://join.slack.com/t/wgu-itpros/signup
P.S. My model doesn't 'work' as a tool that should EVER be used in a medical setting ... It was trained on a dataset of roughly 600 patients who were surveyed in a single hospital in Venezuela. So consider the predictive result given by the prototype as arbitrary if you feel like entering your own information into the dataframe for fun. If you're keeping up with your regular checkups, you're fine!
https://www.reddit.com/WGU_CompSci/comments/d21igo/c964_computer_science_capstone_task_2_part_d/
https://www.reddit.com/WGU_CompSci/comments/d2k1lz/c964_computer_science_capstone_task_2_part_b_and_a/
submitted by lynda_ to WGU_CompSci [link] [comments]

netdata, the open-source real-time performance and health monitoring, released v1.17 !

Hi all,
Release v1.17.0 contains 38 bug fixes, 33 improvements, and 20 documentation updates.
At a glance
You can now change the data collection frequency at will, without losing previously collected values. A major improvement to the new database engine allows you not only to store metrics at variable granularity, but also to autoscale the time axis of the charts, depending on the data collection frequencies used during the presented time.
You can also now monitor VM performance from one or more vCenter servers with a new VSphere collector. In addition, the proc plugin now also collects ZRAM device performance metrics and the apps plugin monitors process uptime for the defined process groups.
Continuing our efforts to integrate with as many existing solutions as possible, you can now directly archive metrics from Netdata to MongoDB via a new backend.
Netdata badges now support international (UTF8) characters! We also made our URL parser smarter, not only for international character support, but also for other strange API queries.
We also added .DEB packages to our binary distribution repositories at Packagecloud, a new collector for Linux zram device metrics, and support for plain text email notifications.
This release includes several fixes and improvements to the TLS encryption feature we introduced in v1.16.0. First, encryption slave-to-master streaming connections wasn't working as intended. And second, our community helped us discover cases where HTTP requests were not correctly redirected to HTTPS with TLS enabled. This release mitigates those issues and improves TLS support overall.
Finally, we improved the way Netdata displays charts with no metrics. By default, Netdata displays charts for disks, memory, and networks only when the associated metrics are not zero. Users could enable these charts permanently using the corresponding configuration options, but they would need to change more than 200 options. With this new improvement, users can enable all charts with zero values using a single, global configuration parameter.
Improvements
Check the release log at github.
If you are new to netdata, check a few live demos at its home page and the project home at github.
Netdata is FOSS (Free Open Source Software), released under GPLv3+.
Enjoy real-time performance and health monitoring!
submitted by ktsaou to linux [link] [comments]

netdata, the open-source real-time performance and health monitoring, released v1.17 !

Hi all,
Release v1.17.0 contains 38 bug fixes, 33 improvements, and 20 documentation updates.
At a glance
You can now change the data collection frequency at will, without losing previously collected values. A major improvement to the new database engine allows you not only to store metrics at variable granularity, but also to autoscale the time axis of the charts, depending on the data collection frequencies used during the presented time.
You can also now monitor VM performance from one or more vCenter servers with a new VSphere collector. In addition, the proc plugin now also collects ZRAM device performance metrics and the apps plugin monitors process uptime for the defined process groups.
Continuing our efforts to integrate with as many existing solutions as possible, you can now directly archive metrics from Netdata to MongoDB via a new backend.
Netdata badges now support international (UTF8) characters! We also made our URL parser smarter, not only for international character support, but also for other strange API queries.
We also added .DEB packages to our binary distribution repositories at Packagecloud, a new collector for Linux zram device metrics, and support for plain text email notifications.
This release includes several fixes and improvements to the TLS encryption feature we introduced in v1.16.0. First, encryption slave-to-master streaming connections wasn't working as intended. And second, our community helped us discover cases where HTTP requests were not correctly redirected to HTTPS with TLS enabled. This release mitigates those issues and improves TLS support overall.
Finally, we improved the way Netdata displays charts with no metrics. By default, Netdata displays charts for disks, memory, and networks only when the associated metrics are not zero. Users could enable these charts permanently using the corresponding configuration options, but they would need to change more than 200 options. With this new improvement, users can enable all charts with zero values using a single, global configuration parameter.
Improvements
Check the release log at github.
If you are new to netdata, check a few live demos at its home page and the project home at github.
Netdata is FOSS (Free Open Source Software), released under GPLv3+.
Enjoy real-time performance and health monitoring!
submitted by ktsaou to linuxadmin [link] [comments]

netdata, the open-source, real-time, performance and health monitoring, released v1.17 !

Hi all,
Release v1.17.0 contains 38 bug fixes, 33 improvements, and 20 documentation updates.
At a glance
You can now change the data collection frequency at will, without losing previously collected values. A major improvement to the new database engine allows you not only to store metrics at variable granularity, but also to autoscale the time axis of the charts, depending on the data collection frequencies used during the presented time.
You can also now monitor VM performance from one or more vCenter servers with a new VSphere collector. In addition, the proc plugin now also collects ZRAM device performance metrics and the apps plugin monitors process uptime for the defined process groups.
Continuing our efforts to integrate with as many existing solutions as possible, you can now directly archive metrics from Netdata to MongoDB via a new backend.
Netdata badges now support international (UTF8) characters! We also made our URL parser smarter, not only for international character support, but also for other strange API queries.
We also added .DEB packages to our binary distribution repositories at Packagecloud, a new collector for Linux zram device metrics, and support for plain text email notifications.
This release includes several fixes and improvements to the TLS encryption feature we introduced in v1.16.0. First, encryption slave-to-master streaming connections wasn't working as intended. And second, our community helped us discover cases where HTTP requests were not correctly redirected to HTTPS with TLS enabled. This release mitigates those issues and improves TLS support overall.
Finally, we improved the way Netdata displays charts with no metrics. By default, Netdata displays charts for disks, memory, and networks only when the associated metrics are not zero. Users could enable these charts permanently using the corresponding configuration options, but they would need to change more than 200 options. With this new improvement, users can enable all charts with zero values using a single, global configuration parameter.
Improvements
Check the release log at github.
If you are new to netdata, check a few live demos at its home page and the project home at github.
Netdata is FOSS (Free Open Source Software), released under GPLv3+.
Enjoy real-time performance and health monitoring!
submitted by ktsaou to Ubuntu [link] [comments]

netdata, the open-source real-time performance and health monitoring, released v1.17 !

Hi all,
Release v1.17.0 contains 38 bug fixes, 33 improvements, and 20 documentation updates.
At a glance
You can now change the data collection frequency at will, without losing previously collected values. A major improvement to the new database engine allows you not only to store metrics at variable granularity, but also to autoscale the time axis of the charts, depending on the data collection frequencies used during the presented time.
You can also now monitor VM performance from one or more vCenter servers with a new VSphere collector. In addition, the proc plugin now also collects ZRAM device performance metrics and the apps plugin monitors process uptime for the defined process groups.
Continuing our efforts to integrate with as many existing solutions as possible, you can now directly archive metrics from Netdata to MongoDB via a new backend.
Netdata badges now support international (UTF8) characters! We also made our URL parser smarter, not only for international character support, but also for other strange API queries.
We also added .DEB packages to our binary distribution repositories at Packagecloud, a new collector for Linux zram device metrics, and support for plain text email notifications.
This release includes several fixes and improvements to the TLS encryption feature we introduced in v1.16.0. First, encryption slave-to-master streaming connections wasn't working as intended. And second, our community helped us discover cases where HTTP requests were not correctly redirected to HTTPS with TLS enabled. This release mitigates those issues and improves TLS support overall.
Finally, we improved the way Netdata displays charts with no metrics. By default, Netdata displays charts for disks, memory, and networks only when the associated metrics are not zero. Users could enable these charts permanently using the corresponding configuration options, but they would need to change more than 200 options. With this new improvement, users can enable all charts with zero values using a single, global configuration parameter.
Improvements
Check the release log at github.
If you are new to netdata, check a few live demos at its home page and the project home at github.
Netdata is FOSS (Free Open Source Software), released under GPLv3+.
Enjoy real-time performance and health monitoring!
submitted by ktsaou to docker [link] [comments]

netdata, the open-source real-time performance and health monitoring, released v1.17 !

Hi all,
Release v1.17.0 contains 38 bug fixes, 33 improvements, and 20 documentation updates.
At a glance
You can now change the data collection frequency at will, without losing previously collected values. A major improvement to the new database engine allows you not only to store metrics at variable granularity, but also to autoscale the time axis of the charts, depending on the data collection frequencies used during the presented time.
You can also now monitor VM performance from one or more vCenter servers with a new VSphere collector. In addition, the proc plugin now also collects ZRAM device performance metrics and the apps plugin monitors process uptime for the defined process groups.
Continuing our efforts to integrate with as many existing solutions as possible, you can now directly archive metrics from Netdata to MongoDB via a new backend.
Netdata badges now support international (UTF8) characters! We also made our URL parser smarter, not only for international character support, but also for other strange API queries.
We also added .DEB packages to our binary distribution repositories at Packagecloud, a new collector for Linux zram device metrics, and support for plain text email notifications.
This release includes several fixes and improvements to the TLS encryption feature we introduced in v1.16.0. First, encryption slave-to-master streaming connections wasn't working as intended. And second, our community helped us discover cases where HTTP requests were not correctly redirected to HTTPS with TLS enabled. This release mitigates those issues and improves TLS support overall.
Finally, we improved the way Netdata displays charts with no metrics. By default, Netdata displays charts for disks, memory, and networks only when the associated metrics are not zero. Users could enable these charts permanently using the corresponding configuration options, but they would need to change more than 200 options. With this new improvement, users can enable all charts with zero values using a single, global configuration parameter.
Improvements
Check the release log at github.
If you are new to netdata, check a few live demos at its home page and the project home at github.
Netdata is FOSS (Free Open Source Software), released under GPLv3+.
Enjoy real-time performance and health monitoring!
submitted by ktsaou to SysAdminBlogs [link] [comments]

MAME 0.203

MAME 0.203

With Hallowe’en basically over, the only thing you need to make October complete is MAME 0.203. Newly supported titles include not just one, but two Nintendo Game & Watch classics: Donkey Kong and Green House, and the HP 9825B desktop computer. We’ve added dozens of new versions of supported systems, including European bootlegs of Puck Man, Ms. Pac-Man, Phoenix, Pengo and Zero Time, more revisions of Street Fighter II and Super Street Fighter II, and a version of Soldier Girl Amazon made under license by Tecfri.
There are major improvements to plug-in TV games in this release, specifically systems based on the XaviX and SunPlus µ'nSP processors. The Vii is now playable with sound, and the V.Smile can boot games. Tiger Game.com emulation has come to the point where all but one of the games are playable. Some long-standing issues with Tandy CoCo cartridges have been fixed.
It isn’t just home systems that have received attention this month: Namco System 22 emulation has leapt forward. Yes, the hit box errors making it impossible to pass the helicopter (Time Crisis) and the tanks (Tokyo Wars) have finally been fixed. On top of that, video emulation improvements make just about everything on the system look better. In particular, rear view mirrors in the driving games now work properly. If that isn’t enough for you, the code has been optimised, so there’s a good chance you’ll get full speed emulation on a modern PC. There have been less dramatic improvements to video emulation in other Namco and Tecmo systems, and CPS-3 row scroll effects have been implemented.
MAME 0.203 should build out-of-the-box on macOS “Mojave” with the latest Xcode tools (provided your SDL2 framework is up-to-date), a number of lingering debugger issues have been fixed, and it’s now possible to run SDL MAME on a system with no display. MAME’s internal file selection menus should behave better when you type the name of a file to select it.
MAME 0.203 is a huge update, touching all kinds of areas. You can get the source and Windows binary packages from the download page.

MAMETesters Bugs Fixed

New working machines

New working clones

Machines promoted to working

New machines marked as NOT_WORKING

New clones marked as NOT_WORKING

New working software list additions

Software list items promoted to working

New NOT_WORKING software list additions

Source Changes

submitted by cuavas to emulation [link] [comments]

netdata, the open-source, real-life, performance and health monitoring, released v1.17 !

Hi all,
Release v1.17.0 contains 38 bug fixes, 33 improvements, and 20 documentation updates.
At a glance
You can now change the data collection frequency at will, without losing previously collected values. A major improvement to the new database engine allows you not only to store metrics at variable granularity, but also to autoscale the time axis of the charts, depending on the data collection frequencies used during the presented time.
You can also now monitor VM performance from one or more vCenter servers with a new VSphere collector. In addition, the proc plugin now also collects ZRAM device performance metrics and the apps plugin monitors process uptime for the defined process groups.
Continuing our efforts to integrate with as many existing solutions as possible, you can now directly archive metrics from Netdata to MongoDB via a new backend.
Netdata badges now support international (UTF8) characters! We also made our URL parser smarter, not only for international character support, but also for other strange API queries.
We also added .DEB packages to our binary distribution repositories at Packagecloud, a new collector for Linux zram device metrics, and support for plain text email notifications.
This release includes several fixes and improvements to the TLS encryption feature we introduced in v1.16.0. First, encryption slave-to-master streaming connections wasn't working as intended. And second, our community helped us discover cases where HTTP requests were not correctly redirected to HTTPS with TLS enabled. This release mitigates those issues and improves TLS support overall.
Finally, we improved the way Netdata displays charts with no metrics. By default, Netdata displays charts for disks, memory, and networks only when the associated metrics are not zero. Users could enable these charts permanently using the corresponding configuration options, but they would need to change more than 200 options. With this new improvement, users can enable all charts with zero values using a single, global configuration parameter.
Improvements
Check the release log at github.
If you are new to netdata, check a few live demos at its home page and the project home at github.
Netdata is FOSS (Free Open Source Software), released under GPLv3+.
Enjoy real-time performance and health monitoring!
submitted by ktsaou to linux_programming [link] [comments]

netdata, the open-source, real-time, performance and health monitoring, released v1.17 !

Hi all,
Release v1.17.0 contains 38 bug fixes, 33 improvements, and 20 documentation updates.
At a glance
You can now change the data collection frequency at will, without losing previously collected values. A major improvement to the new database engine allows you not only to store metrics at variable granularity, but also to autoscale the time axis of the charts, depending on the data collection frequencies used during the presented time.
You can also now monitor VM performance from one or more vCenter servers with a new VSphere collector. In addition, the proc plugin now also collects ZRAM device performance metrics and the apps plugin monitors process uptime for the defined process groups.
Continuing our efforts to integrate with as many existing solutions as possible, you can now directly archive metrics from Netdata to MongoDB via a new backend.
Netdata badges now support international (UTF8) characters! We also made our URL parser smarter, not only for international character support, but also for other strange API queries.
We also added .DEB packages to our binary distribution repositories at Packagecloud, a new collector for Linux zram device metrics, and support for plain text email notifications.
This release includes several fixes and improvements to the TLS encryption feature we introduced in v1.16.0. First, encryption slave-to-master streaming connections wasn't working as intended. And second, our community helped us discover cases where HTTP requests were not correctly redirected to HTTPS with TLS enabled. This release mitigates those issues and improves TLS support overall.
Finally, we improved the way Netdata displays charts with no metrics. By default, Netdata displays charts for disks, memory, and networks only when the associated metrics are not zero. Users could enable these charts permanently using the corresponding configuration options, but they would need to change more than 200 options. With this new improvement, users can enable all charts with zero values using a single, global configuration parameter.
Improvements
Check the release log at github.
If you are new to netdata, check a few live demos at its home page and the project home at github.
Netdata is FOSS (Free Open Source Software), released under GPLv3+.
Enjoy real-time performance and health monitoring!
submitted by ktsaou to linux_mentor [link] [comments]

netdata, the open-source, real-time, performance and health monitoring, released v1.17 !

Hi all,
Release v1.17.0 contains 38 bug fixes, 33 improvements, and 20 documentation updates.
At a glance
You can now change the data collection frequency at will, without losing previously collected values. A major improvement to the new database engine allows you not only to store metrics at variable granularity, but also to autoscale the time axis of the charts, depending on the data collection frequencies used during the presented time.
You can also now monitor VM performance from one or more vCenter servers with a new VSphere collector. In addition, the proc plugin now also collects ZRAM device performance metrics and the apps plugin monitors process uptime for the defined process groups.
Continuing our efforts to integrate with as many existing solutions as possible, you can now directly archive metrics from Netdata to MongoDB via a new backend.
Netdata badges now support international (UTF8) characters! We also made our URL parser smarter, not only for international character support, but also for other strange API queries.
We also added .DEB packages to our binary distribution repositories at Packagecloud, a new collector for Linux zram device metrics, and support for plain text email notifications.
This release includes several fixes and improvements to the TLS encryption feature we introduced in v1.16.0. First, encryption slave-to-master streaming connections wasn't working as intended. And second, our community helped us discover cases where HTTP requests were not correctly redirected to HTTPS with TLS enabled. This release mitigates those issues and improves TLS support overall.
Finally, we improved the way Netdata displays charts with no metrics. By default, Netdata displays charts for disks, memory, and networks only when the associated metrics are not zero. Users could enable these charts permanently using the corresponding configuration options, but they would need to change more than 200 options. With this new improvement, users can enable all charts with zero values using a single, global configuration parameter.
Improvements
Check the release log at github.
If you are new to netdata, check a few live demos at its home page and the project home at github.
Netdata is FOSS (Free Open Source Software), released under GPLv3+.
Enjoy real-time performance and health monitoring!
submitted by ktsaou to netdata [link] [comments]

netdata, the open-source real-time performance and health monitoring, released v1.17 !

Hi all,
Release v1.17.0 contains 38 bug fixes, 33 improvements, and 20 documentation updates.
At a glance
You can now change the data collection frequency at will, without losing previously collected values. A major improvement to the new database engine allows you not only to store metrics at variable granularity, but also to autoscale the time axis of the charts, depending on the data collection frequencies used during the presented time.
You can also now monitor VM performance from one or more vCenter servers with a new VSphere collector. In addition, the proc plugin now also collects ZRAM device performance metrics and the apps plugin monitors process uptime for the defined process groups.
Continuing our efforts to integrate with as many existing solutions as possible, you can now directly archive metrics from Netdata to MongoDB via a new backend.
Netdata badges now support international (UTF8) characters! We also made our URL parser smarter, not only for international character support, but also for other strange API queries.
We also added .DEB packages to our binary distribution repositories at Packagecloud, a new collector for Linux zram device metrics, and support for plain text email notifications.
This release includes several fixes and improvements to the TLS encryption feature we introduced in v1.16.0. First, encryption slave-to-master streaming connections wasn't working as intended. And second, our community helped us discover cases where HTTP requests were not correctly redirected to HTTPS with TLS enabled. This release mitigates those issues and improves TLS support overall.
Finally, we improved the way Netdata displays charts with no metrics. By default, Netdata displays charts for disks, memory, and networks only when the associated metrics are not zero. Users could enable these charts permanently using the corresponding configuration options, but they would need to change more than 200 options. With this new improvement, users can enable all charts with zero values using a single, global configuration parameter.
Improvements
Check the release log at github.
If you are new to netdata, check a few live demos at its home page and the project home at github.
Netdata is FOSS (Free Open Source Software), released under GPLv3+.
Enjoy real-time performance and health monitoring!
submitted by ktsaou to linux4noobs [link] [comments]

netdata, the open-source, real-time, performance and health monitoring, released v1.17 !

Hi all,
Release v1.17.0 contains 38 bug fixes, 33 improvements, and 20 documentation updates.
At a glance
You can now change the data collection frequency at will, without losing previously collected values. A major improvement to the new database engine allows you not only to store metrics at variable granularity, but also to autoscale the time axis of the charts, depending on the data collection frequencies used during the presented time.
You can also now monitor VM performance from one or more vCenter servers with a new VSphere collector. In addition, the proc plugin now also collects ZRAM device performance metrics and the apps plugin monitors process uptime for the defined process groups.
Continuing our efforts to integrate with as many existing solutions as possible, you can now directly archive metrics from Netdata to MongoDB via a new backend.
Netdata badges now support international (UTF8) characters! We also made our URL parser smarter, not only for international character support, but also for other strange API queries.
We also added .DEB packages to our binary distribution repositories at Packagecloud, a new collector for Linux zram device metrics, and support for plain text email notifications.
This release includes several fixes and improvements to the TLS encryption feature we introduced in v1.16.0. First, encryption slave-to-master streaming connections wasn't working as intended. And second, our community helped us discover cases where HTTP requests were not correctly redirected to HTTPS with TLS enabled. This release mitigates those issues and improves TLS support overall.
Finally, we improved the way Netdata displays charts with no metrics. By default, Netdata displays charts for disks, memory, and networks only when the associated metrics are not zero. Users could enable these charts permanently using the corresponding configuration options, but they would need to change more than 200 options. With this new improvement, users can enable all charts with zero values using a single, global configuration parameter.
Improvements
Check the release log at github.
If you are new to netdata, check a few live demos at its home page and the project home at github.
Netdata is FOSS (Free Open Source Software), released under GPLv3+.
Enjoy real-time performance and health monitoring!
submitted by ktsaou to systemd [link] [comments]

Decred Journal — June 2018

Note: You can read this on GitHub, Medium or old Reddit to see the 207 links.

Development

The biggest announcement of the month was the new kind of decentralized exchange proposed by @jy-p of Company 0. The Community Discussions section considers the stakeholders' response.
dcrd: Peer management and connectivity improvements. Some work for improved sighash algo. A new optimization that gives 3-4x faster serving of headers, which is great for SPV. This was another step towards multipeer parallel downloads – check this issue for a clear overview of progress and planned work for next months (and some engineering delight). As usual, codebase cleanup, improvements to error handling, test infrastructure and test coverage.
Decrediton: work towards watching only wallets, lots of bugfixes and visual design improvements. Preliminary work to integrate SPV has begun.
Politeia is live on testnet! Useful links: announcement, introduction, command line voting example, example proposal with some votes, mini-guide how to compose a proposal.
Trezor: Decred appeared in the firmware update and on Trezor website, currently for testnet only. Next steps are mainnet support and integration in wallets. For the progress of Decrediton support you can track this meta issue.
dcrdata: Continued work on Insight API support, see this meta issue for progress overview. It is important for integrations due to its popularity. Ongoing work to add charts. A big database change to improve sorting on the Address page was merged and bumped version to 3.0. Work to visualize agenda voting continues.
Ticket splitting: 11-way ticket split from last month has voted (transaction).
Ethereum support in atomicswap is progressing and welcomes more eyeballs.
decred.org: revamped Press page with dozens of added articles, and a shiny new Roadmap page.
decredinfo.com: a new Decred dashboard by lte13. Reddit announcement here.
Dev activity stats for June: 245 active PRs, 184 master commits, 25,973 added and 13,575 deleted lines spread across 8 repositories. Contributions came from 2 to 10 developers per repository. (chart)

Network

Hashrate: growth continues, the month started at 15 and ended at 44 PH/s with some wild 30% swings on the way. The peak was 53.9 PH/s.
F2Pool was the leader varying between 36% and 59% hashrate, followed by coinmine.pl holding between 18% and 29%. In response to concerns about its hashrate share, F2Pool made a statement that they will consider measures like rising the fees to prevent growing to 51%.
Staking: 30-day average ticket price is 94.7 DCR (+3.4). The price was steadily rising from 90.7 to 95.8 peaking at 98.1. Locked DCR grew from 3.68 to 3.81 million DCR, the highest value was 3.83 million corresponding to 47.87% of supply (+0.7% from previous peak).
Nodes: there are 240 public listening and 115 normal nodes per dcred.eu. Version distribution: 57% on v1.2.0 (+12%), 25% on v1.1.2 (-13%), 14% on v1.1.0 (-1%). Note: the reported count of non-listening nodes has dropped significantly due to data reset at decred.eu. It will take some time before the crawler collects more data. On top of that, there is no way to exactly count non-listening nodes. To illustrate, an alternative data source, charts.dcr.farm showed 690 reachable nodes on Jul 1.
Extraordinary event: 247361 and 247362 were two nearly full blocks. Normally blocks are 10-20 KiB, but these blocks were 374 KiB (max is 384 KiB).

ASICs

Update from Obelisk: shipping is expected in first half of July and there is non-zero chance to meet hashrate target.
Another Chinese ASIC spotted on the web: Flying Fish D18 with 340 GH/s at 180 W costing 2,200 CNY (~340 USD). (asicok.comtranslated, also on asicminervalue)
dcrASIC team posted a farewell letter. Despite having an awesome 16 nm chip design, they decided to stop the project citing the saturated mining ecosystem and low profitability for their potential customers.

Integrations

bepool.org is a new mining pool spotted on dcred.eu.
Exchange integrations:
Two OTC trading desks are now shown on decred.org exchanges page.
BitPro payment gateway added Decred and posted on Reddit. Notably, it is fully functional without javascript or cookies and does not ask for name or email, among other features.
Guarda Wallet integrated Decred. Currently only in their web wallet, but more may come in future. Notable feature is "DCR purchase with a bank card". See more details in their post or ask their representative on Reddit. Important: do your best to understand the security model before using any wallet software.

Adoption

Merchants:
BlueYard Capital announced investment in Decred and the intent to be long term supporters and to actively participate in the network's governance. In an overview post they stressed core values of the project:
There are a few other remarkable characteristics that are a testament to the DNA of the team behind Decred: there was no sale of DCR to investors, no venture funding, and no payment to exchanges to be listed – underscoring that the Decred team and contributors are all about doing the right thing for long term (as manifested in their constitution for the project).
The most encouraging thing we can see is both the quality and quantity of high calibre developers flocking to the project, in addition to a vibrant community attaching their identity to the project.
The company will be hosting an event in Berlin, see Events below.
Arbitrade is now mining Decred.

Events

Attended:
Upcoming:

Media

stakey.club: a new website by @mm:
Hey guys! I'd like to share with you my latest adventure: Stakey Club, hosted at stakey.club, is a website dedicated to Decred. I posted a few articles in Brazilian Portuguese and in English. I also translated to Portuguese some posts from the Decred Blog. I hope you like it! (slack)
@morphymore translated Placeholder's Decred Investment Thesis and Richard Red's write-up on Politeia to Chinese, while @DZ translated Decred Roadmap 2018 to Italian and Russian, and A New Kind of DEX to Italian and Russian.
Second iteration of Chinese ratings released. Compared to the first issue, Decred dropped from 26 to 29 while Bitcoin fell from 13 to 17. We (the authors) restrain ourselves commenting on this one.
Videos:
Audio:
Featured articles:
Articles:

Community Discussions

Community stats: Twitter followers 40,209 (+1,091), Reddit subscribers 8,410 (+243), Slack users 5,830 (+172), GitHub 392 stars and 918 forks of dcrd repository.
An update on our communication systems:
Jake Yocom-Piatt did an AMA on CryptoTechnology, a forum for serious crypto tech discussion. Some topics covered were Decred attack cost and resistance, voting policies, smart contracts, SPV security, DAO and DPoS.
A new kind of DEX was the subject of an extensive discussion in #general, #random, #trading channels as well as Reddit. New channel #thedex was created and attracted more than 100 people.
A frequent and fair question is how the DEX would benefit Decred. @lukebp has put it well:
Projects like these help Decred attract talent. Typically, the people that are the best at what they do aren’t driven solely by money. They want to work on interesting projects that they believe in with other talented individuals. Launching a DEX that has no trading fees, no requirement to buy a 3rd party token (including Decred), and that cuts out all middlemen is a clear demonstration of the ethos that Decred was founded on. It helps us get our name out there and attract the type of people that believe in the same mission that we do. (slack)
Another concern that it will slow down other projects was addressed by @davecgh:
The intent is for an external team to take up the mantle and build it, so it won't have any bearing on the current c0 roadmap. The important thing to keep in mind is that the goal of Decred is to have a bunch of independent teams on working on different things. (slack)
A chat about Decred fork resistance started on Twitter and continued in #trading. Community members continue to discuss the finer points of Decred's hybrid system, bringing new users up to speed and answering their questions. The key takeaway from this chat is that the Decred chain is impossible to advance without votes, and to get around that the forker needs to change the protocol in a way that would make it clearly not Decred.
"Against community governance" article was discussed on Reddit and #governance.
"The Downside of Democracy (and What it Means for Blockchain Governance)" was another article arguing against on-chain governance, discussed here.
Reddit recap: mining rig shops discussion; how centralized is Politeia; controversial debate on photos of models that yielded useful discussion on our marketing approach; analysis of a drop in number of transactions; concerns regarding project bus factor, removing central authorities, advertising and full node count – received detailed responses; an argument by insette for maximizing aggregate tx fees; coordinating network upgrades; a new "Why Decred?" thread; a question about quantum resistance with a detailed answer and a recap of current status of quantum resistant algorithms.
Chats recap: Programmatic Proof-of-Work (ProgPoW) discussion; possible hashrate of Blake-256 miners is at least ~30% higher than SHA-256d; how Decred is not vulnerable to SPV leaf/node attack.

Markets

DCR opened the month at ~$93, reached monthly high of $110, gradually dropped to the low of $58 and closed at $67. In BTC terms it was 0.0125 -> 0.0150 -> 0.0098 -> 0.0105. The downturn coincided with a global decline across the whole crypto market.
In the middle of the month Decred was noticed to be #1 in onchainfx "% down from ATH" chart and on this chart by @CoinzTrader. Towards the end of the month it dropped to #3.

Relevant External

Obelisk announced Launchpad service. The idea is to work with coin developers to design a custom, ASIC-friendly PoW algorithm together with a first batch of ASICs and distribute them among the community.
Equihash-based ZenCash was hit by a double spend attack that led to a loss of $450,000 by the exchange which was targeted.
Almost one year after collecting funds, Tezos announced a surprise identification procedure to claim tokens (non-javascript version).
A hacker broke into Syscoin's GitHub account and implanted malware stealing passwords and private keys into Windows binaries. This is a painful reminder for everybody to verify binaries after download.
Circle announced new asset listing framework for Poloniex. Relevant to recent discussions of exchange listing bribery:
Please note: we will not accept any kind of payment to list an asset.
Bithumb got hacked with a $30 m loss.
Zcash organized Zcon0, an event in Canada that focused on privacy tech and governance. An interesting insight from Keynote Panel on governance: "There is no such thing as on-chain governance".
Microsoft acquired GitHub. There was some debate about whether it is a reason to look into alternative solutions like GitLab right now. It is always a good idea to have a local copy of Decred source code, just in case.
Status update from @sumiflow on correcting DCR supply on various sites:
To begin with, none of the below sites were showing the correct supply or market cap for Decred but we've made some progress. coingecko.com, coinlib.io, cryptocompare.com, livecoinwatch.com, worldcoinindex.com - corrected! cryptoindex.co, onchainfx.com - awaiting fix coinmarketcap.com - refused to fix because devs have coins too? (slack)

About This Issue

This is the third issue of Decred Journal after April and May.
Most information from third parties is relayed directly from source after a minimal sanity check. The authors of Decred Journal have no ability to verify all claims. Please beware of scams and do your own research.
The new public Matrix logs look promising and we hope to transition from Slack links to Matrix links. In the meantime, the way to read Slack links is explained in the previous issue.
As usual, any feedback is appreciated: please comment on Reddit, GitHub or #writers_room. Contributions are welcome too, anything from initial collection to final review to translations.
Credits (Slack names, alphabetical order): bee and Richard-Red. Special thanks to @Haon for bringing May 2018 issue to medium.
submitted by jet_user to decred [link] [comments]

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