After a recommendation from the Gamefromscratch discord server, today we take a look at acdsee Video Studio 3 that is available for free until July 29th, if registered with a valid email address.  Acdsee Video Studio enables you to capture, edit and produce video in an easy to use way.

Acdsee Video Studio is described as:

With a simple, easy-to-master interface, powerful 64-bit performance, and high res results, ACDSee Video Studio 3 provides value-based video editing without the learning curve. Now featuring higher quality screen recording, support for still images, 3x faster recording save times, 4K rendering, a variety of creative filters, audio effects, flexible tracks that you can layer and blend, and much more, ACDSee Video Studio 3 is versatile content creation in one lean package.

Engaging your audience, students, employees, and customers has never been this painless. ACDSee Video Studio allows for the quick creation of accessible media content and takes the mystery out of distribution with easy sharing solutions.

It’s amazing to see just how much of a copy this program is to Camtasia Studio, as you can see in the video below.

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GameDev News Applications

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Introduction

ML-Pack is a small footprint C++ machine learning library that can be easily integrated into other programs. It is an actively developed open source project and released under a BSD-3 license. Machine learning has gained popularity due to the large amount of electronic data that can be collected. Some other popular machine learning frameworks include TensorFlow, MxNet, PyTorch, Chainer and Paddle Paddle, however these are designed for more complex workflows than ML-Pack. On Fedora, ML-Pack is packaged by its lead developer Ryan Curtin. In addition to a command line interface, ML-Pack has bindings for Python and Julia. Here, we will focus on the command line interface since this may be useful for system administrators to integrate into their workflows.

Installation

You can install ML-Pack on the Fedora command line using

$ sudo dnf -y install mlpack mlpack-bin

You can also install the documentation, development headers and Python bindings by using …

$ sudo dnf -y install mlpack-doc \

mlpack-devel mlpack-python3

though they will not be used in this introduction.

Example

As an example, we will train a machine learning model to classify spam SMS messages. To keep this article brief, linux commands will not be fully explained, but you can find out more about them by using the man command, for example for the command first command used below, wget

$ man wget

will give you information that wget will download files from the web and options you can use for it.

Get a dataset

We will use an example spam dataset in Indonesian provided by Yudi Wibisono

 
$ wget https://drive.google.com/file/d/1-stKadf...Fxm_Q/view

$ unzip dataset_sms_spam_bhs_indonesia_v1.zip
 

Pre-process dataset

We will try to classify a message as spam or ham by the number of occurrences of a word in a message. We first change the file line endings, remove line 243 which is missing a label and then remove the header from the dataset. Then, we split our data into two files, labels and messages. Since the labels are at the end of the message, the message is reversed and then the label removed and placed in one file. The message is then removed and placed in another file.

$ tr 'r' 'n' < dataset_sms_spam_v1.csv > dataset.txt

$ sed '243d' dataset.txt > dataset1.csv

$ sed '1d' dataset1.csv > dataset.csv

$ rev dataset.csv | cut -c1 | rev > labels.txt

$ rev dataset.csv | cut -c2- | rev > messages.txt

$ rm dataset.csv

$ rm dataset1.csv

$ rm dataset.txt

Machine learning works on numeric data, so we will use labels of 1 for ham and 0 for spam. The dataset contains three labels, 0, normal sms (ham), 1, fraud (spam), and 2 promotion (spam). We will label all spam as 1, so promotions and fraud will be labelled as 1.

$ tr '2' '1' < labels.txt > labels.csv

$ rm labels.txt

The next step is to convert all text in the messages to lower case and for simplicity remove punctuation and any symbols that are not spaces, line endings or in the range a-z (one would need expand this range of symbols for production use)

$ tr '[:upper:]' '[:lower:]' < \

messages.txt > messagesLower.txt

$ tr -Cd 'abcdefghijklmnopqrstuvwxyz n' < \ messagesLower.txt > messagesLetters.txt

$ rm messagesLower.txt

We now obtain a sorted list of unique words used (this step may take a few minutes, so use nice to give it a low priority while you continue with other tasks on your computer).

$ nice -20 xargs -n1 < messagesLetters.txt > temp.txt

$ sort temp.txt > temp2.txt

$ uniq temp2.txt > words.txt

$ rm temp.txt

$ rm temp2.txt

We then create a matrix, where for each message, the frequency of word occurrences is counted (more on this on Wikipedia, here and here). This requires a few lines of code, so the full script, which should be saved as ‘makematrix.sh’ is below

#!/bin/bash

declare -a words=()

declare -a letterstartind=()

declare -a letterstart=()

letter=" "

i=0

lettercount=0

while IFS= read -r line; do labels[$((i))]=$line let "i++"

done < labels.csv

i=0

while IFS= read -r line; do words[$((i))]=$line firstletter="$( echo $line | head -c 1 )" if [ "$firstletter" != "$letter" ] then letterstartind[$((lettercount))]=$((i)) letterstart[$((lettercount))]=$firstletter letter=$firstletter let "lettercount++" fi let "i++"

done < words.txt

letterstartind[$((lettercount))]=$((i))

echo "Created list of letters" touch wordfrequency.txt

rm wordfrequency.txt

touch wordfrequency.txt

messagecount=0

messagenum=0

messages="$( wc -l messages.txt )"

i=0

while IFS= read -r line; do let "messagenum++" declare -a wordcount=() declare -a wordarray=() read -r -a wordarray <<> wordfrequency.txt echo "Processed message ""$messagenum" let "i++"

done < messagesLetters.txt

# Create csv file

tr ' ' ',' data.csv

Since Bash is an interpreted language, this simple implementation can take upto 30 minutes to complete. If using the above Bash script on your primary workstation, run it as a task with low priority so that you can continue with other work while you wait:

$ nice -20 bash makematrix.sh

Once the script has finished running, split the data into testing (30%) and training (70%) sets:

$ mlpack_preprocess_split \ --input_file data.csv \ --input_labels_file labels.csv \ --training_file train.data.csv \ --training_labels_file train.labels.csv \ --test_file test.data.csv \ --test_labels_file test.labels.csv \ --test_ratio 0.3 \ --verbose

Train a model

Now train a Logistic regression model:

$ mlpack_logistic_regression \

--training_file train.data.csv \

--labels_file train.labels.csv --lambda 0.1 \

--output_model_file lr_model.bin

Test the model

Finally we test our model by producing predictions,

$ mlpack_logistic_regression \

--input_model_file lr_model.bin \ --test_file test.data.csv \

--output_file lr_predictions.csv

and comparing the predictions with the exact results,

$ export incorrect=$(diff -U 0 lr_predictions.csv \

test.labels.csv | grep '^@@' | wc -l)

$ export tests=$(wc -l < lr_predictions.csv)

$ echo "scale=2; 100 * ( 1 - $((incorrect)) \

/ $((tests)))" | bc

This gives approximately 90% validation rate, similar to that obtained here.

The dataset is composed of approximately 50% spam messages, so the validation rates are quite good without doing much parameter tuning. In typical cases, datasets are unbalanced with many more entries in some categories than in others. In these cases a good validation rate can be obtained by mispredicting the class with a few entries. Thus to better evaluate these models, one can compare the number of misclassifications of spam, and the number of misclassifications of ham. Of particular importance in applications is the number of false positive spam results as these are typically not transmitted. The script below produces a confusion matrix which gives a better indication of misclassification. Save it as ‘confusion.sh’

#!/bin/bash

declare -a labels

declare -a lr

i=0

while IFS= read -r line; do labels[i]=$line let "i++"

done < test.labels.csv

i=0

while IFS= read -r line; do lr[i]=$line let "i++"

done < lr_predictions.csv

TruePositiveLR=0

FalsePositiveLR=0

TrueZerpLR=0

FalseZeroLR=0

Positive=0

Zero=0

for i in "${!labels[@]}"; do if [ "${labels[$i]}" == "1" ] then let "Positive++" if [ "${lr[$i]}" == "1" ] then let "TruePositiveLR++" else let "FalseZeroLR++" fi fi if [ "${labels[$i]}" == "0" ] then let "Zero++" if [ "${lr[$i]}" == "0" ] then let "TrueZeroLR++" else let "FalsePositiveLR++" fi fi done

echo "Logistic Regression"

echo "Total spam" $Positive

echo "Total ham" $Zero

echo "Confusion matrix"

echo " Predicted class"

echo " Ham | Spam "

echo " ---------------"

echo " Actual| Ham | " $TrueZeroLR "|" $FalseZeroLR

echo " class | Spam | " $FalsePositiveLR " |" $TruePositiveLR

echo ""

then run the script

$ bash confusion.sh

You should get output similar to

Logistic Regression
Total spam 183
Total ham 159
Confusion matrix

which indicates a reasonable level of classification. Other methods you can try in ML-Pack for this problem include Naive Bayes, random forest, decision tree, AdaBoost and perceptron.

To improve the error rating, you can try other pre-processing methods on the initial data set. Neural networks can give upto 99.95% validation rates, see for example here, here and here. However, using these techniques with ML-Pack cannot be done on the command line interface at present and is best covered in another post.

For more on ML-Pack, please see the documentation.

Koei Tecmo Europe and developer Gust Studios have announced a sequel to Atelier Ryza: Ever Darkness & The Secret Hideout coming to Switch (and PS4 and Windows PC) “this Winter”.

Described as a direct sequel, Atelier Ryza 2: Lost Legends & the Secret Fairy sees Ryza return as the protagonist, the first character in the history of the long-running RPG Atelier franchise to take on the hero role in two successive titles, according to the official PR for the game. Check out the Japanese trailer above for a look at the battle system, a handful of environments and other details.

While the game didn’t feature in the Western showcases, it was one of a number of games highlighted in the Japanese broadcast, which clocked in at around four minutes longer than the Western equivalent. A teaser site for the game went live during the broadcast is set to reveal more details on the 29th July 2020.

Here are a few screenshots we grabbed from the presentation:

How many of the Atelier games have you got under your belt? Looking forward to this one? Let us know below.

Team17 is hosting a new sale on Nintendo Switch, slashing prices of some of its biggest games by as much as 90%.

That tasty sounding 90% discount is available for Mugsters, and you’ll find other great titles like the Yooka-Laylee games, Moving Out, Overcooked, My Time at Portia and more on sale, too. We have a full list of the discounts available in North America for you below (similar discounts can also be found across Europe):

The sale will remain live until on 26th July – make sure to snap up any deals that take your fancy over the next few days.

Anything in particular catching your eye? Let us know which deals you’re thinking of going for in the comments below.

Nintendo Life

One fish we know you’re probably sick of hooking by now in Animal Crossing: New Horizons is the sea bass.

According to a story over on Polygon, it has got to the point where a modder by the name of ‘SmuggestGirl’ has decided to eradicate this sea roach from the game. Their mod, simply titled ‘No Sea Bass’, reduces the spawn rate of this particular fish to zero percent. Here’s some user feedback so far:

“Shucks, now [it’s] all squids, olive flounders, and red snappers”

The same modder has also created the “complete opposite” of this – removing all other varieties of fish from the water but the sea bass. It left one commenter questioning what would happen if they ran both mods at the same time.

While the mod removing this fish from the game is definitely amusing, alternatively you could just learn to live with it occupying the surrounding waters of your tropical paradise. It still sells for 400 Bells!

Nintendo Life

Do you think the amount of sea bass swimming about in New Horizons needs to be reduced? Is there anyone out there who wants to catch more sea bass? Do you want them gone for good? Share your thoughts below.

Last month the Philippines arm of KFC grabbed the headlines (well, our headlines at least) when it created its own KFC restaurant in Animal Crossing: New Horizons and invited its Twitter followers to visit its island, with the hope of finding Colonel Sanders and getting a voucher code for free chicken in real life.

While Nintendo obviously had nothing to do with this and the whole thing was just a marketing ploy conjured up by KFC, it’s far from the first time video games have been associated with food brands. In fact, ever since the very early days of home console gaming – we’re talking the Atari 2600 here – there have been games containing product placement for snacks. Some of these are subtle, others have entire games built around them. Here are some of our favourites.

This list is by no means complete: there are plenty of other examples. Honourable mentions go to Death Stranding and its infamous Monster Energy consumables, Pepsiman on the PlayStation, Fight Night Round 3 (which lets you unlock the terrifying Burger King mascot to accompany you to the ring) and the strange Space Invader-style Commodore 64 game Weetabix Versus the Titchies. Two games we haven’t forgotten, though, are the Chester Cheetah games for the SNES and Mega Drive, partly because even though they star the Cheetos mascot, they don’t actually mention Cheetos at any point.

That said: sit back, grab an ice cold refreshing Irn Bru™, and enjoy.

Have these blatant snack sponsorships left you feeling a little peckish? Let us know which other brand deals you’ve noticed in games with a comment below…