Approach messages from strangers with caution.

Virginia-based security firm Kryptowire recently reported that there are few Android devices from a Chinese company that are infected with software that collects users’ personal information and conversation made through text messages and calls.

Test your knowledge of science fiction, fantasy, horror and steampunk films in this random quiz that changes every time you do it.

While I like the Harry Potter series of books, I haven't liked the movies. Most of the characters irritate, and...

Well a couple weeks ago we finally got around to launching release #68. Ours is the Wednesday 4:10pm class, an awesome timeslot for everyone who just wants to knock off work a bit early and take a midweek break for an hour. It's our favorite class to teach.
I'm a full-time software developer who's always running five minutes late, so finding the time to pick up choreography can be a challenge sometimes. It's definitely not unusual for me to resort to "cramming" just before class - Tami, on the other hand, is never late and learns her music days ahead of time. Good thing for me that opposites attract!
Our class tells us they enjoy the banter that goes on between Tami and I during the workout. Well there's nothing scripted or made up for that - it's just a continuation of what goes on between us pretty much most of the time. It just so happens for an hour that day everyone gets to overhear it. I'm behaving badly in this pic to the right, with Tami on the verge of thumping my head lest I forget who really wears the pants in our family.
Somehow, in spite of ourselves, we usually manage to run a pretty tight class. Form - not weight - is our hot topic, and we've found that most everybody really wants to execute the routines properly. Last week we ran another of our Form Friday classes - the cool thing this last time around was that we were mostly helping people make "fine tuning" adjustments (all the typical "big stuff" seemed to be in pretty good shape!) Which brings me to the closing thought for this post - maybe it's time to resurrect our Wicked Wednesday mix for this afternoon's class...

Aiming at the problems of high error rate, low evaluation accuracy and low investment return in traditional methods, a random forest-based e-bank investment risk evaluation method is proposed. First, establish a scientific e-bank investment risk evaluation index system. Then, G1-COWA combined weighting method is used to calculate the weights of each index. Finally, the e-bank investment risk evaluation index data is taken as the input vector, and the e-bank investment risk evaluation result is taken as the output vector. The random forest model is established and the result of e-banking investment risk evaluation is obtained. The experimental results show that the maximum relative error rate of this method is 4.32%, the evaluation accuracy range is 94.5~98.1%, and the maximum return rate of e-banking investment is 8.32%. It shows that this method can accurately evaluate the investment risk of electronic banking.

Action recognition plays a vital role in analysing human body behaviour and has significant implications for research and education. However, traditional recognition methods often suffer from issues such as inaccurate time and spatial feature vectors. Therefore, this study addresses the problem of inaccurate recognition of aerobic gymnastics action image data and proposes a visualised three-dimensional convolutional neural network algorithm-based action recognition model. This model incorporates unsupervised visualisation methods into the traditional network and enhances data recognition capabilities through the introduction of a random noise perturbation enhancement algorithm. The research results indicate that the data augmented with noise perturbation achieves the lowest mean square error, reducing the error value from 0.3352 to 0.3095. The use of unsupervised visualisation analysis enables clearer recognition of human actions, and the algorithm model is capable of accurately recognising aerobic movements. Compared to traditional algorithms, the new algorithm exhibits higher recognition accuracy and superior performance.

In order to improve the identification accuracy, accuracy and time-consuming of traditional financial risk identification methods, this paper proposes a risk identification method for financial abnormal data based on weighted random forest. Firstly, SMOTE algorithm is used to collect abnormal financial data; secondly, the original accounting data is decomposed into features, and the features of abnormal data are extracted through random forests; then, the index weight is calculated according to the entropy weight method; finally, the negative gradient fitting is used to determine the loss function, and the weighted random forest method is used to solve the loss function value, and the recognition result is obtained. The results show that the identification accuracy of this method can reach 99.9%, the accuracy rate can reach 96.06%, and the time consumption is only 6.8 seconds, indicating that the risk identification effect of this method is good.

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Bro Robin Kachfi filmed a bunch of clips during the Simple Session 2019 that he cut into a cool edit. Watch the whole video right here!

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Video: Robin Kachfi



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Today's random set is 4728 Escape from Privet Drive, released during 2002. It's one of 17 Harry Potter sets produced that year. It contains 278 pieces and 3 minifigs, and its retail price was US$30/£24.99.

It's owned by 4,361 Brickset members. If you want to add it to your collection you should find it for sale at BrickLink, where new ones sell for around $154.00, or eBay.

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