Even though the computerised assessment of developmental dysgraphia (DD) based on online handwriting processing has increasing popularity, most of the solutions are based on a setup, where a child writes on a paper fixed to a digitizing tablet that is connected to a computer. Although this approach enables the standard way of writing using an inking pen, it is difficult to be administered by children themselves. The main goal of this study is thus to explore, whether the quantitative analysis of online handwriting recorded via a display/screen tablet could sufficiently support the assessment of DD as well. For the purpose of this study, we enrolled 144 children (attending the 3rd and 4th class of a primary school), whose handwriting proficiency was assessed by a special education counsellor, and who assessed themselves by the Handwriting Proficiency Screening Questionnaires for Children (HPSQ-C). Using machine learning models based on a gradient-boosting algorithm, we were able to support the DD diagnosis with up to 83.6 % accuracy. The HPSQ-C total score was estimated with a minimum error equal to 10.34 %. Children with DD spent significantly higher time in-air, they had a higher number of pen elevations, a bigger height of on-surface strokes, a lower in-air tempo, and a higher variation in the angular velocity. Although this study shows a promising impact of DD assessment via display tablets, it also accents the fact that modelling of subjective scores is challenging and a complex and data-driven quantification of DD manifestations is needed. This study was supported by a project of the Technology Agency of the Czech Republic no.

Bit-stream recognition (BSR) has many applications, such as forensic investigations, detection of copyright infringement, and malware analysis. We propose the first BSR that takes a bare input bit-stream and outputs a class label without any preprocessing. To achieve our goal, we propose a centrifuge mechanism, where the upstream layers (sub-net) capture global features and tell the downstream layers (main-net) to switch the focus, even if a part of the input bit-stream has the same value. We applied the centrifuge mechanism to compiler provenance recovery, a type of BSR, and achieved excellent classification. Additionally, downstream transfer learning (DTL), one of the learning methods we propose for the centrifuge mechanism, pre-trains the main-net using the sub-net's ground truth instead of the sub-net's output. We found that sub-predictions made by DTL tend to be highly accurate when the sub-label classification contributes to the essence of the main prediction.

Bruce Rosenblum switched on his Apple II, which rang out a high F note followed by the clatter of the floppy drive. After a string of thock thock keystrokes, the 12-inch Sanyo monitor began to phosphoresce. A green grid appeared, 16 units wide and 16 units tall. This was "Gridmaster," a program Bruce had cooked up in the programming language BASIC to build one of the world's first Chinese digital fonts. He was developing the font for an experimental machine called the Sinotype III, which was among the first personal computers to handle Chinese-language input and output.

In this issue of Communications, as evidenced by the cover and lead article, we celebrate the latest recipients of the ACM A.M. Turing Award. Yoshua Bengio, Yann LeCun, and Geoffrey Hinton carried out pioneering work in deep learning that has touched all our lives. As Turing Laureates, they now join the eminent group of technology visionaries recognized with the world's highest distinction in computing. The Turing Award is one of a suite of professional honors ACM bestows annually to recognize technical achievements that have made significant contributions to our field. This month, I will have the pleasure of joining the awardees, ACM Fellows, and other luminaries in San Francisco for the ACM Awards Banquet.

A nurse asks a patient to describe her symptoms. A fast-food worker greets a customer and asks for his order. A tourist asks a police officer for directions to a local point of interest. For those with all of their physical faculties intact, each of these scenarios can be viewed as a routine occurrence of everyday life, as they are able to easily and efficiently interact without any assistance. However, each of these interactions are significantly more difficult when a person is deaf, and must rely on the use of sign language to communicate.