Abstract: Techniques for generating user profile data including one or more frequent channels, related users, and/or related topics within a communication platform are discussed herein. In some examples, a machine-learning model can receive user interaction data (messages sent, messages read, channel posts, documents shared, frequent key words used, etc.) associated with the communication platform and output one or more frequent channels, related users, and/or related topics. The communication platform may then associate the one or more frequent channels, related users, and/or related topics with the user's profile data. In some examples, the communication platform may present different frequent channels, related users, and/or related topics associated with a profile page based on interaction action associated with the user account viewing the profile page.

Abstract: A band scrambler which processes only time domain samples is described. The band scrambler has the effect of dividing the input signal spectrum into N sub-bands. The N sub-bands are permuted such that the r th band is mapped onto the k.r th band modulo N, where N is a constant of the scrambler and k is the key which is variable in the range 2<k<N-1. The output samples y(n) produced by the scrambler from the input speech signal samples x(n) are defined by the equation: ##EQU1## The down-sampling function s(n'+n(k-1)) determines which of N series of ganged switches is closed and the window function h(n') determines the values of the factors stored in the multipliers. The summation is carried out by the adder in order to produce the required output time samples which are reconverted to an analogue signal via a digital-to-analogue converter. The signal can be de-scrambled by sampling and passing through another identical scrambler operating with a key, k*, where kk*=1 (mod N).