We present the first empirical study on customer churn prediction in the scholarly publishing industry. The study examines our proposed method for prediction on a customer subscription data over a period of 6.5 years, which was provided by a major academic publisher. We explore the subscription-type market within the context of customer defection and modelling, and provide analysis of the business model of such markets, and how these characterise the academic publishing business. The proposed method for prediction attempts to provide inference of customer's likelihood of defection on the basis of their re-sampled use of provider resources -in this context, the volume and frequency of content downloads. We show that this approach can be both accurate as well as uniquely useful in the business-to-business context, with which the scholarly publishing business model shares similarities. The main findings of this work suggest that whilst all predictive models examined, especially ensemble methods of machine learning, achieve substantially accurate prediction of churn, nearly a year ahead, this can be furthermore achieved even when the specific behavioural attributes that can be associated to each customer probability to churn are overlooked. Allowing as such highly accurate inference of churn from minimal possible data. We show that modelling churn on the basis of re-sampling customers' use of resources over subscription time is a better (simplified) approach than when considering the high granularity that can often characterise consumption behaviour.

Commercial adoption of automatic music composition requires the capability of generating diverse and high-quality music suitable for the desired context (e.g., music for romantic movies, action games, restaurants, etc.). In this paper, we introduce combinatorial music generation, a new task to create varying background music based on given conditions. Combinatorial music generation creates short samples of music with rich musical metadata, and combines them to produce a complete music. In addition, we introduce ComMU, the first symbolic music dataset consisting of short music samples and their corresponding 12 musical metadata for combinatorial music generation. Notable properties of ComMU are that (1) dataset is manually constructed by professional composers with an objective guideline that induces regularity, and (2) it has 12 musical metadata that embraces composers' intentions. Our results show that we can generate diverse high-quality music only with metadata, and that our unique metadata such as track-role and extended chord quality improves the capacity of the automatic composition. We highly recommend watching our video before reading the paper (https://pozalabs.github.io/ComMU/).

In this paper, we design an auto encoder based off of Google's FNet Architecture in order to generate text from a subset of news stories contained in Google's C4 dataset. We discuss previous attempts and methods to generate text from autoencoders and non LLM Models. FNET poses multiple advantages to BERT based encoders in the realm of efficiency which train 80% faster on GPUs and 70% faster on TPUs. We then compare outputs of how this autencoder perfroms on different epochs. Finally, we analyze what outputs the encoder produces with different seed text.

Microblink, a global leader in AI-powered computer vision technology, has been listed as one of the fastest growing companies on the prestigious Deloitte's Technology Fast 500 list. Microblink's exponential growth rate of 284 percent is attributed to its enterprise-ready AI solutions and best-in-class user experience born of a culture of curiosity and a fearless team. Recognized for the third time, Microblink's technology leverages Artificial Intelligence and Machine Learning to generate solutions for its two business segments; Identity and Commerce. The identity product portfolio encompasses solutions for ID scanning, identity document verification and identity verification to improve the customer onboarding experience. For commerce businesses, they are developing solutions that create magical shopping experiences while using purchasing data to empower retailers and CPGs.

As one of the most popular programming languages Python is dynamically-typed and garbage-collected. Learn free step by step Python programming on this website Our aim is to make learning easy even for a layman. Simply follow the index on this page to learn each topic. Why should you learn Python? In fact, Youtube was built using mostly Python!

In 2012, a short movie called KARA was released. In the movie, a robot called KARA is about to be tested and initialized before being forwarded to Supply Chain and sold on the market. But shortly before it happens, KARA raises her voice. She doesn’t want to be merchandise; she thinks she is alive. In moving, yet heartbreaking sequences, as the human operator is about to disassemble her as a defective model, she expresses so much that the human stops. And silently cursing, he turns a blind eye to this malfunction — and she is set to be free (at least still in a package). This short was a proof of concept for the long-term video game “Detroit: Become Human” by Quantic Dream, a storytelling masterpiece published in 2018. In one scene of the game, an Android, Markus (acted via motion capture by Jesse Williams), is working as a butler in the house of Carl, an artist

Seekr, an internet technology company that offers information discovery and content evaluation, and Freestar, a leading monetization partner for publishers, e-commerce sites and app developers, today announced a partnership to monetize search on Seekr that will offer online advertisers the opportunity to target high-quality, brand-safe content. Powered by AI, Seekr offers the first search engine that reimagines what web results can look like when bias and misinformation are removed. Seekr will offer brand-safe targeting capabilities using the Seekr Score, a set of proprietary algorithms that incorporate machine learning to sift through daily news stories and offer a score that reflects the reliability and credibility of every article. The inclusion of the Seekr Score will allow brands to target advertisements based on an article's reliability and will bolster confidence for brands concerned with having their ads appear next to poor quality, unreliable, or potentially brand-damaging content. Programmatically sold advertising was worth $418 billion in 2021, and is expected to reach $725 billion by 2026.

Social media has become an important data source for event analysis. When collecting this type of data, most contain no useful information to a target event. Thus, it is essential to filter out those noisy data at the earliest opportunity for a human expert to perform further inspection. Most existing solutions for event filtering rely on fully supervised methods for training. However, in many real-world scenarios, having access to large number of labeled samples is not possible. To deal with a few labeled sample training problem for event filtering, we propose a graph-based few-shot learning pipeline. We also release the Brazilian Protest Dataset to test our method. To the best of our knowledge, this dataset is the first of its kind in event filtering that focuses on protests in multi-modal social media data, with most of the text in Portuguese. Our experimental results show that our proposed pipeline has comparable performance with only a few labeled samples (60) compared with a fully labeled dataset (3100). To facilitate the research community, we make our dataset and code available at https://github.com/jdnascim/7Set-AL.

Tabla is a North Indian percussion instrument used as an accompaniment and an exclusive instrument for solo performances. Tabla solo is intricate and elaborate, exhibiting rhythmic evolution through a sequence of homogeneous sections marked by shared rhythmic characteristics. Each section has a specific structure and name associated with it. Tabla learning and performance in the Indian subcontinent is based on stylistic schools called gharana-s. Several compositions by various composers from different gharana-s are played in each section. This paper addresses the task of segmenting the tabla solo concert into musically meaningful sections. We then assign suitable section labels and recognize gharana-s from the sections. We present a diverse collection of over 38 hours of solo tabla recordings for the task. We motivate the problem and present different challenges and facets of the tasks. Inspired by the distinct musical properties of tabla solo, we compute several rhythmic and timbral features for the segmentation task. This work explores the approach of automatically locating the significant changes in the rhythmic structure by analyzing local self-similarity in an unsupervised manner. We also explore supervised random forest and a convolutional neural network trained on hand-crafted features. Both supervised and unsupervised approaches are also tested on a set of held-out recordings. Segmentation of an audio piece into its structural components and labeling is crucial to many music information retrieval applications like repetitive structure finding, audio summarization, and fast music navigation. This work helps us obtain a comprehensive musical description of the tabla solo concert.

The development of generative Machine Learning (ML) models in creative practices, enabled by the recent improvements in usability and availability of pre-trained models, is raising more and more interest among artists, practitioners and performers. Yet, the introduction of such techniques in artistic domains also revealed multiple limitations that escape current evaluation methods used by scientists. Notably, most models are still unable to generate content that lay outside of the domain defined by the training dataset. In this paper, we propose an alternative prospective framework, starting from a new general formulation of ML objectives, that we derive to delineate possible implications and solutions that already exist in the ML literature (notably for the audio and musical domain). We also discuss existing relations between generative models and computational creativity and how our framework could help address the lack of creativity in existing models.