This paper features convolutional neural networks defined on hypercomplex algebras applied to classify lymphocytes in blood smear digital microscopic images. Such classification is helpful for the diagnosis of acute lymphoblast leukemia (ALL), a type of blood cancer. We perform the classification task using eight hypercomplex-valued convolutional neural networks (HvCNNs) along with real-valued convolutional networks. Our results show that HvCNNs perform better than the real-valued model, showcasing higher accuracy with a much smaller number of parameters. Moreover, we found that HvCNNs based on Clifford algebras processing HSV-encoded images attained the highest observed accuracies. Precisely, our HvCNN yielded an average accuracy rate of 96.6% using the ALL-IDB2 dataset with a 50% train-test split, a value extremely close to the state-of-the-art models but using a much simpler architecture with significantly fewer parameters.

Tractian, a machine intelligence company offering one of the most advanced industrial monitoring systems on the market, announced $15 million in Series A funding led by Next47, a global venture capital firm specializing in building category-defining B2B technology businesses. YCombinator and other previous investors also participated in the round. The new capital will allow the company to consolidate its position in the global market by extending operations from Brazil to Mexico and the U.S. and continuing rapid development of industry-leading products. "We know the industries that empower their frontline workers with best-in-class productivity tools have superpowers compared to others, and Tractian appears as the right arm of maintenance managers to manage their routines around the world" Tractian has developed streamlined hardware-software solutions designed to give maintenance technicians and decision-makers comprehensive oversight of their operations. With ease of installation and quick value generation at the heart of its customer approach, Tractian is democratizing access to sophisticated monitoring and analytics.

Quadintel published a new report on the Latin America Machine Learning Market.

The threat of wildfires has never been greater than it is today. In recent years, countries around the world – from the US, Argentina and Brazil to Italy, Greece and Australia – have been gravely affected by wildfires. This has resulted in many human and animal deaths, as well as the loss of millions of hectares of forests. And wildfire risks continue to grow – a recent UN Environment Programme report warns that the number of wildfires will rise by 50% by 2100 and governments are not prepared.

Maybe the worse thing that can happen to an idea is being born on the wrong moment, or/and even wrong place. Take the case of YouTube, was is the first video streaming platform? But it was born on the right moment! "In 1999–2000 it was too hard to watch online content you had to put codecs in your browser and do all this stuff [about company that failed two years before YouTube]" Bill Gross It was somehow similar with deep learning, since the act adding more hidden-layers is not new, and it is even straightfoward: anyone with a outside thinking could try that out, and have succeeded if we had the proper tools. What made deep learning just now? Indeed, it is amazing how fast hardware evolved, in special for personal usage.

A tool has been developed to help healthcare professionals identify hospitalised patients most at risk of dying from COVID-19 using artificial intelligence (AI). The algorithm could help doctors to direct critical care resources to those in most immediate need, which the developers of the AI tool say could be especially valuable to resource-limited countries. And with no end in sight for the coronavirus pandemic, with new variants leading to fresh waves of sickness and hospitalisation, the scientists behind the tool say there is a need for generalised tools like this which can be easily rolled out. To develop the tool, scientists used biochemical data from routine blood samples taken from nearly 30,000 patients hospitalised in over 150 hospitals in Spain, the US, Honduras, Bolivia and Argentina between March 2020 and February 2022. Taking blood from so many patients meant the team were able to capture data from people with different immune statuses – vaccinated, unvaccinated and those with natural immunity – and from people infected with every variant of COVID-19.

Givaudan has developed Consumer Foresight, a new tool that aims to help its customers co-create and innovate. This'futurescaping' platform will leverage big data, artificial intelligence and Givaudan's'deep expertise' of the food and beverage sector. It is a step beyond the trend forecasting models of today, Taste & Wellbeing President Louie D'Amico believes. "Most trend forecasting models largely focus on understanding the past and the present. Customer Foresight will be more predictive, with an ability to create potential future scenarios of what should be possible tomorrow to shape the future of food," he told FoodNavigator.

Incorporating ethics and legal compliance into data-driven algorithmic systems has been attracting significant attention from the computing research community, most notably under the umbrella of fair8 and interpretable16 machine learning. While important, much of this work has been limited in scope to the "last mile" of data analysis and has disregarded both the system's design, development, and use life cycle (What are we automating and why? Is the system working as intended? Are there any unforeseen consequences post-deployment?) and the data life cycle (Where did the data come from? How long is it valid and appropriate?). In this article, we argue two points. First, the decisions we make during data collection and preparation profoundly impact the robustness, fairness, and interpretability of the systems we build. Second, our responsibility for the operation of these systems does not stop when they are deployed. To make our discussion concrete, consider the use of predictive analytics in hiring. Automated hiring systems are seeing ever broader use and are as varied as the hiring practices themselves, ranging from resume screeners that claim to identify promising applicantsa to video and voice analysis tools that facilitate the interview processb and game-based assessments that promise to surface personality traits indicative of future success.c Bogen and Rieke5 describe the hiring process from the employer's point of view as a series of decisions that forms a funnel, with stages corresponding to sourcing, screening, interviewing, and selection. The hiring funnel is an example of an automated decision system--a data-driven, algorithm-assisted process that culminates in job offers to some candidates and rejections to others. The popularity of automated hiring systems is due in no small part to our collective quest for efficiency.

This is my last editorial as Editor-in-Chief of Communications,a so it is a moment to share learnings and, of course, to reflect on accomplishments. First, we launched the Regional Special Sections (RSS) in November 2018 with a spotlight on computing in the China Region. With 40 pages of articles, spanning tech idols to gaming to computing culture to fintech and "superAI," the first RSS created an excitement that inspired and challenged co-hosts of the Europe, India, East Asia and Oceania, Latin America, and Arabia Regions. In just three years, we have circumnavigated the globe,b and with the second Europe Region Section (April 2022) and India Region Section (November 2022), a new circuit is well under way! The RSS are an exciting read for the ACM community (great job by the co-hosts and authors), delivering news insights and perspectives into how computing is shaping and being shaped around the world.

GitHub is the world's largest host of source code, with more than 150M repositories. However, most of these repositories are not labeled or inadequately so, making it harder for users to find relevant projects. There have been various proposals for software application domain classification over the past years. However, these approaches lack a well-defined taxonomy that is hierarchical, grounded in a knowledge base, and free of irrelevant terms. This work proposes GitRanking, a framework for creating a classification ranked into discrete levels based on how general or specific their meaning is. We collected 121K topics from GitHub and considered $60\%$ of the most frequent ones for the ranking. GitRanking 1) uses active sampling to ensure a minimal number of required annotations; and 2) links each topic to Wikidata, reducing ambiguities and improving the reusability of the taxonomy. Our results show that developers, when annotating their projects, avoid using terms with a high degree of specificity. This makes the finding and discovery of their projects more challenging for other users. Furthermore, we show that GitRanking can effectively rank terms according to their general or specific meaning. This ranking would be an essential asset for developers to build upon, allowing them to complement their annotations with more precise topics. Finally, we show that GitRanking is a dynamically extensible method: it can currently accept further terms to be ranked with a minimum number of annotations ($\sim$ 15). This paper is the first collective attempt to build a ground-up taxonomy of software domains.