With advances in data-driven machine learning research, a wide variety of prediction models have been proposed to capture spatio-temporal features for the analysis of video streams. Recognising actions and detecting action transitions within an input video are challenging but necessary tasks for applications that require real-time human-machine interaction. By reviewing a large body of recent related work in the literature, we thoroughly analyse, explain and compare action segmentation methods and provide details on the feature extraction and learning strategies that are used on most state-of-the-art methods. We cover the impact of the performance of object detection and tracking techniques on human action segmentation methodologies. We investigate the application of such models to real-world scenarios and discuss several limitations and key research directions towards improving interpretability, generalisation, optimisation and deployment.

Convergint, a global leader in service-based systems integration, announced plans to acquire Prosys Services, an Australian tier-one provider in security management. Prosys Services is Convergint's third acquisition in Australia, adding 124 colleagues, a deepened presence in New South Wales (NSW), and a new office in the Australian Capital Territory (ACT). "This is another important milestone in expanding our capabilities and service delivery in Oceania, and in our mission to be our customers' best service provider around the globe." "We're thrilled to bring Prosys' team of talented colleagues to Convergint," said Tony Wang, CEO of ICD Security Solutions, Convergint's APAC subsidiary. "From its focus on continuous development to its strong company culture, Prosys has an incredible reputation built on a long-standing commitment to innovating for the future of security technology. This acquisition further accelerates our growth strategy in APAC and allows us to extend our best-in-class service delivery to global enterprises in the region."

Dr. Dorel Iosif is a Board Director and CEO with Cognisium, a tech executive marketplace headquartered in Australia. He held senior executive roles with KBR, WorleyParsons, PwC and Advisian Management Consulting. Dr Iosif started his career in Israel with the Technion Institute of Technology and continued in Australia with BHPBilliton and the University of Melbourne. He holds a Ph.D in applied mathematics from the University of Melbourne and studied Corporate Level Strategy - Executive Program at Harvard Business School. Dorel worked in Australia, USA, Europe and the Middle East.

Machine Learning explainability techniques have been proposed as a means of `explaining' or interrogating a model in order to understand why a particular decision or prediction has been made. Such an ability is especially important at a time when machine learning is being used to automate decision processes which concern sensitive factors and legal outcomes. Indeed, it is even a requirement according to EU law. Furthermore, researchers concerned with imposing overly restrictive functional form (e.g., as would be the case in a linear regression) may be motivated to use machine learning algorithms in conjunction with explainability techniques, as part of exploratory research, with the goal of identifying important variables which are associated with an outcome of interest. For example, epidemiologists might be interested in identifying `risk factors' - i.e. factors which affect recovery from disease - by using random forests and assessing variable relevance using importance measures. However, and as we demonstrate, machine learning algorithms are not as flexible as they might seem, and are instead incredibly sensitive to the underling causal structure in the data. The consequences of this are that predictors which are, in fact, critical to a causal system and highly correlated with the outcome, may nonetheless be deemed by explainability techniques to be unrelated/unimportant/unpredictive of the outcome. Rather than this being a limitation of explainability techniques per se, we show that it is rather a consequence of the mathematical implications of regression, and the interaction of these implications with the associated conditional independencies of the underlying causal structure. We provide some alternative recommendations for researchers wanting to explore the data for important variables.

Occlusions caused by a robot's own body is a common problem for closed-loop control methods employed in eye-to-hand camera setups. We propose an optimization-based reactive controller that minimizes self-occlusions while achieving a desired goal pose. The approach allows coordinated control between the robot's base, arm and head by encoding the line-of-sight visibility to the target as a soft constraint along with other task-related constraints, and solving for feasible joint and base velocities. The generalizability of the approach is demonstrated in simulated and real-world experiments, on robots with fixed or mobile bases, with moving or fixed objects, and multiple objects. The experiments revealed a trade-off between occlusion rates and other task metrics. While a planning-based baseline achieved lower occlusion rates than the proposed controller, it came at the expense of highly inefficient paths and a significant drop in the task success. On the other hand, the proposed controller is shown to improve visibility to the line target object(s) without sacrificing too much from the task success and efficiency. Videos and code can be found at: rhys-newbury.github.io/projects/vmc/.

Recent years have brought a stream of exciting developments in the field of Business Process Management (BPM). We have seen a breathtaking uptake of business process automation technology, such as Robotic Process Automation (RPA). We have witnessed the rise of process mining, and promising evolutions in the areas of predictive process analytics and digital process twins. In the eyes of a business analyst, each of these technologies offers compelling opportunities to enhance operational excellence. However, if we look at these technologies in isolation, it is easy to miss the bigger picture and the wider space of opportunities that these technologies open when used jointly rather than applied in individual projects or silos.

Data Scientists to join the Data Insights team at Zapier. Data Insights is responsible for driving impactful insight, experimentation, and quantitative research at Zapier. We work across Product, Growth & Revenue, Marketing, and Support, steering our business stakeholders to make data-informed decisions and deepening business understanding of opportunities and risks. Our Data Scientists are semi-embedded into different business zones, developing tight-knit thought partnerships with key stakeholders. We're hiring for a range of zones, so if you are a creative quantitative analyst interested in helping to grow a product that helps the world automate their work so they can get back to living, this may be the right challenge for you!

George Orwell's novel 1984 got one thing wrong. A surveillance state will not have people watching people, as the Stasi did in East Germany. Computers will be the ones watching people. Technology lets you perform surveillance at an industrial scale. This is already happening in China, where facial recognition software is being used by law enforcement for catching relatively minor offenders such as jaywalkers to enabling much more disturbing activities such as tracking Uyghurs.

The success of machine learning for a wide range of applications has come with serious costs. The largest deep neural networks can have hundreds of billions of parameters that need to be tuned to mammoth datasets. This computationally intensive training process can cost millions of dollars, as well as large amounts of energy and associated carbon. Inference, the subsequent application of a trained model to new data, is less demanding for each use, but for widely used applications, the cumulative energy use can be even greater. "Typically there will be more energy spent on inference than there is on training," said David Patterson, Professor Emeritus at the University of California, Berkeley, and a Distinguished Engineer at Google, who in 2017 shared ACM's A.M. Turing Award.

Deakin University says it welcomes the announcement from Hanwha Defense Australia, and the Australian Government, that its new armoured vehicle centre of excellence will be constructed at Avalon Airport. Deakin Vice-Chancellor Professor Iain Martin said the Geelong-based centre would cement the University's close connection with Hanwha, facilitating investment in local research expertise and the development of high-tech jobs. It's expected the new bespoke production facility will create a minimum of 300 jobs, spread across construction, acquisition and maintenance, as well as generating ongoing support opportunities for Australian industry until the late 2040s. Deakin and Hanwha Defense Australia signed a Memorandum of Understanding (MoU) last year, an agreement to drive collaboration in modelling and simulation, machine learning applications and human performance. "Deakin and Hanwha share a common goal in the development of emerging technologies and advanced manufacturing, with a focus on growing the local economy through education and employment opportunities," Professor Martin said.