The ubiquity of mobile devices and wearable sensors offers unprecedented opportunities for continuous collection of multimodal physiological data. Such data enables temporal characterization of an individual's behaviors, which can provide unique insights into her physical and psychological health. Understanding the relation between different behaviors/activities and personality traits such as stress or work performance can help build strategies to improve the work environment. Especially in workplaces like hospitals where many employees are overworked, having such policies improves the quality of patient care by prioritizing mental and physical health of their caregivers. One challenge in analyzing physiological data is extracting the underlying behavioral states from the temporal sensor signals and interpreting them. Here, we use a non-parametric Bayesian approach, to model multivariate sensor data from multiple people and discover dynamic behaviors they share. We apply this method to data collected from sensors worn by a population of workers in a large urban hospital, capturing their physiological signals, such as breathing and heart rate, and activity patterns. We show that the learned states capture behavioral differences within the population that can help cluster participants into meaningful groups and better predict their cognitive and affective states. This method offers a practical way to learn compact behavioral representations from dynamic multivariate sensor signals and provide insights into the data.

In the machine learning field, dimensionality reduction is an important task. It mitigates the undesired properties of high-dimensional spaces to facilitate classification, compression, and visualization of high-dimensional data. During the last decade, researchers proposed many new (non-linear) techniques for dimensionality reduction. Most of these techniques are based on the intuition that data lies on or near a complex low-dimensional manifold that is embedded in the high-dimensional space. New techniques for dimensionality reduction aim at identifying and extracting the manifold from the high-dimensional space. Isomap is one of widely-used low-dimensional embedding methods, where geodesic distances on a weighted graph are incorporated with the classical scaling (metric multidimensional scaling). The Isomap chooses the nearest neighbours based on the distance only which causes bridges and topological instability. In this paper, we propose a new algorithm to choose the nearest neighbours to reduce the number of short-circuit errors and hence improves the topological stability. Because at any point on the manifold, that point and its nearest neighbours form a vector subspace and the orthogonal to that subspace is orthogonal to all vectors spans the vector subspace. The prposed algorithmuses the point itself and its two nearest neighbours to find the bases of the subspace and the orthogonal to that subspace which belongs to the orthogonal complementary subspace. The proposed algorithm then adds new points to the two nearest neighbours based on the distance and the angle between each new point and the orthogonal to the subspace. The superior performance of the new algorithm in choosing the nearest neighbours is confirmed through experimental work with several datasets.

Feature selection is a crucial preprocessing step in data analytics and machine learning. Classical feature selection algorithms select features based on the correlations between predictive features and the class variable and do not attempt to capture causal relationships between them. It has been shown that the knowledge about the causal relationships between features and the class variable has potential benefits for building interpretable and robust prediction models, since causal relationships imply the underlying mechanism of a system. Consequently, causality-based feature selection has gradually attracted greater attentions and many algorithms have been proposed. In this paper, we present a comprehensive review of recent advances in causality-based feature selection. To facilitate the development of new algorithms in the research area and make it easy for the comparisons between new methods and existing ones, we develop the first open-source package, called CausalFS, which consists of most of the representative causality-based feature selection algorithms (available at https://github.com/kuiy/CausalFS). Using CausalFS, we conduct extensive experiments to compare the representative algorithms with both synthetic and real-world data sets. Finally, we discuss some challenging problems to be tackled in future causality-based feature selection research.

In cooperative pathfinding problems, no-conflicts paths that bring several agents from their start location to their destination need to be planned. This problem can be efficiently solved by Multi-agent RRT*(MA-RRT*) algorithm, which offers better scalability than the classical algorithms, such as Optimal Anytime(OA), in sparse environments. However, the implementation of this algorithm in systems with limited memory is hindered because the number of nodes in the tree grows indefinitely as the paths get optimized. This paper proposes an improved version of MA-RRT*, called Multi-agent RRT* Fixed Node(MA-RRT*FN), which limits the number of nodes stored in the tree by removing the weak nodes which are not likely on the path reaching the goal. The results show that MA-RRT*FN performs close to MA-RRT* in terms of scalability and solution quality while the memory required is much lower and fixed.

To gauge the complexity of the juggling act utility firms must perform to stay in business, consider two statistics. In the four years to 2018, the number of Britons who switched their energy supplier almost doubled to 5.9 million; at the same time, the contribution of renewables to energy firms' output mix grew from about 13% to just shy of 20%. One represents a fundamental change in consumer expectations of the service they receive while the other highlights the political and environmental pressures being brought to bear on suppliers' operations. To the list of challenges that are adding to the pressures under which utilities operate, add the tightening – and disparate – the grip of regulators, the fracturing of transmission networks and the increasing influence of activist investors. Managing these changing times can be incredibly challenging for established utilities, especially at a time when technology is enabling venture-backed start-ups to move into niche segments of their operations.

As an industry defined by the relationship between patient and carer, at first glance it may seem incongruous to nudge healthcare towards a robotic future. In fact, artificial intelligence (AI) has the potential to completely reshape the health industry, offering greater support to human capabilities and allowing healthcare organizations to deliver higher-quality services more efficiently. AI is a broad term for computer systems that can "think" and act like humans. They can sense their environment, absorb information, learn from past experience, make decisions and take action. AI has transformative power for two reasons: the explosive growth in data, coupled with huge computational advances and processing speeds.

Edge computing is helping Newcrest Mining improve throughput and reduce downtime in Australia's largest underground block cave mine, the Cadia Valley gold mine in New South Wales. Newcrest Mining won the best Primary Industry Project in our 2019 IoT Awards for the project, which uses machine learning to optimise the level of crushed ore in bins, preventing downtime. Now Microsoft and its partner Insight Enterprises have released details about the solution and its benefits. The solution is improving productivity, reducing downtime and increasing throughput, Newcrest Mining CIO Gavin Wood stated in a press release. And the company has seen a return on investment within three months of starting to use the solution.

Civil court leaders have a newly strong interest in how artificial intelligence can improve the quality and efficiency of legal services in the justice system, especially for problems that self-represented litigants face [1, 2, 3, 4, 5]. The promise is that artificial intelligence can address the fundamental crises in courts: that ordinary people are not able to use the system clearly or efficiently; that courts struggle to manage vast amounts of information; and that litigants and judicial officials often have to make complex decisions with little support. If AI is able to gather and sift through vast troves of information, identify patterns, predict optimal strategies, detect anomalies, classify issues, and draft documents, the promise is that these capabilities could be harnessed for making the civil court system more accessible to people. The question then, is how real these promises are, and how they are being implemented and evaluated. Now that early experimentation and agenda-setting have begun, the study of AI as a means for enhancing the quality of justice in the civil court system deserves greater definition.

A study by the University of London and Vanson Bourne for WP Engine has concluded that transparency, trust and humanness are key drivers to unlock value in artificial intelligence (AI). The study, which surveyed consumers and enterprise companies with 1,000 employees or more in the US, the UK and Australia, found a large proportion of businesses are already well on their way towards widespread AI implementation. Chris Brauer, director of innovation at Goldsmiths, University of London, said: "Our research shows enterprises investing in AI are already seeing astounding return on investment and performance outcomes. Consumers are demanding that innovating with AI in digital experiences clearly prioritises and expresses values around privacy, trust, and transparency. "Only by laying a solid foundation of ethics and values that guide the implementation of all facets of an AI solution will companies truly be able to fully harness the value of AI." The study reported that 85.5% of businesses ...

CHINA is selling deadly'Blowfish' drones which can decide who lives and who dies to armies in the war-torn Middle East, say reports. The unmanned war machines are capable of launching autonomous strikes with their arsenal of mortar shells, grenade launchers and machine guns. They are said to be "impossible to defend" against and the Pentagon has already made it clear it fears they will end up in the wrong hands. Some military experts fear the proposed sale of the AI mini-choppers will spark even more bloodshed in the troubled region, reports news.com. "They would be impossible to defend yourself against," warns University of New South Wales Professor of Artificial Intelligence Toby Walsh.