Multimodal integration is an important process in perceptual decision-making. In humans, this process has often been shown to be statistically optimal, or near optimal: sensory information is combined in a fashion that minimises the average error in perceptual representation of stimuli. However, sometimes there are costs that come with the optimization, manifesting as illusory percepts. We review audio-visual facilitations and illusions that are products of multisensory integration, and the computational models that account for these phenomena. In particular, the same optimal computational model can lead to illusory percepts, and we suggest that more studies should be needed to detect and mitigate these illusions, as artefacts in artificial cognitive systems. We provide cautionary considerations when designing artificial cognitive systems with the view of avoiding such artefacts. Finally, we suggest avenues of research towards solutions to potential pitfalls in system design. We conclude that detailed understanding of multisensory integration and the mechanisms behind audio-visual illusions can benefit the design of artificial cognitive systems.

Microsoft is blending Indigenous knowledge with AI to protect parts of Kakadu National Park. The tech behemoth partnered with the CSIRO and Indigenous rangers at the World Heritage-listed park to restore native wildlife using artificial intelligence (AI). Located in the Northern Territory, Kakadu is jointly managed by Parks Australia and traditional Indigenous owners. Its wetlands are home to protected Australian species such as magpie geese, which are considered by traditional custodians as a major indicator of'healthy country'. But the introduction of a weed called para grass has seen the reduction of native plants and has removed habitats for the magpie geese. Michael Douglas, leader of NESP Northern Australia Environmental Resources Hub, explained that para grass was planted in the area during the late 1960s before Kakadu was a national park, used as buffalo and cattle feed.

TECHNOLOGIES such as artificial intelligence (AI), machine learning (ML), the internet of things (IoT), 5G, and robotics have plenty of potential in the healthcare space. However, most healthcare organizations are unable to leverage those technologies because they neither have the digital infrastructure nor the technology talent to turn their vision into real projects. "Health services have a lot to do before they can make the most of these opportunities," Waitematā District Health Board (DHB) Clinical Director Innovation Robyn Whittaker told Tech Wire Asia underscoring the fact that industry professionals aren't oblivious to the power that technology holds. Whittaker and her team at Waitematā DHB have just made it to the list of finalists in the talent accelerator category at the IDC Digital Transformation Awards. To prepare for the digital age, Whittaker's team is working on the foundational layers, digitizing data, and making it available for clinicians and researchers looking to make sense of that data.

NEW DELHI – Japan and India agreed Saturday to conduct their first joint fighter aircraft exercise in Japan as part of efforts to promote bilateral security cooperation in the face of China's military buildup and regional assertiveness. In inaugural "two-plus-two" security talks, the nations' foreign and defense ministers also welcomed the significant progress in negotiations for a pact that would allow the sharing of defense capabilities and supplies including fuel and ammunition. They called for a speedy conclusion to the acquisition and cross-servicing agreement (ACSA), according to a joint statement issued after the talks in New Delhi. The two governments are planning to sign the deal when Prime Minister Shinzo Abe visits India for talks with Prime Minister Narendra Modi in mid-December, according to Japanese officials. Tokyo and New Delhi aim to have a joint exercise involving fighter jets from the Air Self-Defense Force and the Indian Air Force next year, the officials said.

Graph embedding methods transform high-dimensional and complex graph contents into low-dimensional representations. They are useful for a wide range of graph analysis tasks including link prediction, node classification, recommendation and visualization. Most existing approaches represent graph nodes as point vectors in a low-dimensional embedding space, ignoring the uncertainty present in the real-world graphs. Furthermore, many real-world graphs are large-scale and rich in content (e.g. node attributes). In this work, we propose GLACE, a novel, scalable graph embedding method that preserves both graph structure and node attributes effectively and efficiently in an end-to-end manner. GLACE effectively models uncertainty through Gaussian embeddings, and supports inductive inference of new nodes based on their attributes. In our comprehensive experiments, we evaluate GLACE on real-world graphs, and the results demonstrate that GLACE significantly outperforms state-of-the-art embedding methods on multiple graph analysis tasks.

Information regarding the location of power distribution grid can be extracted from the power signature embedded in the multimedia signals (e.g., audio, video data) recorded near electrical activities. This implicit mechanism of identifying the origin-of-recording can be a very promising tool for multimedia forensics and security applications. In this work, we have developed a novel grid-of-origin identification system from media recording that consists of a number of support vector machine (SVM) followed by pole-matching (PM) classifiers. First, we determine the nominal frequency of the grid (50 or 60 Hz) based on the spectral observation. Then an SVM classifier, trained for the detection of a grid with a particular nominal frequency, narrows down the list of possible grids on the basis of di ff erent discriminating features extracted from the electric network frequency (ENF) signal. The decision of the SVM classifier is then passed to the PM classifier that detects the final grid based on the minimum distance between the estimated poles of test and training grids. Thus, we start from the problem of classifying grids with di fferent nominal frequencies and simplify the problem of classification in three stages based on nominal frequency, SVM and finally using PM classifier. This cascaded system of classification ensures better accuracy (15 .57% Keywords: Location forensics, ENF, nominal frequency, SVM, AR model, pole-matching classifier. 1. Introduction With the proliferation of terrorism, child pornography [1] or abuse on women, location forensics has become an important area of research in the 21 Success in identifying such locations properly can ease the process of getting hold of the criminals involved.

Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes. Much research has been devoted to the design of smart loss functions or data mining strategies for training such networks. Most methods consider only pairs or triplets of samples within a mini-batch to compute the loss function, which is commonly based on the distance between embeddings. We propose Group Loss, a loss function based on a differentiable label-propagation method that enforces embedding similarity across all samples of a group while promoting, at the same time, low-density regions amongst data points belonging to different groups. Guided by the smoothness assumption that "similar objects should belong to the same group", the proposed loss trains the neural network for a classification task, enforcing a consistent labelling amongst samples within a class. We show state-of-the-art results on clustering and image retrieval on several datasets, and show the potential of our method when combined with other techniques such as ensembles

Hierarchical forecasting (HF) is needed in many situations in the supply chain (SC) because managers often need different levels of forecasts at different levels of SC to make a decision. Top-Down (TD), Bottom-Up (BU) and Optimal Combination (COM) are common HF models. These approaches are static and often ignore the dynamics of the series while disaggregating them. Consequently, they may fail to perform well if the investigated group of time series are subject to large changes such as during the periods of promotional sales. We address the HF problem of predicting real-world sales time series that are highly impacted by promotion. We use three machine learning (ML) models to capture sales variations over time. Artificial neural networks (ANN), extreme gradient boosting (XGboost), and support vector regression (SVR) algorithms are used to estimate the proportions of lower-level time series from the upper level. We perform an in-depth analysis of 61 groups of time series with different volatilities and show that ML models are competitive and outperform some well-established models in the literature.

Asynchronous Advantage Actor Critic (A3C) is an effective Reinforcement Learning (RL) algorithm for a wide range of tasks, such as Atari games and robot control. The agent learns policies and value function through trial-and-error interactions with the environment until converging to an optimal policy. Robustness and stability are critical in RL; however, neural network can be vulnerable to noise from unexpected sources and is not likely to withstand very slight disturbances. We note that agents generated from mild environment using A3C are not able to handle challenging environments. Learning from adversarial examples, we proposed an algorithm called Adversary Robust A3C (AR-A3C) to improve the agent's performance under noisy environments. In this algorithm, an adversarial agent is introduced to the learning process to make it more robust against adversarial disturbances, thereby making it more adaptive to noisy environments. Both simulations and real-world experiments are carried out to illustrate the stability of the proposed algorithm. The AR-A3C algorithm outperforms A3C in both clean and noisy environments.

Conformance checking techniques let us find out to what degree a process model and real execution data correspond to each other. In recent years, alignments have proven extremely useful in calculating conformance statistics. Most techniques to compute alignments provide an exact solution. However, in many applications, it is enough to have an approximation of the conformance value. Specifically, for large event data, the computing time for alignments is considerably long using current techniques which makes them inapplicable in reality. Also, it is no longer feasible to use standard hardware for complex processes. Hence, we need techniques that enable us to obtain fast, and at the same time, accurate approximation of the conformance values. This paper proposes new approximation techniques to compute approximated conformance checking values close to exact solution values in a faster time. Those methods also provide upper and lower bounds for the approximated alignment value. Our experiments on real event data show that it is possible to improve the performance of conformance checking by using the proposed methods compared to using the state-of-the-art alignment approximation technique. Results show that in most of the cases, we provide tight bounds, accurate approximated alignment values, and similar deviation statistics.