The July 2021 issue of IEEE/CAA Journal of Automatica Sinica features six articles that showcase the potential of machine learning in its various forms. The applications described in the studies range from advanced driver assistance systems and computer vision to image processing and collaborative robotics. Automation of technology has reshaped both the way in which we work and how we tackle problems. Thanks to the progress made in robotics and artificial intelligence (AI) over the last few years, it is now possible to leave several tasks in the hands to machines and algorithms. To highlight these advances, the IEEE and the Chinese Association of Automation (CAA) decided to join forces, in the first issue of IEEE/CAA Journal of Automatica Sinica.

Researchers have developed an artificial intelligence monitoring system to keep track of whether people are social distancing in public spaces, and alerting local authorities if they are not. Griffith University researchers developed the system to monitor the movement of crowds of people in real-time and then applied it to look for instances of people not maintaining social distancing. An AI system which can detected social distancing breaches in real time has been praised by its creators, but raised worries with privacy advocates.Credit:Paul Jeffers Lead researcher Professor Dian Tjondronegoro, an expert in the integration of AI and business innovation, said they moved to allay any privacy concerns around the system by ensuring that no data was stored by it at any time. "We knew we couldn't keep everything on the server because it would be very slow in processing and there are also privacy concerns," he said.

Scania, the Swedish manufacturer of heavy lorries, trucks and buses, is testing L4 level self-driving trucks on the E4 motorway between Södertälje and Jönköping, in collaboration with San Diego-based company TuSimple. Participating truck provides actual commercial services to The Scania Transport Laboratory, loading materials required for production operations. The truck is controlled by the TuSimple's unmanned driving system, with a safety officer and test engineer onboard for monitoring. Scania has been testing self-driving trucks for mining transportation in Australia since 2017. TuSimple has also partnered with companies like Volkswagen and Navistar to test commercial vehicles.

Eye gaze has the potential to provide insight into the minds of individuals, and this idea has been used in prior research to improve human goal recognition by combining human's actions and gaze. However, most existing research assumes that people are rational and honest. In adversarial scenarios, people may deliberately alter their actions and gaze, which presents a challenge to goal recognition systems. In this paper, we present new models for goal recognition under deception using a combination of gaze behaviour and observed movements of the agent. These models aim to detect when a person is deceiving by analysing their gaze patterns and use this information to adjust the goal recognition. We evaluated our models in two human-subject studies: (1) using data collected from 30 individuals playing a navigation game inspired by an existing deception study and (2) using data collected from 40 individuals playing a competitive game (Ticket To Ride). We found that one of our models (Modulated Deception Gaze Ontic) offers promising results compared to the previous state-of-the-art model in both studies. Our work complements existing adversarial goal recognition systems by equipping these systems with the ability to tackle ambiguous gaze behaviours.

Deep learning has achieved great success in a wide spectrum of multimedia applications such as image classification, natural language processing and multimodal data analysis. Recent years have seen the development of many deep learning frameworks that provide a high-level programming interface for users to design models, conduct training and deploy inference. However, it remains challenging to build an efficient end-to-end multimedia application with most existing frameworks. Specifically, in terms of usability, it is demanding for non-experts to implement deep learning models, obtain the right settings for the entire machine learning pipeline, manage models and datasets, and exploit external data sources all together. Further, in terms of adaptability, elastic computation solutions are much needed as the actual serving workload fluctuates constantly, and scaling the hardware resources to handle the fluctuating workload is typically infeasible. To address these challenges, we introduce SINGA-Easy, a new deep learning framework that provides distributed hyper-parameter tuning at the training stage, dynamic computational cost control at the inference stage, and intuitive user interactions with multimedia contents facilitated by model explanation. Our experiments on the training and deployment of multi-modality data analysis applications show that the framework is both usable and adaptable to dynamic inference loads. We implement SINGA-Easy on top of Apache SINGA and demonstrate our system with the entire machine learning life cycle.

Machine learning (ML) models are known to be vulnerable to adversarial examples. Applications of ML to voice biometrics authentication are no exception. Yet, the implications of audio adversarial examples on these real-world systems remain poorly understood given that most research targets limited defenders who can only listen to the audio samples. Conflating detectability of an attack with human perceptibility, research has focused on methods that aim to produce imperceptible adversarial examples which humans cannot distinguish from the corresponding benign samples. We argue that this perspective is coarse for two reasons: 1. Imperceptibility is impossible to verify; it would require an experimental process that encompasses variations in listener training, equipment, volume, ear sensitivity, types of background noise etc, and 2. It disregards pipeline-based detection clues that realistic defenders leverage. This results in adversarial examples that are ineffective in the presence of knowledgeable defenders. Thus, an adversary only needs an audio sample to be plausible to a human. We thus introduce surreptitious adversarial examples, a new class of attacks that evades both human and pipeline controls. In the white-box setting, we instantiate this class with a joint, multi-stage optimization attack. Using an Amazon Mechanical Turk user study, we show that this attack produces audio samples that are more surreptitious than previous attacks that aim solely for imperceptibility. Lastly we show that surreptitious adversarial examples are challenging to develop in the black-box setting.

The power system is undergoing rapid evolution with the roll-out of advanced metering infrastructure and local energy applications (e.g. electric vehicles) as well as the increasing penetration of intermittent renewable energy at both transmission and distribution level, which characterizes the peak load demand with stronger randomness and less predictability and therefore poses a threat to the power grid security. Since storing large quantities of electricity to satisfy load demand is neither economically nor environmentally friendly, effective peak demand management strategies and reliable peak load forecast methods become essential for optimizing the power system operations. To this end, this paper provides a timely and comprehensive overview of peak load demand forecast methods in the literature. To our best knowledge, this is the first comprehensive review on such topic. In this paper we first give a precise and unified problem definition of peak load demand forecast. Second, 139 papers on peak load forecast methods were systematically reviewed where methods were classified into different stages based on the timeline. Thirdly, a comparative analysis of peak load forecast methods are summarized and different optimizing methods to improve the forecast performance are discussed. The paper ends with a comprehensive summary of the reviewed papers and a discussion of potential future research directions.

Reading comprehension is a complex cognitive process involving many human brain activities. Plenty of works have studied the reading patterns and attention allocation mechanisms in the reading process. However, little is known about what happens in human brain during reading comprehension and how we can utilize this information as implicit feedback to facilitate information acquisition performance. With the advances in brain imaging techniques such as EEG, it is possible to collect high-precision brain signals in almost real time. With neuroimaging techniques, we carefully design a lab-based user study to investigate brain activities during reading comprehension. Our findings show that neural responses vary with different types of contents, i.e., contents that can satisfy users' information needs and contents that cannot. We suggest that various cognitive activities, e.g., cognitive loading, semantic-thematic understanding, and inferential processing, at the micro-time scale during reading comprehension underpin these neural responses. Inspired by these detectable differences in cognitive activities, we construct supervised learning models based on EEG features for two reading comprehension tasks: answer sentence classification and answer extraction. Results show that it is feasible to improve their performance with brain signals. These findings imply that brain signals are valuable feedback for enhancing human-computer interactions during reading comprehension.

One widely used approach towards understanding the inner workings of deep convolutional neural networks is to visualize unit responses via activation maximization. Feature visualizations via activation maximization are thought to provide humans with precise information about the image features that cause a unit to be activated. If this is indeed true, these synthetic images should enable humans to predict the effect of an intervention, such as whether occluding a certain patch of the image (say, a dog's head) changes a unit's activation. Here, we test this hypothesis by asking humans to predict which of two square occlusions causes a larger change to a unit's activation. Both a large-scale crowdsourced experiment and measurements with experts show that on average, the extremely activating feature visualizations by Olah et al. (2017) indeed help humans on this task ($67 \pm 4\%$ accuracy; baseline performance without any visualizations is $60 \pm 3\%$). However, they do not provide any significant advantage over other visualizations (such as e.g. dataset samples), which yield similar performance ($66 \pm 3\%$ to $67 \pm 3\%$ accuracy). Taken together, we propose an objective psychophysical task to quantify the benefit of unit-level interpretability methods for humans, and find no evidence that feature visualizations provide humans with better "causal understanding" than simple alternative visualizations.

The Australian Federal Court recently handed down its first-instance judgement in Thaler v Commissioner of Patents [2021] FCA 879 where the central issue considered was whether an artificial intelligence (AI) system could be an'inventor' for the purposes of the Australian Patents Act 1990 (Act) and its corresponding regulations. The Court found that an AI system can be an inventor – where'inventor' may be construed broadly to include a'person or thing that invents'1. This decision puts Australia in the spotlight as a favourable country to patent AI-created inventions – for now. Given the subject-matter and controversy generated by this decision, an appeal to the Full Federal Court is almost certain. This Federal Court decision is an appeal from a Patent Office hearing where the Office rejected Australian patent application no. Interestingly, the objection to inventorship was initially raised in a formalities objection issued within a few weeks after the application was filed, and not during examination which would be years later under normal circumstances.