The notion of Cyber-Physical-Social System (CPSS) is an emerging concept developed as a result of the need to understand the impact of Cyber-Physical Systems (CPS) on humans and vice versa. This paradigm shift from CPS to CPSS was mainly attributed to the increasing use of sensor-enabled smart devices and the tight link with the users. The concept of CPSS has been around for over a decade and it has gained increasing attention over the past few years. The evolution to incorporate human aspects in the CPS research has unlocked a number of research challenges. Particularly human dynamics brings additional complexity that is yet to be explored. The exploration to conceptualise the notion of CPSS has been partially addressed in few scientific literatures. Although its conceptualisation has always been use-case dependent. Thus, there is a lack of generic view as most works focus on specific domains. Furthermore, the systemic core and design principles linking it with the theory of systems are loose. This work aims at addressing these issues by first exploring and analysing scientific literature to understand the complete spectrum of CPSS through a Systematic Literature Review (SLR). Thereby identifying the state-of-the-art perspectives on CPSS regarding definitions, underlining principles and application areas. Subsequently, based on the findings of the SLR, we propose a domain-independent definition and a meta-model for CPSS, grounded in the Theory of Systems. Finally, a discussion on feasible future research directions is presented based on the systemic notion and the proposed meta-models.

Data-driven artificial intelligence models fed with published scientific findings have been used to create powerful prediction engines for scientific and technological advance, such as the discovery of novel materials with desired properties and the targeted invention of new therapies and vaccines. These AI approaches typically ignore the distribution of human prediction engines -- scientists and inventor -- who continuously alter the landscape of discovery and invention. As a result, AI hypotheses are designed to substitute for human experts, failing to complement them for punctuated collective advance. Here we show that incorporating the distribution of human expertise into self-supervised models by training on inferences cognitively available to experts dramatically improves AI prediction of future human discoveries and inventions. Including expert-awareness into models that propose (a) valuable energy-relevant materials increases the precision of materials predictions by ~100%, (b) repurposing thousands of drugs to treat new diseases increases precision by 43%, and (c) COVID-19 vaccine candidates examined in clinical trials by 260%. These models succeed by predicting human predictions and the scientists who will make them. By tuning AI to avoid the crowd, however, it generates scientifically promising "alien" hypotheses unlikely to be imagined or pursued without intervention, not only accelerating but punctuating scientific advance. By identifying and correcting for collective human bias, these models also suggest opportunities to improve human prediction by reformulating science education for discovery.

The remaining Useful Life (RUL) of equipment is defined as the duration between the current time and its failure. An accurate and reliable prognostic of the remaining useful life provides decision-makers with valuable information to adopt an appropriate maintenance strategy to maximize equipment utilization and avoid costly breakdowns. In this work, we propose an end-to-end deep learning model based on multi-layer perceptron and long short-term memory layers (LSTM) to predict the RUL. After normalization of all data, inputs are fed directly to an MLP layers for feature learning, then to an LSTM layer to capture temporal dependencies, and finally to other MLP layers for RUL prognostic. The proposed architecture is tested on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. Despite its simplicity with respect to other recently proposed models, the model developed outperforms them with a significant decrease in the competition score and in the root mean square error score between the predicted and the gold value of the RUL. In this paper, we will discuss how the proposed end-to-end model is able to achieve such good results and compare it to other deep learning and state-of-the-art methods.

System design tools are often only available as blackboxes with complex nonlinear relationships between inputs and outputs. Blackboxes typically run in the forward direction: for a given design as input they compute an output representing system behavior. Most cannot be run in reverse to produce an input from requirements on output. Thus, finding a design satisfying a requirement is often a trial-and-error process without assurance of optimality. Finding designs concurrently satisfying multiple requirements is harder because designs satisfying individual requirements may conflict with each other. Compounding the hardness are the facts that blackbox evaluations can be expensive and sometimes fail to produce an output due to non-convergence of underlying numerical algorithms. This paper presents CNMA (Constrained optimization with Neural networks, MILP solvers and Active Learning), a new optimization method for blackboxes. It is conservative in the number of blackbox evaluations. Any designs it finds are guaranteed to satisfy all requirements. It is resilient to the failure of blackboxes to compute outputs. It tries to sample only the part of the design space relevant to solving the design problem, leveraging the power of neural networks, MILPs, and a new learning-from-failure feedback loop. The paper also presents parallel CNMA that improves the efficiency and quality of solutions over the sequential version, and tries to steer it away from local optima. CNMA's performance is evaluated for seven nonlinear design problems of 8 (2 problems), 10, 15, 36 and 60 real-valued dimensions and one with 186 binary dimensions. It is shown that CNMA improves the performance of stable, off-the-shelf implementations of Bayesian Optimization and Nelder Mead and Random Search by 1%-87% for a given fixed time and function evaluation budget. Note, that these implementations did not always return solutions.

Recent neural-based relation extraction approaches, though achieving promising improvement on benchmark datasets, have reported their vulnerability towards adversarial attacks. Thus far, efforts mostly focused on generating adversarial samples or defending adversarial attacks, but little is known about the difference between normal and adversarial samples. In this work, we take the first step to leverage the salience-based method to analyze those adversarial samples. We observe that salience tokens have a direct correlation with adversarial perturbations. We further find the adversarial perturbations are either those tokens not existing in the training set or superficial cues associated with relation labels. To some extent, our approach unveils the characters against adversarial samples. We release an open-source testbed, "DiagnoseAdv".

NASA's Ingenuity helicopter has managed to spin its blades to 50 revolutions per minute (RPM) in preparation for its maiden flight on Mars this weekend. The space agency shared a short animation, captured by cameras attached to Ingenuity's parent craft Perseverance, of the blades rotating. Takeoff of the 4-pound (1.8-kilogram) robotic helicopter, already detached from the Perseverance rover, is now slated for this Sunday (April 11). If successful, Ingenuity, which has become affectionately known as'Ginny', will be the first powered and controlled flight of an aircraft on any planet other than Earth. Ingenuity carries a small amount of fabric that covered one of the wings of the Wright brothers' aircraft, known as the Flyer, during the first powered, controlled flight on Earth in 1903.

The French army is the latest buyer of Boston Dynamics' robot dog Spot, which it's using for training in combat scenarios. Images have been shared by France's military school, the Saint-Cyr, of Spot with soldiers during military exercises. The military school said Spot, and the'robotisation of the battlefield', is helping'raising students' awareness of the challenges of tomorrow'. Spot, which is suited for indoor or outdoor use, can map its environment, sense and avoid obstacles, climb stairs and open doors. It can undertake hazardous tasks in a variety of inhospitable environments such as nuclear plants, offshore oil fields and construction sites.

Root AI, a Somerville, Mass.-based startup developing the Virgo harvesting robot for indoor farms, was acquired by AppHarvest for $60 million. AppHarvest is investing approximately $10 million in cash and the remaining balance in AppHarvest common shares to acquire Root AI. Founded in 2018, Root AI's 19 full-time employees are expected to join AppHarvest's technology group. Root AI co-founder and CEO Josh Lessing will take on the role of CTO for AppHarvest. He will take the lead in continuing to develop the robots and AI capabilities for the network of indoor farms AppHarvest is building.

The state of autonomous vehicle safety standard regulation in the US today is between two presidential administrations, with the Trump Administration-era regulations issued Jan. 14 likely to be soon superseded by policies of the Biden Administration. The Trump Administration rules would allow self-driving vehicle manufacturers to skip certain federal crash safety requirements in vehicles not designed to carry people, marking the first major update to federal safety standards to accommodate innovations of driverless technology, according to an account in The Detroit News.This would apply for example to the delivery vehicle from startup Nuro, which has no driver or passengers. The National Highway Traffic Safety Administration estimated the rule would save automakers and consumers $5.8 billion in 2050. "With more than 90% of serious crashes caused by driver error, it's vital that we remove unnecessary barriers to technology that could help save lives," stated then NHTSA Deputy Administrator James Owens. On Jan. 25, Steve Cliff, deputy executive officer of the California Air Resources Board, was named deputy administrator of the NHTSA. Ariel Wolf, counsel to the Self-Driving Coalition, said of the Jan. 14 announcement that the NHTSA rule was a "highly significant" development in safety rules for self-driving vehicles.

THE COFFEESHOP is an engine of social mobility. Barista jobs require soft skills and little experience, making them a first port of call for young people and immigrants looking for work. So it may be worrying that robotic baristas are spreading. RC Coffee, which bills itself "Canada's first robotic café", opened in Toronto last summer. "[T]he barista-to-customer interaction is somewhat risky despite people's best efforts to maintain a safe environment," the firm says.