The technology available to water quality management applications needs to be advanced due to greater use of automation to increase ease of operation, support remote operation and reduce risks due to operator error. In this case study, a comparison is made between System-Theoretic Process Analysis (STPA) and the Bow-tie methodology for identifying process hazards and countermeasures which can be used to guide the design and testing of an automated water quality management system (AWQMS). For this study, the application considered is a small hydroponics installation where water quality management has been automated. The STPA methodology uses a system theory-based approach to identify hazards, which include operational failures, human errors, and component interactions. The Bow-tie diagram focuses on individual barriers for a given threat which can prevent the realisation of a hazardous event and unwanted consequences. Thus, the 22 preventative barriers and seven recovery barriers identified through the Bow-tie diagram provide the design process with broad requirements for reducing the risks of user error as well as the ones associated with ongoing operations. The STPA method identified many Causal Factors (CF) generated from the Unsafe Control Actions after considering all the feasible scenarios. For design input, the STPA provided the design process with 204 specific CFs which were used to create 94 countermeasures to be included in software and hardware design as well as user information material. Both methods identified useful measures to control the hazards associated with human interaction with the AWQMS. However, the measures differed in the level of detail and the involvement in the evolution in the final system losses. In this study, the STPA process was able to identify several hazards which did not visibly relate to the Bow-tie barriers. However, the Bow-tie diagram illustrates a distinction between preventative and recovery hazard controls.

With the development of the Internet, user comments produced an unprecedented impact on information acquisition, goods purchase, and other aspects. For example, the user comments can quickly render a topic the focus of discussion in social networks. It can promote the sales of goods in e-commerce, and it influences the ratings of books, movies, or albums. Among these network applications and services, “astroturfing,” a kind of online suspicious behavior, can generate abnormal, damaging, and even illegal behaviors in cyberspace that mislead public perception and bring a bad effect on Internet users and society. Hence, the manner of detecting and combating astroturfing behavior has become highly urgent, attracting interest from researchers both from information technology and sociology. In the current paper, we restudy it mainly from the perspective of information technology, summarize the latest research findings of astroturfing detection, analyze the astroturfing feature, classify the machine learning-based detection methods and evaluation criteria, and introduce the main applications. Different from the previous surveys, we also discuss the new future directions of astroturfing detection, such as cross-domain astroturfing detection and user privacy protection.

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Inefficient signal control will not only exaggerate traffic congestion, but also increase the fuel consumption and exhaust emissions. Thus, signal planning is highly important in green transportation. As the Connected vehicle (CV) technology has transformed today's transportation systems by connecting vehicles and the transportation infrastructure through wireless communication, the CV-based signal control system has seen significant studies recently. Unfortunately, existing signal planning algorithms in use are developed for the signal-intersection, showing low traffic efficiency in the multi-intersection collaborative planning due to ignoring the traffic correlation among the neighboring intersections. In this work, we target the USDOT (U.S. Department of Transportation) sponsored CVbased traffic control system, and implement a multi-intersection traffic network. We model the multi-intersection collaborative signal planning problem as a multi-agent reinforcement learning problem, and present an actor-attention-critic algorithm to improve transportation efficiency and energy efficiency in green transportation, as well as resist congestion attack. Experiment results on the multi-intersection traffic network indicates that 1) compared to the baseline, our approach reduces the total delay by as high as 44.24%; 2) our method transports more vehicles passing the intersections meanwhile reduces the total CO 2 emissions by 2.40%; 3) under the congestion attack, our approach shows robustness and reduces the total delay by as high as 64.33%.

Rewards are critical hyperparameters in reinforcement learning (RL), since in most cases different reward values will lead to greatly different performance. Due to their commercial value, RL rewards become the target of reverse engineering by the inverse reinforcement learning (IRL) algorithm family. Existing efforts typically utilize two metrics to measure the IRL performance: the expected value difference and the mean reward loss, which we call them EVD and MRL respectively. Unfortunately, in some cases, EVD and MRL can give completely opposite results, due to MRL focusing on whole state-space rewards while EVD only considering partly sampled rewards. Such situation naturally rises to one fundamental question: whether current metrics and assessment are sufficient and accurate for more general use. Thus, in this paper, based on the metric called normalized mutual information of reward clusters (C-NMI) we propose a novel IRL assessment; we aim to fill this research gap by considering a middle-granularity state space between the entire state space and the specific sampling space. We utilize the agglomerative nesting algorithm (AGNES) to control dynamical C-NMI computing via a 4-order tensor model with injected manipulated trajectories. With such a model, we can uniformly capture different-dimension values of MRL, EVD, and C-NMI, and perform more comprehensive and accurate assessment and analyses. Extensive experiments on several mainstream IRLs are experimented in Object World, hence revealing that the assessing accuracy of our method increases 110.13% and 116.59% respectively when compared with the EVD and MRL. Meanwhile, C-NMI is more robust than EVD and MRL under different demonstrations.Impact Statement-In this work, we pay attention to the inconformity problem of MRL-and EVD-based IRL assessment. There are two main challenges for us to address: (1) how to design a novel metric by combining the advantages of both MRL and EVD, and (2) how to construct a comprehensive assessment method for accurate comparison and analysis. To address such challenges, we craft a novel assessment of IRL based on the metric called normalized mutual information of reward clusters (C-NMI). Hence we attempt to fill the existing research gap by considering a middle-granularity state space between the entire state space and the certain sampled space. We list all of the notation and parameters used in the rest of this paper in Table I.