There are three key roles in Intelligent Transportation Systems (ITS): driver, vehicle and road. However, existing static interactions among Driver-Vehicle-Road (DVR) are too passive to reflect the change of driver preferences, vehicle conditions, road conditions, etc. Therefore, we provide a data-driven Cloud-Fog-Edge Collaborative Driver-Vehicle-Road (CFEC-DVR) framework. The framework could self-adaptively evolves through continuous iteration to provide better ITS services for humans. The collaboration among DVR creates a lot of relation data that construct our relation graphs. Cloud brings some privacy risks. Relation graphs have great analytic value. As DVR collaboration, privacy quality and analytic accuracy are three key issues in the framework, we propose a Relation Graph Privacy-Preserving scheme with High Accuracy in our framework, which is named as RGPP-HA. Based on machine learning, our method nearly maximizes the difficulty for attackers to know exactly how many other roles are connected to the attacked role, which enhances the privacy quality. Meanwhile, we find as much valuable information as possible from roles’ encrypted relations for more accurately analytic performance. Based on the experiments, we compare the proposed scheme RGPP-HA with existing classic and relevant schemes. The experimental results show that our scheme has the best privacy quality and analytic accuracy. This further verifies the feasibility of CFEC-DVR framework.

The development and applications of artificial intelligence (AI) have brought unprecedented opportunities to humans, but also brought many challenges and concerns such as unfairness, immorality, distrust, illegality, and discrimination. Responsible AI provides a new solution to effectively address these AI potential threats by integrating social/physical rules into AI systems. However, these rules are high-level regulations and ethical principles, which are difficult to be formalized. To this end, we attempt to use the data generated in various AI systems such as cyber–physical–social systems (CPSS) to discover and reflect these rules to provide more responsible services for humans. In this article, we first propose a data-driven responsible CPSS framework. Its core idea is to mine valuable rules through perception, fusion, processing, and analysis of CPSS data, and then use these rules to adaptively optimize CPSS. Based on this framework, three tensor-based couple hidden Markov models (T-CHMMs) are constructed to integrate three responsible features (i.e., timing, periodicity, and correlation) for mining potential and valuable rules. Then, the corresponding tensor-based Baum–Welch (TBW) algorithms are designed to solve their learning problems. Finally, the predictive accuracy and computational efficiency of the proposed models and algorithms are verified on three open datasets. The experimental results show that proposed methods have the best performances for various scenarios, which reflects that our methods are more promising and responsible than existing methods.

Most of existing methods do not consider the influence of expression and tone on barrage sentiment analysis. This decreases the effect and accuracy of barrage sentiment analysis. Therefore, we propose a barrage sentiment analysis scheme based on expression and tone. First, we propose a new sentiment dictionary based on expression and tone to increase the effect of barrage sentiment analysis. Second, we propose a new calculation method of sentiment value based on expression and tone for barrage sentiment analysis to increase the accuracy. Meanwhile, we change a single threshold to a threshold range, to expand the scope of neutral barrages. Finally, the experimental results show that our scheme is more effective and practical than the existing methods in the scenario of barrage sentiment analysis.

Consumer Perceived Corporate Social Responsibility Authenticity (CPCSRA) belongs to the field of micro Corporate Social Responsibility (CSR) research. In general, understanding the formation mechanism of CPCSRA could make it better able to play its role in several ways. Firstly, most previous studies do not empirically consider a key factor, i.e., the consumer perceived senior managers’ involvement. We add this key factor into the independent variables of our formation mechanism. Secondly, most previous empirical research studies the relevant factors of consumer perceived CSR commitment as a whole. We study these relevant factors separately instead. Thirdly, we simultaneously choose the consumer perceived strategy-driven motive and consumer perceived value-driven motive as the mediating variables of our formation mechanism. Based on the above innovations, we comprehensively and systematically study the formation mechanism of CPCSRA. We use structural equation modeling to study the formation mechanism. For the convenience of data collection, our data are all from Chinese consumers. The study results show that three independent variables can directly affect CPCSRA. The three independent variables are consumer perceived level of invested resources, consumer perceived CSR efforts matching company and consumer perceived senior managers’ involvement. Consumer perceived senior managers’ involvement has the greatest effect. The two variables consumer perceived strategy-driven motive and consumer perceived value-driven motive play partial mediating effects on the relationship between independent variables and CPCSRA. Based on our empirical research, we put forward some feasible suggestions for company managers to increase company sustainability in market competition by promoting the formation of CPCSRA.

This paper addresses the limitations of existing theme mining algorithms in extracting user-preferred themes from time-location data for personalized recommendation. We propose a Time-Location Theme Mining algorithm based on R-tree and user Attention...