Abstract. Re-identification of individuals across camera networks with limited or no overlapping fields of view remains challenging in spite of significant research efforts. In this paper, we propose the use, and extensively evaluate the performance, of four alternatives for re-ID classification: regularized Pairwise Constrained Component Analysis, kernel Local Fisher Discriminant Analysis, Marginal Fisher Analysis and a ranking ensemble voting scheme, used in conjunction with different sizes of sets of histogram-based features and linear, χ 2 and RBF-χ 2 kernels. Comparisons against the state-of-art show significant improvements in performance measured both in terms of Cumulative Match Characteristic curves (CMC) and Proportion of Uncertainty Removed (PUR) scores on the challenging VIPeR, iLIDS, CAVIAR and 3DPeS datasets.

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Industrial internet of unmanned aerial vehicles (IIoUAVs) which enable autonomous inspection and measurement of anything anytime anywhere have become an essential component of the future industrial internet of things (IIoT) ecosystem. In this paper, we investigate how to apply IIoUAVs for power line inspection in smart grid from an energy efficiency perspective. Firstly, the energy consumption minimization problem is formulated as a joint optimization problem, which involves both the large-timescale optimization such as trajectory scheduling, velocity control, and frequency regulation, and the small-timescale optimization such as relay selection and power allocation. Then, the original NP-hard problem is transformed into a two-stage suboptimal problem by exploring the timescale difference and the energy magnitude difference between the large-timescale and the small-timescale optimizations, and is solved by combining dynamic programming (DP), auction theory and matching theory. Finally, the proposed algorithm is verified based on real-world map and realistic power grid topology.

Opinion exchange models aim to describe the process of public opinion formation, seeking to uncover the intrinsic mechanism in social systems; however, the model results are seldom empirically justified using large-scale actual data. Online social media provide an abundance of data on opinion interaction, but the question of whether opinion models are suitable for characterizing opinion formation on social media still requires exploration. We collect a large amount of user interaction information from an actual social network, i.e., Twitter, and analyze the dynamic sentiments of users about different topics to investigate realistic opinion evolution. We find two nontrivial results from these data. First, public opinion often evolves to an ordered state in which one opinion predominates, but not to complete consensus. Second, agents are reluctant to change their opinions, and the distribution of the number of individual opinion changes follows a power law. Then, we suggest a model in which agents take external actions to express their internal opinions according to their activity. Conversely, individual actions can influence the activity and opinions of neighbors. The probability that an agent changes its opinion depends nonlinearly on the fraction of opponents who have taken an action. Simulation results show user action patterns and the evolution of public opinion in the model coincide with the empirical data. For different nonlinear parameters, the system may approach different regimes. A large decay in individual activity slows down the dynamics, but causes more ordering in the system. Opinion dynamics tries to describe the process of public opinion formation in social systems. Many opinion models have been presented that explore how the local individual behavior affects collective phenomena. However, those model results are seldom empirically justified using large-scale actual data, and whether traditional opinion models suitably describe online opinion interactions requires further exploration. We analyze users' opinions regarding a certain topic using large-scale actual data collected from a famous social network, i.e., Twitter, and discover two nontrivial results: first, consensus is difficult to achieve in a finite time and second, users seldom change their opinions, and the number of individual opinion changes decays as a power law. We present a discrete opinion model including agents' internal opinions and external actions that are determined by agents' activity. Agents' activity also evolves during the dynamics. Simulation results show our model can retrieve similar properties to those of actual data. We hope theoretical opinion models will be verified by actual data in different social systems so that they can better characterize actual social interactions. In the future, whether opinion models can predict the evolutionary trend of public opinion in actual situation will be investigated. This study will improve the applicability of research on opinion dynamics.

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