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The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. This paper proposes a consensus-trust driven framework of bidirectional interaction for social network large-group decision making. Firstly, the concepts of interaction consensus threshold and interaction trust threshold are defined, which are used to discriminate the interaction modes between subgroups into four categories. Corresponding hybrid feedback strategies are designed in which the consensus level and trust level of subgroups are regarded as reliable resources to facilitate the achievement of group consensus. Secondly, a minimum adjustment bidirectional feedback model considering cohesion is developed to help the interacting subgroups reach mutual consensus with minimum opinion modification. Finally, the proposed consensus framework is applied to a blockchain platform selection problem in supply chain to demonstrate the effectiveness and applicability of the model.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. This paper proposes a decentralized feedback mechanism to help large-scale decision makers (DMs) reach consensus considering the limited compromise behavior of subgroups. First, a novel decentralized opinion interaction mechanism is designed to identify the most inconsistent subgroups pair in the group and make them interact with each other. Then it is different from the centralized interaction mechanism, which adopts the aggregation of the opinions of other subgroups. The former is suitable for situations with low initial consensus level while the later is for high consensus level. Secondly, the compromise behavior of subgroups in the feedback process is explored, and then the concept of ‘compromise threshold’ (CT) is defined to analyze the limited compromise behavior of subgroups. Finally, an illustrative example regarding the smart logistics supplier selection (SLEC) is described to demonstrate the effectiveness and advantages of the proposed mechanism.

Multi-dimensional time series are a series of data recorded by multiple variables in the order of time. Anomaly detection refers to the detection of data that do not conform to normal expectations. Existing multi dimensional time series anomaly detection methods do not well distinguish the anomalies in the data, either in the effectiveness of the algorithm, or in the time cost. Based on the research at home and abroad, an algorithm with both algorithm effectiveness and reducing time cost advantages: Temporal Deep Residual Shrinkage Networks, TDRSN. Combining Time Convolutional Network (TCN) with Deep Residual Shrinkage Network (DRSN), adopt dilated causal convolution to expand the receptive field, introduce the soft threshold of self attention, enabling it to not only remember historical data, reduce the impact of gradient vanishing and gradient exploding, but also automatically set thresholds without expert experience, eliminate redundant features and reduce the influence of unimportant features. The experimental results show that TDRSN has a better anomaly detection effect than TCN, DRSN and other classical time series anomaly detection models.