Interactive group decision making procedure based on uncertain multiplicative linguistic preference relations

Group decision making problems are investigated with uncertain multiplicative linguistic preference relations.An unbalanced multiplicative linguistic label set is introduced,which can be used by the experts to express their linguistic preference information over alternatives.The uncertain linguistic weighted geometric mean operator is utilized to aggregate all the individual uncertain multiplicative linguistic preference relations into a collective one,and then a simple approach is developed to determine the experts＇ weights by utilizing the consensus degrees among the individual uncertain multiplicative linguistic preference relations and the collective uncertain multiplicative linguistic preference relations.Furthermore,a practical interactive procedure for group decision making is proposed based on uncertain multiplicative linguistic preference relations,in which a possibility degree formula and a complementary matrix are used to rank the given alternatives.Finally,the proposed procedure is applied to solve the group decision making problem of a manufacturing company searching the best global supplier for one of its most critical parts used in assembling process.

Three Heads Better than One:Pure Entity,Relation Label and Adversarial Training for Cross-domain Few-shot Relation Extraction

In this paper,we study cross-domain relation extraction.Since new data mapping to feature spaces always differs from the previously seen data due to a domain shif,few-shot relation extraction often perform poorly.To solve the problems caused by cross-domain,we propose a method for combining the pure entity,relation labels and adversarial(PERLA).We first use entities and complete sentences for separate encoding to obtain context-independent entity features.Then,we combine relation labels which are useful for relation extraction to mitigate context noise.We combine adversarial to reduce the noise caused by cross-domain.We conducted experiments on the publicly available cross-domain relation extraction dataset Fewrel 2.o[1]o,and the results show that our approach improves accuracy and has better transferability for better adaptation to cross-domain tasks.

A Relation Matrix Approach to Labelling Temporal Relations in Scheduling

In this paper,we present a relation matrix description of temporal relations.Based on this modela new algorithm for labelling temporal relations is proposed.Under certain conditions the algorithm iscompleted and has a polynomial complexity.In general cases it is still an efficient algorithm comparedto some known algorithms.