Social Network Group Decision-Making Method Based on Q-Rung Orthopair Fuzzy Set and Its Application in the Evaluation of Online Teaching Quality

As q-rung orthopair fuzzy set (q-ROFS) theory can effectively express complex fuzzy information, this study explores its application to social network environments and proposes a social network group decision-making (SNGDM) method based on the q-ROFS. Firstly, the q-rung orthopair fuzzy value is used to represent the trust relationships between experts in the social network, and a trust q-rung orthopair fuzzy value is defined. Secondly, considering the decreasing and multipath of trust in the process of trust propagation, this study designs a trust propagation mechanism by using its multiplication operation in the q-ROFS environment and proposes a trust q-ROFS aggregation approach. Moreover, based on the trust scores and confidence levels of experts, a new integration operator called q-rung orthopair fuzzy-induced ordered weighted average operator is proposed to fuse experts’ evaluation information. Additionally, considering the impact of consensus interaction on decision-making results, a consensus interaction model based on the q-ROF distance measure and trust relationship is proposed, including consistency measurement, identification of inconsistent expert decision-making opinions and a personalized adjustment mechanism. Finally, the SNGDM method is applied to solve the problem of evaluating online teaching quality.

Visualization of preference aggregation based on Weber point in social network group decision-making problem

PurposeThis study aims to construct a Weber point-based model to complete the visualization of preference aggregation in group decision-making problem, in which decision-makers are associated with trust relationship.Design/methodology/approachThis study mainly comprises four parts: trust propagation, preference aggregation, opinion adjustment and alternative selection. Firstly, the incomplete trust between decision-makers is completed with trust transfer operators and propagation probability in trust propagation process. Secondly, a preference aggregation model based on Weber point is proposed to aggregate the group preference visually. Thirdly, opinions are adjusted to reach a consensus. Finally, the ranking of alternatives is determined by the correlation coefficient with the group preference as a reference.FindingsThe Weber point-based model proposed in this study can minimize the gap in the preference of alternatives between the group and all decision-makers, and realize the visualization of aggregation result. A case of plan selection is introduced to illustrate the feasibility and effectiveness of the proposed model.Originality/valueBy comparing the result with the weighted average-based preference aggregation method, the Weber point-based model proposed in this study can show the result of preference aggregation intuitively and improve group consensus.

STOCHASTIC DECODING OF LINEAR BLOCK CODES USING CHECK MATRIX

Для итеративного декодирования на графах используется новый альтернативный подход - это стохастическое декодирование. Возможность стохастического декодирования была недавно предложена для декодирования LDPC-кодов. Эта статья расширяет применение стохастического подхода для декодирования линейных блочных кодов с помощью проверочных матриц (PCM), таких как коды Боуза - Чоудхури - Хоквингема (BCH), коды Рида - Соломона (RS) и блочные турбокоды на основе компонентов кодов BCH. Показано, как стохастический подход способен генерировать информацию мягкого выхода для итеративного декодирования с мягким входом и мягким выходом Soft - Input Soft - Output (SISO). Описывается структура стохастических переменных узлов высокой степени, используемых в кодах с помощью проверочных матриц PCM. Результаты моделирования для кода BCH (128, 120), кода RS (31, 25) и RS (63, 55) и турбокода блока BCH (256, 121) и (1024, 676) демонстрируют эффективность декодирования при закрытии к итеративному декодеру SISO с реализацией с плавающей запятой. Эти результаты показывают производительность декодирования, близкую к адаптивному алгоритму распространения доверия и/или турбо-ориентированному адаптированному алгоритму распространения доверия Stochastic decoding capability has recently been proposed for decoding LDPC codes. This paper expands on the application of the stochastic approach to decoding linear block codes using parity check matrices (PCMs) such as Bose-Chowdhury-Hawkingham (BCH) codes, Reed-Solomon (RS) codes, and BCH component-based block turbo codes. We show how the stochastic approach is able to generate soft-output information for iterative decoding with soft-input and soft-output Soft-Input Soft-Output (SISO). We describe the structure of high degree stochastic node variables used in codes using PCM parity check matrices. Simulation results for BCH code (128, 120), RS code (31, 25) and RS (63, 55), and BCH block turbo code (256, 121) and (1024, 676) demonstrate the decoding efficiency on close to SISO iterative decoder with floating point implementation. These results show decoding performance close to the adaptive trust propagation algorithm and / or turbo-oriented adapted trust propagation algorithm