Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning

Academic networks in the real world can usually be portrayed as heterogeneous information networks (HINs) with multi-type, universally connected nodes and multi-relationships. Some existing studies for the representation learning of homogeneous information networks cannot be applicable to heterogeneous information networks because of the lack of ability to issue heterogeneity. At the same time, data has become a factor of production, playing an increasingly important role. Due to the closeness and blocking of businesses among different enterprises, there is a serious phenomenon of data islands. To solve the above challenges, aiming at the data information of scientific research teams closely related to science and technology, we proposed an academic heterogeneous information network embedding representation learning method based on federated learning (FedAHE), which utilizes node attention and meta path attention mechanism to learn low-dimensional, dense and real-valued vector representations while preserving the rich topological information and meta-path-based semantic information of nodes in network. Moreover, we combined federated learning with the representation learning of HINs composed of scientific research teams and put forward a federal training mechanism based on dynamic weighted aggregation of parameters (FedDWA) to optimize the node embeddings of HINs. Through sufficient experiments, the efficiency, accuracy and feasibility of our proposed framework are demonstrated

Theoretical and experimental study on the dynamic characteristics of an axially moving nested clamped-hinged beam

The present paper is concerned with the transverse vibration properties of an axially moving nested clamped-hinged beam, which can be regarded as a stepped beam. The transverse vibration equation for the axially moving nested clamped-hinged beam is derived by D’Alembert’s principle. The modified Galerkin’s method, which uses the instantaneous modal function of the clamped-hinged stepped beam as a trial function, is used to solve the vibration equation. An axially moving nested clamped-hinged beam model is designed for the vibration test. The theoretical model is updated by calculating the flexural rigidity values of the first segment of the nested beam based on the measured first-order vibration frequencies, which are tested for different lengths in the main beam. The first order decay coefficients are identified by the logarithmic decrement method. Then, the functional relationship between the flexural rigidity and beam length, as well as the decay coefficient and beam length, is established using the polynomial fitting method. The calculated responses of the modified model agree well with the experimental results, which verifies the correctness of the proposed calculation model and indicates the effectiveness of the methods of model updating and damping determination. The theoretical and experimental results demonstrate that the change law of the frequency with the main beam length increasing is a low-high-low-high trend. Further investigations into the non-damping free vibration properties of the nested clamped-hinged beam during extension and retraction of the main beam are performed. It is determined that there is no obvious change of the dynamic response amplitude of the nested structure during different axial moving rates in the main beam. Furthermore, as the length of the main beam increases, the vibration displacement decreases gradually, and the total mechanical energy increases constantly; therefore, the extension movement of the main beam becomes unstable. Moreover, the numerical results indicate that the non-damping free vibration characteristics of the nested clamped-hinged beam during extension and retraction of the main beam are inversely related

An Experimental Study on Attribute Validity of Code Quality Evaluation Model

Regarding the practicality of the quality evaluation model, the lack of quantitative experimental evaluation affects the effective use of the quality model, and also a lack of effective guidance for choosing the model. Aiming at this problem, based on the sensitivity of the quality evaluation model to code defects, a machine learning-based quality evaluation attribute validity verification method is proposed. This method conducts comparative experiments by controlling variables. First, extract the basic metric elements; then, convert them into quality attributes of the software; finally, to verify the quality evaluation model and the effectiveness of medium quality attributes, this paper compares machine learning methods based on quality attributes with those based on text features, and conducts experimental evaluation in two data sets. The result shows that the effectiveness of quality attributes under control variables is better, and leads by 15% in AdaBoostClassifier; when the text feature extraction method is increased to 50 - 150 dimensions, the performance of the text feature in the four machine learning algorithms overtakes the quality attributes; but when the peak is reached, quality attributes are more stable. This also provides a direction for the optimization of the quality model and the use of quality assessment in different situations