Satisfaction of the condition of order preservation: A simulation study

The paper presents the results of the second stage of research on business models of language schools. It was assumed that there is a significant difference in the value propositions of schools and the expectations of their clients. An examination procedure was planned with the use of a questionnaire and statistical analysis such as factor analysis, on its basis. Respondents consisted of a group of school managers (representing the majority of enterprises in Lower Silesia) on the one hand and, on the other hand, a large group of former and current clients. The results of the research confirm the existence of a gap in the perception of the values of both groups. The analysis has been conducted in the convention of the business model canvas template. The distinctness of the offer’s perception is presented in the form of activity packages, responsible for creating value for the clients (the right side of the model canvas). The structure of the packages, as a picture of the gap, is discussed. The results of the first stage of the research, diagnosing the influence of the surrounding elements on the business models of language schools, are also referred to. Directional changes in the business models that result from both stages of the research are suggested. The strategic dimension of the gap results from the strategy-business model relation. In light of the literature review, it may be supposed that the research is unique due to the segment of subjects and research methodology.

Relation Extraction Using Supervision from Topic Knowledge of Relation Labels

Explicitly exploring the semantics of a relation is significant for high-accuracy relation extraction, which is, however, not fully studied in previous work. In this paper, we mine the topic knowledge of a relation to explicitly represent the semantics of this relation, and model relation extraction as a matching problem. That is, the matching score between a sentence and a candidate relation is predicted for an entity pair. To this end, we propose a deep matching network to precisely model the semantic similarity between a sentence-relation pair. Besides, the topic knowledge also allows us to derive the importance information of samples as well as two knowledge-guided negative sampling strategies in the training process. We conduct extensive experiments to evaluate the proposed framework and observe improvements in AUC of 11.5% and max F1 of 5.4% over the baselines with state-of-the-art performance.

Exact Modeling of Binary Decision Diagram Complexity

An exact model of Binary Decision Diagram (BDD) complexity is derived in this paper. An estimate of BDD complexity using models has already been proposed. The estimated BDD complexity in previous papers is just an approximation of experimental data using a curve that fits the data. Mathematical analysis indicates that many curves can be fitted to the same data. This paper derives the exact model for the BDD complexity by analysis. In order to derive the exact model, relation between the SOP (Sum Of Product) terms and Min-terms was analyzed. Further the variation of BDD size for various Min-terms was also analyzed. Finally the plot between the experimental BDD size and the derived exact model proves the accuracy of the derived model.

Multi-Task Clustering with Model Relation Learning

Multi-task clustering improves the clustering performance of each task by transferring knowledge among the related tasks. An important aspect of multi-task clustering is to assess the task relatedness. However, to our knowledge, only two previous works have assessed the task relatedness, but they both have limitations. In this paper, we propose a multi-task clustering with model relation learning (MTCMRL) method, which automatically learns the model parameter relatedness between each pair of tasks. The objective function of MTCMRL consists of two parts: (1) within-task clustering: clustering each task by introducing linear regression model into symmetric nonnegative matrix factorization; (2) cross-task relatedness learning: updating the parameter of the linear regression model in each task by learning the model parameter relatedness between the clusters in each pair of tasks. We present an effective alternating algorithm to solve the non-convex optimization problem. Experimental results show the superiority of the proposed method over traditional single-task clustering methods and existing multi-task clustering methods.

Exploring Encoder-Decoder Model for Distant Supervised Relation Extraction

In this paper, we present an encoder-decoder model for distant supervised relation extraction. Given an entity pair and its sentence bag as input, in the encoder component, we employ the convolutional neural network to extract the features of the sentences in the sentence bag and merge them into a bag representation. In the decoder component, we utilize the long short-term memory network to model relation dependencies and predict the target relations in a sequential manner. In particular, to enable the sequential prediction of relations, we introduce a measure to quantify the amounts of information the relations take in their sentence bag, and use such information to determine the order of the relations of a sentence bag during model training. Moreover, we incorporate the attention mechanism into our model to dynamically adjust the bag representation to reduce the impact of sentences whose corresponding relations have been predicted. Extensive experiments on a popular dataset show that our model achieves significant improvement over state-of-the-art methods.

Analysis of motion of the three wheeled mobile platform

The work is dedicated to the designing motion of the three wheeled mobile platform under the unsteady conditions. In this paper the results of the analysis based on the dynamics model of the three wheeled mobile robot, with two rear wheels and one front wheel has been included The prototype has been developed by the author's construction assumptions that is useful to realize the motion of the platform in a various configurations of wheel drives, including control of the active forces and the direction of their settings while driving. Friction forces, in longitudinal and in the transverse directions, are considered in the proposed model. Relation between friction and active forces are also included. The motion parameters of the mobile platform has been determined by adopting classical approach of mechanics. The formulated initial problem of platform motion has been solved numerically using the Runge-Kutta method of the fourth order. Results of motion analysis with motion parameters values are determined and sample results are presented.