Structural change detection in ordinal time series

Change-point detection in health care data has recently obtained considerable attention due to the increased availability of complex data in real-time. In many applications, the observed data is an ordinal time series. Two kinds of test statistics are proposed to detect the structural change of cumulative logistic regression model, which is often used in applications for the analysis of ordinal time series. One is the standardized efficient score vector, the other one is the quadratic form of the efficient score vector with a weight function. Under the null hypothesis, we derive the asymptotic distribution of the two test statistics, and prove the consistency under the alternative hypothesis. We also study the consistency of the change-point estimator, and a binary segmentation procedure is suggested for estimating the locations of possible multiple change-points. Simulation results show that the former statistic performs better when the change-point occurs at the centre of the data, but the latter is preferable when the change-point occurs at the beginning or end of the data. Furthermore, the former statistic could find the reason for rejecting the null hypothesis. Finally, we apply the two test statistics to a group of sleep data, the results show that there exists a structural change in the data.

Estimating the Covariance Matrix of the Maximum Likelihood Estimator Under Linear Cluster-Weighted Models

In recent years, the research into cluster-weighted models has been intense. However, estimating the covariance matrix of the maximum likelihood estimator under a cluster-weighted model is still an open issue. Here, an approach is developed in which information-based estimators of such a covariance matrix are obtained from the incomplete data log-likelihood of the multivariate Gaussian linear cluster-weighted model. To this end, analytical expressions for the score vector and Hessian matrix are provided. Three estimators of the asymptotic covariance matrix of the maximum likelihood estimator, based on the score vector and Hessian matrix, are introduced. The performances of these estimators are numerically evaluated using simulated datasets in comparison with a bootstrap-based estimator; their usefulness is illustrated through a study aiming at evaluating the link between tourism flows and attendance at museums and monuments in two Italian regions.

Assessment of Item and Test parameters: Cosine Similarity Approach

The paper proposes new measures of difficulty and discriminating values of binary items and test consisting of such items and find their relationships including estimation of test error variance and thereby the test reliability, as per definition using cosine similarities. The measures use entire data. Difficulty value of test and item is defined as function of cosine of the angle between the observed score vector and the maximum possible score vector. Discriminating value of test and an item are taken as coefficient of variation (CV) of test score and item score respectively. Each ranges between 0 and 1 like difficulty value of test and an item. With increase in number of correct answer to an item, item difficulty curve increases and item discriminating curve decreases. The point of intersection of the two curves can be used for item deletion along with other criteria. Cronbach alpha was expressed and computed in terms of discriminating value of test and item. Relationship derived between test discriminating value and test reliability as per theoretical definition. Empirical verifications of proposed measures were undertaken. Future studies suggested.re to enter text.

Quantized Cooperative Spectrum Sensing in Bandwidth-Constrained Cognitive V2X Based on Deep Learning

The output of the network in a deep learning (DL) based single-user signal detector, which is a normalized 2 × 1 class score vector, needs to be transmitted to the fusion center (FC) by occupying a large amount of the communication channel (CCH) bandwidth in the cooperative spectrum sensing (CSS). Obviously, in cognitive radio for vehicle to everything (CR-V2X), it is particularly important to propose a method that makes full use of the bandwidth-constrained CCH to obtain the optimal detection performance. In this paper, we firstly propose a novel single-user spectrum sensing method based on modified-ResNeXt in CR-V2X. The simulation results show that our proposed method performs better than two advanced DL based spectrum sensing methods with shorter inference time. We then introduce a quantization-based cooperative spectrum sensing (QBCSS) algorithm based on DL in CR-V2X, and the impact of the number of reported bits on the sensing results is also discussed. Through the experimental results, we conclude that the QBCSS algorithm reaches the optimal detection performance when the number of bits for quantizing local sensing data is 4. Finally, according to the conclusion, a bandwidth-constrained QBCSS scheme based on DL is proposed to make full use of the CCH with limited capacity to achieve the optimal detection performance.

An Online Arrangement Method of Difficult Actions in Competitive Aerobics Based on Multimedia Technology

In accordance with the development trend of competitive aerobics’ arrangement structure, this paper studies the online arrangement method of difficult actions in competitive aerobics based on multimedia technology to improve the arrangement effect. RGB image, optical flow image, and corrected optical flow image are taken as the input modes of difficult action recognition network in competitive aerobics video based on top-down feature fusion. The key frames of input modes in competitive aerobics video are extracted by using the key frame extraction method based on subshot segmentation of a double-threshold sliding window and fully connected graph. Through forward propagation, the score vector of video relative to all categories is obtained, and the probability score of probability distribution is obtained after normalization. The human action recognition in competitive aerobics video is completed, and the online arrangement of difficult action in competitive aerobics is realized based on this. The experimental results show that this method has a high accuracy in identifying difficult actions in competitive aerobics video; the online arrangement of difficult actions in competitive aerobics has obvious advantages, meets the needs of users, and has strong practicability.

Identification of unbalanced bids based on grey-fuzzy evaluation method

To provide theoretical reference for owners to identify unbalanced bids, this paper aims to construct an identification method based on grey relational and fuzzy set theory. Firstly, to measure the closeness degree between bidding unit price from engineering’s estimated price, grey relational analysis theory is used to express the relationship between them. Secondly, a combined weight method determining all line items is calculated through integrating analytic hierarchy model and maximizing deviation method. Thirdly, based on fuzzy set theory, the membership degree and the fuzzy relation matrix are constructed, and then a fuzzy comprehensive identification method is established to identify unbalanced bidding. Fourthly, on the basis of fuzzy comprehensive identification method, the scoring set and total score vector are designed, and the rank of unbalanced bids is obtained by total score vector. Finally, a practical construction project bidding is stated to illustrate the effectiveness and practicability of the proposed method.

Mining Hot-Personae Approach Based on Local Social Microblog Graph

With the increasing popularity of online social media platforms, netizens always chat with their friends and share information, such as what they like in their daily lives, on these platforms. Netizens publish tons of information on social platforms every day. These platforms converge many people and information. The processes by which the publishers find the sharers who are interested in their publications and the sharers find some interesting things and information in what the publishers published have resulted in the challenge of retrieving information from social network fields. To address these issues, we propose a novel algorithm, named Hot Persona Mining, to analyze the users' focus personae from microblog posts in the online social networks. During mining, we first utilize local-based graph clustering to establish the nearest neighbor nodes of target users. Then, we mine users' focused personae entities from their neighbors' published microblog posts in different periods. Then, we construct the users' active score vector and their interest matrix to mine the hot personae in every local social graph. The experimental results show that our algorithm effectively mines current focus of the target user, and exhibits good performance as shown by its precision, recall and F-measures.

Do speech registers differ in the predictability of words?

Previous research has demonstrated that language use can vary depending on the context of situation. The present paper extends this finding by comparing word predictability differences between 14 speech registers ranging from highly informal conversations to read-aloud books. We trained 14 statistical language models to compute register-specific word predictability and trained a register classifier on the perplexity score vector of the language models. The classifier distinguishes perfectly between samples from all speech registers and this result generalizes to unseen materials. We show that differences in vocabulary and sentence length cannot explain the speech register classifier’s performance. The combined results show that speech registers differ in word predictability.