Controlling Class Layout for Deep Ordinal Classification via Constrained Proxies Learning

For deep ordinal classification, learning a well-structured feature space specific to ordinal classification is helpful to properly capture the ordinal nature among classes. Intuitively, when Euclidean distance metric is used, an ideal ordinal layout in feature space would be that the sample clusters are arranged in class order along a straight line in space. However, enforcing samples to conform to a specific layout in the feature space is a challenging problem. To address this problem, in this paper, we propose a novel Constrained Proxies Learning (CPL) method, which can learn a proxy for each ordinal class and then adjusts the global layout of classes by constraining these proxies. Specifically, we propose two kinds of strategies: hard layout constraint and soft layout constraint. The hard layout constraint is realized by directly controlling the generation of proxies to force them to be placed in a strict linear layout or semicircular layout (i.e., two instantiations of strict ordinal layout). The soft layout constraint is realized by constraining that the proxy layout should always produce unimodal proxy-to-proxies similarity distribution for each proxy (i.e., to be a relaxed ordinal layout). Experiments show that the proposed CPL method outperforms previous deep ordinal classification methods under the same setting of feature extractor.Comment: Accepted by AAAI 202

Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling

Few-shot sequence labeling aims to identify novel classes based on only a few labeled samples. Existing methods solve the data scarcity problem mainly by designing token-level or span-level labeling models based on metric learning. However, these methods are only trained at a single granularity (i.e., either token level or span level) and have some weaknesses of the corresponding granularity. In this paper, we first unify token and span level supervisions and propose a Consistent Dual Adaptive Prototypical (CDAP) network for few-shot sequence labeling. CDAP contains the token-level and span-level networks, jointly trained at different granularities. To align the outputs of two networks, we further propose a consistent loss to enable them to learn from each other. During the inference phase, we propose a consistent greedy inference algorithm that first adjusts the predicted probability and then greedily selects non-overlapping spans with maximum probability. Extensive experiments show that our model achieves new state-of-the-art results on three benchmark datasets.Comment: Accepted by ACM Transactions on Information System

Modelling Brittle Fractures with Finite Elements: A Time-independent Phase-field Model

The objective of this paper is to propose a 2-D time-independent phase-field model with validating its performance as well as applying it for simulating existing representative experiments. Firstly, the section of the literature review provides an overview of quasi-brittle material and brittle fracture behaviours, as well as the existing FE models from both discontinuous and continuous approaches for simulating fracture behaviours. Next, the governing equations of the proposed phase-field model are determined, which are based on traditional Griffith’s theory as well as a specific variational method evolved from that. The proposed model is implemented in Abaqus. In particular, the implementation is achieved by using the User Subroutine in order to take the phase-field into account. The proposed model is validated by simulating a pure-tension and a pure shear test. In this part, not only the effect of discretisation but also the effects of length parameter and energy release rate has been discussed, of which the latter effect is exclusive in phase-field method. Finally, the validated model is used for simulating two sets of existing experiments, including a mixed-mode test and a series of Brazilian disks test. The results in both validation and simulation part indicate that the proposed model can successfully simulate both crack initiation and propagation in these cases, and good qualitative agreement with theoretical or experimental results can be observed

Factors Affecting Users\u27 Intention to Donate: An Empirical Study From the Perspective of Information Interaction

Online donation is a way for users to browse projects on a website and make donations through electronic payment. Previous studies have focused on the impact of various external factors on donation performance, while few studies have paid attention to the role of information interaction in influencing user intentions. Uniting the signaling theory and elaboration likelihood model, we studied the information and personal factors that influence users\u27 donation intention and tested the moderating effect of personal factors. The results show that the quality of platform execution, the positive emotional narrative, helper’s high and personal engagement have a significant positive impact on users’ intention to donate; at the same time, personal factors also reveal varying degrees of moderating effects. This paper makes up for the shortcomings of relevant empirical research and provides decision-making suggestions for platforms and users, which may increase the intention of the entire user group to donate

Learning to Classify Open Intent via Soft Labeling and Manifold Mixup

Open intent classification is a practical yet challenging task in dialogue systems. Its objective is to accurately classify samples of known intents while at the same time detecting those of open (unknown) intents. Existing methods usually use outlier detection algorithms combined with K-class classifier to detect open intents, where K represents the class number of known intents. Different from them, in this paper, we consider another way without using outlier detection algorithms. Specifically, we directly train a (K+1)-class classifier for open intent classification, where the (K+1)-th class represents open intents. To address the challenge that training a (K+1)-class classifier with training samples of only K classes, we propose a deep model based on Soft Labeling and Manifold Mixup (SLMM). In our method, soft labeling is used to reshape the label distribution of the known intent samples, aiming at reducing model's overconfident on known intents. Manifold mixup is used to generate pseudo samples for open intents, aiming at well optimizing the decision boundary of open intents. Experiments on four benchmark datasets demonstrate that our method outperforms previous methods and achieves state-of-the-art performance. All the code and data of this work can be obtained at https://github.com/zifengcheng/SLMM.Comment: Accepted by IEEE/ACM Transactions on Audio Speech and Language Processin

Influence of Test Parameters on the Evaluation of Chocolate Silkiness Using the Tribological Method

Silkiness is an extremely important attribute in high-end chocolate, and tribology is one of the commonly used methods of evaluating certain properties of the relevant food. In this study, based on three commercial chocolates of the same brand, the silky sensation was assessed by means of the professional sensation evaluation method. Artificial saliva was employed to obtain the mixed solutions with different chocolates, and their viscosity and coefficient of friction (CoF) were measured under different test parameters. The correlation of chocolate silkiness with the viscosities and average CoFs (aCoFs) are later discussed. The results showed that the silkiness of the three chocolates were negatively correlated with cocoa concentration and weakly correlated with viscosity. As the chocolate percentage decreased, the aCoF of the mixed solutions decreased, but the aCoF of the mixed solutions increased in relation to the cocoa concentration. In combination with the correlation coefficient of chocolate silkiness with the aCoFs, it was considered that 75% chocolate solutions using the Two-PDMS pair could be representative of the silkiness characteristic in oral processing at suitable operated parameters. The study results provide an insight into the rapid evaluation and development of similar attributes of high-end food

Influence of Test Parameters on the Evaluation of Chocolate Silkiness Using the Tribological Method

Silkiness is an extremely important attribute in high-end chocolate, and tribology is one of the commonly used methods of evaluating certain properties of the relevant food. In this study, based on three commercial chocolates of the same brand, the silky sensation was assessed by means of the professional sensation evaluation method. Artificial saliva was employed to obtain the mixed solutions with different chocolates, and their viscosity and coefficient of friction (CoF) were measured under different test parameters. The correlation of chocolate silkiness with the viscosities and average CoFs (aCoFs) are later discussed. The results showed that the silkiness of the three chocolates were negatively correlated with cocoa concentration and weakly correlated with viscosity. As the chocolate percentage decreased, the aCoF of the mixed solutions decreased, but the aCoF of the mixed solutions increased in relation to the cocoa concentration. In combination with the correlation coefficient of chocolate silkiness with the aCoFs, it was considered that 75% chocolate solutions using the Two-PDMS pair could be representative of the silkiness characteristic in oral processing at suitable operated parameters. The study results provide an insight into the rapid evaluation and development of similar attributes of high-end food