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The purpose of this study is to solve the problems caused by the growing volumes of pre-annotated literature and variety-oriented annotations, including teamwork, quality control and time effort.

An annotation collaboration workbench is developed, which is named as Bureau for Rapid Annotation Tool (Brat). Main functionalities include an enhanced semantic constraint system, Vim-like shortcut keys, an annotation filter and a graph-visualizing annotation browser. With these functionalities, the annotators are encouraged to question their initial mindset, inspect conflicts and gain agreement from their peers.

The collaborative patterns can indeed be leveraged to structure properly every annotator’s behaviors. The Brat workbench can actually be seen as an experienced-based annotation tool by harnessing collective intelligence. Compared to previous counterparts, about one-third of time can be saved on Xinhuanet military news and patent corpora with the workbench.

The various annotations are very popular in real-world annotation tasks with multiple annotators. Though, it is still under-discussed on variety-oriented annotations. The findings of this study provide the practitioners valuable insight into how to govern annotation projects. In addition, the Brat workbench takes the first step for future research on annotating large-scale text resources.

An exact pattern prototype is a prerequisite for female girdle pattern-making. The purpose of this paper is to develop new ways to make girdle pattern prototypes based on 3D technology.

This paper presented two novel methods for creating girdle pattern prototypes. The first one was the girdle's parametric foundation pattern developing method based on 3D geometric modeling. In this method, considering the different characteristics of a female's lower body shape, several models were created to define the relationship between the female's lower body shape and the pattern, such as a side-waist curvature model, an interior-posterior waist-warping model, a buttocks' parametric model and an abdomen parametric model. Then, parameters of drawing the prototype were abstracted to facilitate transforming the 3D geometric model into the 2D pattern. Another method was implemented by 3D virtual modeling and unwrapping. The whole process included surface division, surface reconstruction and surface unwrapping.

The prototypes created by these two methods were tested using the 3D virtual trying-on examination. Trial tests showed that the patterns can be dressed in the right positions on the virtual model with little pressure. This means that the proportions and shapes of the pattern are correct. The prototypes obtained through the methods proposed in this paper have good effects and high precision. Both methods can be used for making the girdle's foundation pattern.

Two pragmatic approaches of girdle's prototype building have been put forward. The parametric prototype designing method has changed the unconstrained state of free modeling. The pattern structure can be controlled by parameter constraints. In the other method, with 3D scanning and surface modeling technology, personalized girdle's pattern is generated, and the segmentation lines of the girdle can be designed flexibly according to the requirements. These findings also can be used in other tight garments' prototype making.

Image classification is becoming a supporting technology in several image-processing tasks. Due to rich semantic information contained in the images, it is very popular for an image to have several labels or tags. This paper aims to develop a novel multi-label classification approach with superior performance.

Many multi-label classification problems share two main characteristics: label correlations and label imbalance. However, most of current methods are devoted to either model label relationship or to only deal with unbalanced problem with traditional single-label methods. In this paper, multi-label classification problem is regarded as an unbalanced multi-task learning problem. Multi-task least-squares support vector machine (MTLS-SVM) is generalized for this problem, renamed as multi-label LS-SVM (ML²S-SVM).

Experimental results on the emotions, scene, yeast and bibtex data sets indicate that the ML²S-SVM is competitive with respect to the state-of-the-art methods in terms of Hamming loss and instance-based F1 score. The values of resulting parameters largely influence the performance of ML²S-SVM, so it is necessary for users to identify proper parameters in advance.

On the basis of MTLS-SVM, a novel multi-label classification approach, ML²S-SVM, is put forward. This method can overcome the unbalanced problem but also explicitly models arbitrary order correlations among labels by allowing multiple labels to share a subspace. In addition, the multi-label classification approach has a wider range of applications. That is to say, it is not limited to the field of image classification.

With the rapid development of the 3C industry, the problem of automated operation of 3C wire is becoming increasingly prominent. However, the 3C wire has high flexibility, and its deformation is difficult to model and control. How to realize the automation operation of flexible wire in 3C products is still an important issue that restricts the development of the 3C industry. Therefore, this paper designs a system that aims to improve the automation level of the 3C industry.

This paper designed a visual servo control system. Based on the perception of the flexible wire, a Jacobi matrix is used to relate the deformation of the wire to the action of the robot end; by building and optimizing the Jacobi matrix, the robot can control the flexible wire.

By using the visual servo control system, the shape and deformation of the flexible wire are perceived, and based on this, the robot can control the deformation of the flexible wire well. The experimental environment was built to evaluate the accuracy and stability of the system for controlling the deformation of the flexible wire.

An image-based visual servo system is proposed to operate the flexible wire, including the vision system, visual controller and joint velocity controller. It is a scheme suitable for flexible wire operation, which has helped to automate flexible wire-related industries. Its core is to correlate the motion of the robot end with the deformation of the flexible wire through the Jacobian matrix.

Taking the COVID-19 as the background, this study aims to investigate the direct influencing factors regarding knowledge sharing behavior (KSB) on new media platforms and discuss how the characteristics of the users could enhance the KSB through moderation effect, and provide empirical evidences.

Based on the social exchange theory and after the text analysis of the data collected from the Tiktok platform in 2020, this paper uses the quantitative method to evaluate the factors influence KSB on short video social platform during the COVID-19 outbreak.

KSB on new media platform could be enhanced by richer knowledge content of the video posted and the attribute of the platform users directly. Platform users could affect the trustworthiness of the knowledge shared, thus influence the knowledge sharing. On the early stage of the COVID-19, the richer content of the knowledge released by users could effectively enhance the KSB. On the early stage of the emergency events, the official users could play a significant role on KS. During the mitigation stage of COVID-19, the KSB of the knowledge shared by unofficial users with richer content could be enhanced and the moderation effect is relatively stronger.

The research extends the social exchange theory to a disaster management context. The authors provide an effective reference for future governments to effectively cope with the epidemic and spread public knowledge in an emergency response context. By analyzing the influence of knowledge content and influencer characteristics, it could help the social media platform to improve content management and optimize resource allocation.

The purpose of this paper is to develop two types of simple jerk circuits and to carry out their dynamical analyses using a unified mathematical model.

Two types of simple jerk circuits only involve a nonlinear resistive feedback channel composited by a nonlinear device and an inverter. The nonlinear device is implemented through parallelly connecting two diode-switch-based series branches. According to the classifications of switch states and circuit types, a unified mathematical model is established for these two types of simple jerk circuits, and the origin symmetry and scale proportionality along with the origin equilibrium stability are thereby discussed. The coexisting bifurcation behaviors in the two types of simple jerk systems are revealed by bifurcation plots, and the origin symmetry and scale proportionality are effectively demonstrated by phase plots and attraction basins. Moreover, hardware experimental measurements are performed, from which the captured results well validate the numerical simulations.

Two types of simple jerk circuits are unified through parallelly connecting two diode-switch-based series branches and a unified mathematical model with six kinds of nonlinearities is established. Especially, the origin symmetry and scale proportionality for the two types of simple jerk systems are discussed quantitatively. These jerk circuits are all simple and inexpensive, easy to be physically implemented, which are helpful to explore chaos-based engineering applications.

Unlike previous works, the significant values are that through unifying these two types of simple jerk systems, a unified mathematical model with six kinds of nonlinearities is established, upon which symmetrically scaled coexisting behaviors are numerically disclosed and experimentally demonstrated.

Surface texturing has been proven as an effective means of contact performance enhancement. However, limited work has been done to investigate the regular relationship to solve the multi-parameters problem of textures, and inertia effect and elastic deformation were seldom considered together in previous optimization work. This paper aims to quantitatively obtain the relationship between the textured depth and liquid film thickness and find the effect of deformation on the optimal textured height ratio in elastic parallel sliders.

Numerical models of hydrodynamic lubrication are established based on the computational fluid dynamic method. Elastic deformation is considered through fluid–structure interaction (FSI) method. Using response surface optimization method, textured parallel sliders are optimized with maximum loading capacity as the objective.

The results show that the optimal height ratios are all within the range of 0.60-0.65 when textured parallel sliders are considered as rigid. After considering the effect of elastic deformation, loading capacity drops and is reduced more obviously with a decrease in the elastic moduli. The optimal height ratios are within the range of 0.60-0.63, which shows that FSI has a considerable influence on loading capacity but has no significant influence on the optimal height ratio.

The present research provides a theoretical reference for engineering application of elastic textured parallel sliders.

Shear stresses have a considerable influence on the characteristics of lubricants and become significant at high rotating speeds. This study aims to investigate the influences of shear cavitation (SC) on loading capacity of journal bearings.

A principal normal stress cavitation criterion based on the stress applied to flowing lubricant in journal bearings is developed and used to investigate SC in journal bearings. A computational fluid dynamic (CFD) model for calculating the loading capacity is established using this criterion. After validation with experimental results, the loading capacity is calculated under different conditions.

The calculation results indicate that SC intensifies when viscosity, speed and eccentricity increase. Angle of loading capacity with SC is larger than that without SC. The magnitude of loading capacity with SC is smaller than that without SC due to the decrease in the ultimate pressure. In addition, the magnitude difference between the loading capacity with and without SC increases when viscosity, speed and eccentricity increases.

Present research can provide some guidance for calculating the loading capacity when a journal bearing is operating at high speed or with a high viscosity lubricant.

The loading mechanism of textures considering turbulence has not been fully covered. This paper aims to investigate the effect of turbulence on the textured loading capacity under water lubrication and to analyze the causes of the turbulence effect.

Computational fluid dynamic models with different textured shapes are established after validation. The transition shear stress transport (SST) model, which is suitable for predicting the transition process of fluid from laminar state to turbulent state, is adopted in the present study. To illustrate the effect of turbulence, the loading capacity of textures predicted by transition SST model and laminar model is compared.

The loading capacity is higher after considering turbulence because more lubricant enters into textures and the flow rate of lubricant to textured outlet increases. There exists an optimal textured depth ratio and density for loading capacity and the change of flow state would not affect the optimal values. The degree of fluid blockage at textured outlet has a dominant influence on loading capacity. As the textured shape changes to triangle or ellipse from rectangle, the vortices at the textured bottom move forward and the blockage at a textured outlet is enhanced, which makes loading capacity improved under the action of blocking effect.

The enhancement of the blocking effect is found to be crucial to the improvement of textured loading capacity after considering turbulence. Present research provides references to understand the loading mechanism of textures under turbulent conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2020-0149/

Surface textures have been widely used in thrust bearings as a means of enhancing the tribological performance. The effect of textures with a spiral distribution on the lubrication characteristics of thrust bearings has not been fully covered. This paper aims to investigate and find the optimal structure and distribution parameters of textures with the maximum loading capacity and minimum friction force as goals.

Combining the multi-objective optimization method based on the non-dominated sorted genetic algorithm-II with response surface methodology, the key textured parameters are optimized. Local sensitivity analysis is used to evaluate the impact level of each parameter.

Spiral distribution of textures can effectively improve the lubrication performance of the thrust bearing compared with the linear distribution. The distribution with high amplitudes and high cycle numbers will weaken the spiral effect and destroy the high-pressure region. Through the multi-objective optimization of the textured structure and distribution parameters, the loading capacity demonstrates a 55.05per cent improvement compared to the basic model. Textured width is the most sensitive parameter for both loading capacity and friction force.

Present research provides a fundamental design guide for textured thrust bearings.