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The purpose of this paper is to explore the issues on how to achieve crowdsourced real-time captioning of sign language by deaf and hard-of-hearing (DHH) people, such that how a system structure should be designed, how a continuous task of sign language captioning should be divided into microtasks and how many DHH people are required to maintain a high-quality real-time captioning.

The authors first propose a system structure, including the new design of worker roles, task division and task assignment. Then, based on an implemented prototype, the authors analyze the necessary setting for achieving a crowdsourced real-time captioning of sign language, test the feasibility of the proposed system and explore its robustness and improvability through four experiments.

The results of Experiment 1 have revealed the optimal method for task division, the necessary minimum number of groups and the necessary minimum number of workers in a group. The results of Experiment 2 have verified the feasibility of the crowdsourced real-time captioning of sign language by DHH people. The results of Experiment 3 and Experiment 4 have shown the robustness and improvability of the captioning system.

Although some crowdsourcing-based systems have been developed for the captioning of voice to text, the authors intend to resolve the issues on the captioning of sign language to text, for which the existing approaches do not work well due to the unique properties of sign language. Moreover, DHH people are generally considered as the ones who receive support from others, but our proposal helps them become the ones who offer support to others.

An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract features which have strong generalization and data representation ability at the same time is still an open problem for machine vision-based methods.

A micro-crack detection method based on adaptive deep features and visual saliency is proposed in this paper. The proposed method can adaptively extract deep features from the input image without any supervised training. Furthermore, considering the fact that micro-cracks can obviously attract visual attention when people look at the solar cell’s surface, the visual saliency is also introduced for the micro-crack detection.

Comprehensive evaluations are implemented on two existing data sets, where subjective experimental results show that most of the micro-cracks can be detected, and the objective experimental results show that the method proposed in this study has better performance in detecting precision.

First, an adaptive deep features extraction scheme without any supervised training is proposed for micro-crack detection. Second, the visual saliency is introduced for micro-crack detection.

Identifying important users from social media has recently attracted much attention in the information and knowledge management community. Although researchers have focused on users’ knowledge levels on certain topics or influence degrees on other users in social networks, previous works have not studied users’ prediction ability on future popularity. This paper aims to propose a novel approach to find prophetic bloggers based on their buzzword prediction ability.

The main approach is to conduct a time-series analysis in the blogosphere considering four factors: post earliness, content similarity, entry frequency and buzzword coverage. Our method has four steps: categorizing a blogger into knowledgeable categories, identifying past buzzwords, analyzing a buzzword’s peak time content and growth period and, finally, evaluating a blogger’s prediction ability on a buzzword and on a category.

Experimental results on real-world blog data consisting of 150 million entries from 11 million bloggers demonstrate that the proposed approach can find prophetic bloggers and outperforms others that do not take temporal features into account.

To the best of the authors’ knowledge, our approach is the first successful attempt to identify prophetic bloggers. Finding prophetic bloggers can bring great values for two reasons. First, as prophetic bloggers tend to post creative and insightful information, analysis on their blog entries may help find future buzzword candidates. Second, communication with prophetic bloggers can help understand future trends, gain insight into early adopters’ thoughts on new technology or even foresee things that will become popular.

This paper aims to improve the diversity and richness of haptic perception by recognizing multi-modal haptic images.

First, the multi-modal haptic data collected by BioTac sensors from different objects are pre-processed, and then combined into haptic images. Second, a multi-class and multi-label deep learning model is designed, which can simultaneously learn four haptic features (hardness, thermal conductivity, roughness and texture) from the haptic images, and recognize objects based on these features. The haptic images with different dimensions and modalities are provided for testing the recognition performance of this model.

The results imply that multi-modal data fusion has a better performance than single-modal data on tactile understanding, and the haptic images with larger dimension are conducive to more accurate haptic measurement.

The proposed method has important potential application in unknown environment perception, dexterous grasping manipulation and other intelligent robotics domains.

This paper proposes a new deep learning model for extracting multiple haptic features and recognizing objects from multi-modal haptic images.

This paper aims to present the spherical entropy image (SEI), a novel global descriptor for the scan registration of three-dimensional (3D) point clouds. This paper also introduces a global feature-less scan registration strategy based on SEI. It is advantageous for 3D data processing in the scenarios such as mobile robotics and reverse engineering.

The descriptor works through representing the scan by a spherical function named SEI, whose properties allow to decompose the six-dimensional transformation into 3D rotation and 3D translation. The 3D rotation is estimated by the generalized convolution theorem based on the spherical Fourier transform of SEI. Then, the translation recovery is determined by phase only matched filtering.

No explicit features and planar segments should be contained in the input data of the method. The experimental results illustrate the parameter independence, high reliability and efficiency of the novel algorithm in registration of feature-less scans.

A novel global descriptor (SEI) for the scan registration of 3D point clouds is presented. It inherits both descriptive power of signature-based methods and robustness of histogram-based methods. A high reliability and efficiency registration method of scans based on SEI is also demonstrated.

The purpose of this paper is to investigate the effect of perceived fit on thriving and reveal the potential moderator and mediator of this effect by drawing on self-determination theory. Moreover, to adapt the research to Chinese contexts, a four-factor conceptualization of perceived fit is suggested.

The paper includes two studies. Study 1 involved a survey that collected data from 531 employees to investigate the relationship between perceived fit and thriving and the moderating role of personal goal commitment. Study 2 consisted of a scenario-based experiment in which 240 university students were recruited to strengthen the main findings of Study 1 and test the underlying mechanisms of the effect of perceived fit on thriving.

Study 1 shows that perceived fit positively relates to thriving, and personal goal commitment plays a moderating role in this effect. In addition, it demonstrates the validity of the proposed four-factor conceptualization of perceived fit in Chinese contexts. Study 2 strengthens the proposition of the positive effect of perceived fit on thriving and identifies the mediating roles of self-determination in this effect.

This paper contributes to the literature on thriving and fit by exploring new antecedents of thriving and extending the dimensions of perceived fit.

Legislation plays an essential role in addressing climate change in China. However, many barriers to formulating national legislation to address climate change have so far prevented its enactment. The bottom-up approach adopted in the international climate regime sets a good example. Accordingly, the purpose of this paper is to discuss the regional legislation to address climate change in China through exploring the following two questions: whether it is necessary to enact climate change legislation at regional level first and whether it is feasible to develop such regional legislation in the absence of national climate change law.

This paper analyses the necessity and feasibility of regional legislation to address climate change. Section 2 introduces the current legislative framework on climate change in China. Section 3 investigates whether it is better to push the legislative agenda at regional, rather than national level. Section 4 analyses the feasibility of establishing regional legislative systems. Section 5 explores the key issues in formulating and promoting regional legislation.

This paper concludes that it is necessary and feasible to pilot regional legislation before enacting national legislation. Under these circumstances, local governments can take the initiative to begin formulating regional legislation.

Addressing climate change needs immediate action and effective measures. It is, thus, necessary to reconsider the approach that China should adopt when developing legislation on climate change. This paper contributes to broadening current knowledge of regional climate change legislation in China.

Based on the conservation of resources view, the objective of this paper is to examine the relationship between abusive supervision and workplace thriving. Further, this study investigates the underlying mechanisms role of agentic work behaviors (i.e. task focus, heedful relating) and moderating role of employee's core self-evaluations.

Using a time-lag approach, data are collected from 360 full-time employees enrolled in an executive development program in a large university of China. To test the proposed model, data analysis is carried out through Statistical Product and Service Solutions (SPSS) and Analysis of Moment Structures (AMOS).

The results show that abusive supervision negatively influences workplace thriving. Further, the findings also confirm the mediating role of agentic work behaviors and the moderating role of core self-evaluations between the relationship of abusive supervision and thriving.

Based on study findings, this study draws the attention of managers toward the new deleterious outcomes of abusive supervision. Hence, to nurture a thriving workforce, organizations should keep abusive behaviors under keen observations to minimize their frequent occurrences. Further, it is proposed that hiring employees with higher core self-evaluations can mitigate the injurious effect of abusive supervision.

This is the first attempt to our knowledge to untapped the abusive supervision-thriving relationship via the underlying mechanisms of two agentic work behavior's and core self-evaluations as a moderator enriches the extant body of knowledge and provide valuable insight into the abusive supervision and workplace thriving literature.

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment.

In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.

To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states.

In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.