Huiyong Wang, Ding Yang, Liang Guo and Xiaoming Zhang
Intent detection and slot filling are two important tasks in question comprehension of a question answering system. This study aims to build a joint task model with some…
Abstract
Purpose
Intent detection and slot filling are two important tasks in question comprehension of a question answering system. This study aims to build a joint task model with some generalization ability and benchmark its performance over other neural network models mentioned in this paper.
Design/methodology/approach
This study used a deep-learning-based approach for the joint modeling of question intent detection and slot filling. Meanwhile, the internal cell structure of the long short-term memory (LSTM) network was improved. Furthermore, the dataset Computer Science Literature Question (CSLQ) was constructed based on the Science and Technology Knowledge Graph. The datasets Airline Travel Information Systems, Snips (a natural language processing dataset of the consumer intent engine collected by Snips) and CSLQ were used for the empirical analysis. The accuracy of intent detection and F1 score of slot filling, as well as the semantic accuracy of sentences, were compared for several models.
Findings
The results showed that the proposed model outperformed all other benchmark methods, especially for the CSLQ dataset. This proves that the design of this study improved the comprehensive performance and generalization ability of the model to some extent.
Originality/value
This study contributes to the understanding of question sentences in a specific domain. LSTM was improved, and a computer literature domain dataset was constructed herein. This will lay the data and model foundation for the future construction of a computer literature question answering system.
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Xiaohua Zhao, Xuewei Li, Yufei Chen, Haijian Li and Yang Ding
Heavy fog results in low visibility, which increases the probability and severity of traffic crashes, and fog warning system is conducive to the reduction of crashes by conveying…
Abstract
Purpose
Heavy fog results in low visibility, which increases the probability and severity of traffic crashes, and fog warning system is conducive to the reduction of crashes by conveying warning messages to drivers. This paper aims at exploring the effects of dynamic message sign (DMS) of fog warning system on driver performance.
Design/methodology/approach
First, a testing platform was established based on driving simulator and driver performance data under DMS were collected. The experiment route was consisted of three different zones (i.e. warning zone, transition zone and heavy fog zone), and mean speed, mean acceleration, mean jerk in the whole zone, ending speed in the warning zone and transition zone, maximum deceleration rate and mean speed reduction proportion in the transition zone and heavy fog zone were selected. Next, the one-way analysis of variance was applied to test the significant difference between the metrics. Besides, drivers’ subjective perception was also considered.
Findings
The results indicated that DMS is beneficial to reduce speed before drivers enter the heavy fog zone. Besides, when drivers enter a heavy fog zone, DMS can reduce the tension of drivers and make drivers operate more smoothly.
Originality/value
This paper provides a comprehensive approach for evaluating the effectiveness of the warning system in adverse conditions based on the driving simulation test platform. The method can be extended to the evaluation of vehicle-to-infrastructure technology in other special scenarios.
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Amy Yeo Chu May, Carmen Teoh Chia Wen and Jeffton Low Boon Tiong
This study seeks to find an interactive effect between ethical leadership (EL) and corporate governance (CG) variables and investigate whether they would affect employee…
Abstract
This study seeks to find an interactive effect between ethical leadership (EL) and corporate governance (CG) variables and investigate whether they would affect employee organizational citizenship behavior (EOCB) in a Malaysian organizational setting. The collected data from the 300 accounting/finance department employees were analyzed using Statistical Package for Social Sciences (SPSS) and Partial Least Square–Structural Equation Modeling (PLS-SEM; SmartPLS 3.0). Several primary results confirmed a coherent significant relationship between EL and ethical climate (EC), EL and EOCB, EL and CG, and CG and organizational success. Theoretically, it implies a more enhanced EOCB literature on how it can be infused in an organization. It also offers valuable knowledge by providing organizations with several insights concerning the improvement of EOCB, enabling the organization to achieve its desired success and, more importantly, how the findings could contribute directly and indirectly to emerging markets in terms of their industrial and financial performance.
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Divya Goswami and Balraj Verma
Using VOSviewer software, this research delves into the various implications of ethical artificial intelligence (AI) within the retail industry. We explored the latest research…
Abstract
Using VOSviewer software, this research delves into the various implications of ethical artificial intelligence (AI) within the retail industry. We explored the latest research trends using bibliometric analysis unveiling the journals, organisations, sources, articles, and documents that topped the chart. To shed light on the critical areas, we leveraged a citation analysis approach to explore the numerous trending research areas that were associated with fostering trust and transparency in AI-based retail applications. The research recognised the most influential areas by investigating the highly cited works. This research insight works as a guiding roadmap to navigate the complexities related to the ethical use of AI and direct towards fostering trust.
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Jari Laru, Ismail Celik, Iiris Kivioja and Kati Mäkitalo
This book chapter reports on an AI workshop for pre-service teachers that aimed to improve their AI literacy and prepare them for integrating AI technologies into their future…
Abstract
This book chapter reports on an AI workshop for pre-service teachers that aimed to improve their AI literacy and prepare them for integrating AI technologies into their future teaching. The workshop involved the use of an interactive tool (GenAI Teachable Machine) that allows users to create and deploy machine-learning applications without coding. Participants worked in groups to design and develop their own AI applications based on their interests and curriculum subjects. The chapter describes the design and outcomes of the workshop, and the reflections of the participants and the instructor. The chapter also provides recommendations for incorporating similar workshops into teacher training programmes to promote AI literacy skills.
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Chao Liu, Mingyang Yang, Haoyu Han and Wenping Yue
To study fracture characteristics of jointed rock masses under blasting load, the RFPA2D analysis software for dynamic fracture of rocks based on the finite element method and…
Abstract
Purpose
To study fracture characteristics of jointed rock masses under blasting load, the RFPA2D analysis software for dynamic fracture of rocks based on the finite element method and statistical damage theory was used.
Design/methodology/approach
On this basis, this research simulated the fracture process of rock masses in blasting with different joint geometrical characteristics and mainly analysed the influences of distance from joints to blasting holes, the length of joints, the number of joints and joint angle on fracture of rock masses.
Findings
The calculation results show that with the constant increase of the distance from joints to blasting holes, the influences of joints on blasting effects of rock masses gradually reduced. Rock masses with long joints experienced more serious damages than those with short joints. Damages obviously increased with the changing from rock masses without joints to rock masses with joints, and when there were three joints, the further increase of the number of joints had unobvious changes on blasting effects of rock masses. Joints showed significant guidance effect on the propagation of cracks in blasting: promoting propagation of main vertical cracks deflecting to the ends of joints.
Originality/value
The research results are expected to provide some theoretical bases in practical application of engineering blasting.
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Jiaming Han, Zhong Yang, Guoxiong Hu, Ting Fang and Hao Xu
This paper aims to propose a robust and efficient method for vanishing point detection in unstructured road scenes.
Abstract
Purpose
This paper aims to propose a robust and efficient method for vanishing point detection in unstructured road scenes.
Design/methodology/approach
The proposed method includes two main stages: drivable region estimation and vanishing point detection. In drivable region estimation stage, the road image is segmented into a set of patches; then the drivable region is estimated by the patch-wise manifold ranking. In vanishing point detection stage, the LSD method is used to extract the straight lines; then a series of principles are proposed to remove the noise lines. Finally, the vanishing point is detected by a novel voting strategy.
Findings
The proposed method is validated on various unstructured road images collected from the real world. It is more robust and more efficient than the state-of-the-art method and the other three recent methods. Experimental results demonstrate that the detected vanishing point is practical for vision-sensor-based navigation in complex unstructured road scenes.
Originality/value
This paper proposes a patch-wise manifold ranking method to estimate the drivable region that contains most of the informative clues for vanishing point detection. Based on the removal of the noise lines through a series of principles, a novel voting strategy is proposed to detect the vanishing point.
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The purpose of this paper is to propose a novel improved teaching and learning-based algorithm (TLBO) to enhance its convergence ability and solution accuracy, making it more…
Abstract
Purpose
The purpose of this paper is to propose a novel improved teaching and learning-based algorithm (TLBO) to enhance its convergence ability and solution accuracy, making it more suitable for solving large-scale optimization issues.
Design/methodology/approach
Utilizing multiple cooperation mechanisms in teaching and learning processes, an improved TBLO named CTLBO (collectivism teaching-learning-based optimization) is developed. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher–learner cooperation strategies in teaching and learning processes. Applying modularization idea, based on the configuration structure of operators of CTLBO, six variants of CTLBO are constructed. For identifying the best configuration, 30 general benchmark functions are tested. Then, three experiments using CEC2020 (2020 IEEE Conference on Evolutionary Computation)-constrained optimization problems are conducted to compare CTLBO with other algorithms. At last, a large-scale industrial engineering problem is taken as the application case.
Findings
Experiment with 30 general unconstrained benchmark functions indicates that CTLBO-c is the best configuration of all variants of CTLBO. Three experiments using CEC2020-constrained optimization problems show that CTLBO is one powerful algorithm for solving large-scale constrained optimization problems. The application case of industrial engineering problem shows that CTLBO and its variant CTLBO-c can effectively solve the large-scale real problem, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO and CTLBO-c, revealing that CTLBO and its variants can far outperform other algorithms. CTLBO is an excellent algorithm for solving large-scale complex optimization issues.
Originality/value
The innovation of this paper lies in the improvement strategies in changing the original TLBO with two-phase teaching–learning mechanism to a new algorithm CTLBO with three-phase multiple cooperation teaching–learning mechanism, self-learning mechanism in teaching and group teaching mechanism. CTLBO has important application value in solving large-scale optimization problems.
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Shuxin Ding, Tao Zhang, Kai Sheng, Yuanyuan Chen and Zhiming Yuan
The intelligent Central Traffic Control (CTC) system plays a vital role in establishing an intelligent high-speed railway (HSR) system. As the core of HSR transportation command…
Abstract
Purpose
The intelligent Central Traffic Control (CTC) system plays a vital role in establishing an intelligent high-speed railway (HSR) system. As the core of HSR transportation command, the intelligent CTC system is a new HSR dispatching command system that integrates the widely used CTC in China with the practical service requirements of intelligent dispatching. This paper aims to propose key technologies and applications for intelligent dispatching command in HSR in China.
Design/methodology/approach
This paper first briefly introduces the functions and configuration of the intelligent CTC system. Some new servers, terminals and interfaces are introduced, which are plan adjustment server/terminal, interface for automatic train operation (ATO), interface for Dynamic Monitoring System of Train Control Equipment (DMS), interface for Power Supervisory Control and Data Acquisition (PSCADA), interface for Disaster Monitoring, etc.
Findings
The key technologies applied in the intelligent CTC system include automatic adjustment of train operation plans, safety control of train routes and commands, traffic information data platform, integrated simulation of traffic dispatching and ATO function. These technologies have been applied in the Beijing-Zhangjiakou HSR, which commenced operations at the end of 2019. Implementing these key intelligent functions has improved the train dispatching command capacity, ensured the safe operation of intelligent HSR, reduced the labor intensity of dispatching operators and enhanced the intelligence level of China's dispatching system.
Originality/value
This paper provides further challenges and research directions for the intelligent dispatching command of HSR. To achieve the objectives, new measures need to be conducted, including the development of advanced technologies for intelligent dispatching command, coping with new requirements with the development of China's railway signaling system, the integration of traffic dispatching and train control and the application of AI and data-driven modeling and methods.
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Ning Qian, Muhammad Jamil, Wenfeng Ding, Yucan Fu and Jiuhua Xu
This paper is supposed to provide a critical review of current research progress on thermal management in grinding of superalloys, and future directions and challenges. By…
Abstract
Purpose
This paper is supposed to provide a critical review of current research progress on thermal management in grinding of superalloys, and future directions and challenges. By understanding the current progress and identifying the developing directions, thermal management can be achieved in the grinding of superalloys to significantly improve the grinding quality and efficiency.
Design/methodology/approach
The relevant literature is collected from Web of Science, Scopus, CNKI, Google scholar, etc. A total of 185 literature is analyzed, and the findings in the literature are systematically summarized. In this case, the current development and future trends of thermal management in grinding of superalloys can be concluded.
Findings
The recent developments in grinding superalloys, demands, challenges and solutions are analyzed. The theoretical basis of thermal management in grinding, the grinding heat partition analysis, is also summarized. The novel methods and technologies for thermal management are developed and reviewed, i.e. new grinding technologies and parameter optimization, super abrasive grinding wheel technologies, improved lubrication, highly efficient coolant delivery and enhanced heat transfer by passive thermal devices. Finally, the future trends and challenges are identified.
Originality/value
Superalloys have excellent physical and mechanical properties, e.g. high thermal stability, and good high-temperature strength. The superalloys have been broadly applied in the aerospace, energy and automobile industries. Grinding is one of the most important precision machining technologies for superalloy parts. Owing to the mechanical and physical properties of superalloys, during grinding processes, forces are large and a massive heat is generated. Consequently, the improvement of grinding quality and efficiency is limited. It is important to conduct thermal management in the grinding of superalloys to decrease grinding forces and heat generation. The grinding heat is also dissipated in time by enhanced heat transfer methods. Therefore, it is necessary and valuable to holistically review the current situation of thermal management in grinding of superalloys and also provide the development trends and challenges.