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1 – 10 of 448Chuyu Tang, Hao Wang, Genliang Chen and Shaoqiu Xu
This paper aims to propose a robust method for non-rigid point set registration, using the Gaussian mixture model and accommodating non-rigid transformations. The posterior…
Abstract
Purpose
This paper aims to propose a robust method for non-rigid point set registration, using the Gaussian mixture model and accommodating non-rigid transformations. The posterior probabilities of the mixture model are determined through the proposed integrated feature divergence.
Design/methodology/approach
The method involves an alternating two-step framework, comprising correspondence estimation and subsequent transformation updating. For correspondence estimation, integrated feature divergences including both global and local features, are coupled with deterministic annealing to address the non-convexity problem of registration. For transformation updating, the expectation-maximization iteration scheme is introduced to iteratively refine correspondence and transformation estimation until convergence.
Findings
The experiments confirm that the proposed registration approach exhibits remarkable robustness on deformation, noise, outliers and occlusion for both 2D and 3D point clouds. Furthermore, the proposed method outperforms existing analogous algorithms in terms of time complexity. Application of stabilizing and securing intermodal containers loaded on ships is performed. The results demonstrate that the proposed registration framework exhibits excellent adaptability for real-scan point clouds, and achieves comparatively superior alignments in a shorter time.
Originality/value
The integrated feature divergence, involving both global and local information of points, is proven to be an effective indicator for measuring the reliability of point correspondences. This inclusion prevents premature convergence, resulting in more robust registration results for our proposed method. Simultaneously, the total operating time is reduced due to a lower number of iterations.
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Vaishali Rajput, Preeti Mulay and Chandrashekhar Madhavrao Mahajan
Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired…
Abstract
Purpose
Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.
Design/methodology/approach
Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.
Findings
The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.
Originality/value
The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.
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Zhanghuang Xie, Xiaomei Li, Dian Huang, Andrea Appolloni and Kan Fang
We consider a joint optimization problem of product platform design and scheduling on unrelated additive/subtractive hybrid machines, and seek to find efficient solution…
Abstract
Purpose
We consider a joint optimization problem of product platform design and scheduling on unrelated additive/subtractive hybrid machines, and seek to find efficient solution approaches to solve such problem.
Design/methodology/approach
We propose a mathematical formulation for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines, and develop a simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length to solve the problem.
Findings
The simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length (SAHH-osla) that we proposed can be quite efficient in solving the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.
Originality/value
To the best of our knowledge, we are one of the first to consider both cost-related and time-related criteria for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.
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Atefeh Hemmati, Mani Zarei and Amir Masoud Rahmani
Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of…
Abstract
Purpose
Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.
Design/methodology/approach
This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.
Findings
This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.
Research limitations/implications
This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.
Originality/value
This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.
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Mingke Gao, Zhenyu Zhang, Jinyuan Zhang, Shihao Tang, Han Zhang and Tao Pang
Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and…
Abstract
Purpose
Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.
Design/methodology/approach
This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.
Findings
This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.
Originality/value
It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.
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Users often struggle to select choosing among similar online services. To help them make informed decisions, it is important to establish a service reputation measurement…
Abstract
Purpose
Users often struggle to select choosing among similar online services. To help them make informed decisions, it is important to establish a service reputation measurement mechanism. User-provided feedback ratings serve as a primary source of information for this mechanism, and ensuring the credibility of user feedback is crucial for a reliable reputation measurement. Most of the previous studies use passive detection to identify false feedback without creating incentives for honest reporting. Therefore, this study aims to develop a reputation measure for online services that can provide incentives for users to report honestly.
Design/methodology/approach
In this paper, the authors present a method that uses a peer prediction mechanism to evaluate user credibility, which evaluates users’ credibility with their reports by applying the strictly proper scoring rule. Considering the heterogeneity among users, the authors measure user similarity, identify similar users as peers to assess credibility and calculate service reputation using an improved expectation-maximization algorithm based on user credibility.
Findings
Theoretical analysis and experimental results verify that the proposed method motivates truthful reporting, effectively identifies malicious users and achieves high service rating accuracy.
Originality/value
The proposed method has significant practical value in evaluating the authenticity of user feedback and promoting honest reporting.
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Mengyang Gao, Jun Wang and Ou Liu
Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity…
Abstract
Purpose
Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China.
Design/methodology/approach
After verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis.
Findings
The results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies.
Practical implications
The findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types.
Originality/value
This study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.
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Farjam Eshraghian, Najmeh Hafezieh, Farveh Farivar and Sergio de Cesare
The applications of Artificial Intelligence (AI) in various areas of professional and knowledge work are growing. Emotions play an important role in how users incorporate a…
Abstract
Purpose
The applications of Artificial Intelligence (AI) in various areas of professional and knowledge work are growing. Emotions play an important role in how users incorporate a technology into their work practices. The current study draws on work in the areas of AI-powered technologies adaptation, emotions, and the future of work, to investigate how knowledge workers feel about adopting AI in their work.
Design/methodology/approach
We gathered 107,111 tweets about the new AI programmer, GitHub Copilot, launched by GitHub and analysed the data in three stages. First, after cleaning and filtering the data, we applied the topic modelling method to analyse 16,130 tweets posted by 10,301 software programmers to identify the emotions they expressed. Then, we analysed the outcome topics qualitatively to understand the stimulus characteristics driving those emotions. Finally, we analysed a sample of tweets to explore how emotional responses changed over time.
Findings
We found six categories of emotions among software programmers: challenge, achievement, loss, deterrence, scepticism, and apathy. In addition, we found these emotions were driven by four stimulus characteristics: AI development, AI functionality, identity work, and AI engagement. We also examined the change in emotions over time. The results indicate that negative emotions changed to more positive emotions once software programmers redirected their attention to the AI programmer's capabilities and functionalities, and related that to their identity work.
Practical implications
Overall, as organisations start adopting AI-powered technologies in their software development practices, our research offers practical guidance to managers by identifying factors that can change negative emotions to positive emotions.
Originality/value
Our study makes a timely contribution to the discussions on AI and the future of work through the lens of emotions. In contrast to nascent discussions on the role of AI in high-skilled jobs that show knowledge workers' general ambivalence towards AI, we find knowledge workers show more positive emotions over time and as they engage more with AI. In addition, this study unveils the role of professional identity in leading to more positive emotions towards AI, as knowledge workers view such technology as a means of expanding their identity rather than as a threat to it.
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Gianluca Piero Maria Virgilio, Fausto Saavedra Hoyos and Carol Beatriz Bao Ratzemberg
The aim of this paper is to summarise the state-of-the-art debate on impact of artificial intelligence on unemployment and reporting up-to-date academic findings.
Abstract
Purpose
The aim of this paper is to summarise the state-of-the-art debate on impact of artificial intelligence on unemployment and reporting up-to-date academic findings.
Design/methodology/approach
The paper is designed as a review of the labour vs capital conundrum, the differences between industrial automation and artificial intelligence, threat to employment, the difficulty of substituting, role of soft skills and whether technology leads to the deskilling of human workers or favors increasing human capabilities.
Findings
Some authors praise the bright future developments of artificial intelligence while others warn about mass unemployment. Therefore, it is paramount to present an up-to-date overview of the problem, compare and contrast its features with what happened in past innovation waves and contribute to academic discussion about the pros/cons of current trends.
Originality/value
The main value of this paper is presenting a balanced view of 100+ different studies, the vast majority from the last five years. Reading this paper will allow to quickly grasp the main issues around the thorny topic of artificial intelligence and unemployment.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0338
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Daria Plotkina, Hava Orkut and Meral Ahu Karageyim
Financial services industry is increasingly showing interest in automated financial advisors, or robo-advisors, with the aim of democratizing access to financial advice and…
Abstract
Purpose
Financial services industry is increasingly showing interest in automated financial advisors, or robo-advisors, with the aim of democratizing access to financial advice and stimulating investment behavior among populations that were previously less active and less served. However, the extent to which consumers trust this technology influences the adoption of rob-advisors. The resemblance to a human, or anthropomorphism, can provide a sense of social presence and increase trust.
Design/methodology/approach
In this paper, we conduct an experiment (N = 223) to test the effect of anthropomorphism (low vs medium vs high) and gender (male vs female) of the robo-advisor on social presence. This perception, in turn, enables consumers to evaluate personality characteristics of the robo-advisor, such as competence, warmth, and persuasiveness, all of which are related to trust in the robo-advisor. We separately conduct an experimental study (N = 206) testing the effect of gender neutrality on consumer responses to robo-advisory anthropomorphism.
Findings
Our results show that consumers prefer human-alike robo-advisors over machinelike or humanoid robo-advisors. This preference is only observed for male robo-advisors and is explained by perceived competence and perceived persuasiveness. Furthermore, highlighting gender neutrality undermines the positive effect of robo-advisor anthropomorphism on trust.
Originality/value
We contribute to the body of knowledge on robo-advisor design by showing the effect of robot’s anthropomorphism and gender on consumer perceptions and trust. Consequently, we offer insightful recommendations to promote the adoption of robo-advisory services in the financial sector.
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