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Intro -- Preface -- Contents -- Deep Learning in Robotics for Strengthening Industry 4.0.: Opportunities, Challenges and Future Directions -- 1 Introduction -- 2 Introduction to Deep Learning -- 3 Deep Learning in Robotics -- 3.1 Object Detection -- 3.2 Object Grasping or Robotic Grasp -- 3.3 Acoustic Modeling -- 3.4 Motion Control -- 4 Challenges and Future Opportunities -- 5 Conclusion -- References -- The Design of a Pheromone-Based Robotic Varroa Trap for Beekeeping Applications -- 1 Introduction -- 2 Existing Solutions -- 2.1 Cultural or Biological Approaches -- 2.2 Mechanical Approaches -- 2.3 Chemical Approaches -- 3 Hardware -- 3.1 Power Supply -- 3.2 Microcontroller -- 3.3 Communication Interface -- 3.4 Switching Circuit -- 3.5 High Voltage Generation -- 3.6 Temperature Measurement -- 3.7 Miscellaneous Circuits -- 3.8 Vaporizer -- 3.9 Enclosure and High Voltage Grid -- 4 Software -- 4.1 The IDE Coocox and Standard Peripheral -- 4.2 The Main Function -- 4.3 The Initialization Functions -- 4.4 Communication with Computer -- 4.5 Temperature Control -- 4.6 The Complete System -- 5 Results -- 6 Conclusion -- References -- Walk Through Event Stream Processing Architecture, Use Cases and Frameworks Survey -- 1 Introduction -- 2 Data Architecture -- 3 Use Cases -- 4 Frameworks -- 5 Comparison -- 6 Conclusion -- References -- Artificial Intelligence for Cybersecurity: Recent Advancements, Challenges and Opportunities -- 1 Introduction -- 1.1 Motivation -- 2 Related Work -- 3 Framework of AI Based Cyber-Security System -- 4 Latest Trends of AI in Cyber-Security -- 4.1 User Access Authentication -- 4.2 Network Situation Awareness -- 4.3 Risk Monitoring -- 4.4 Abnormal Traffic Identification with the Help of Bots -- 4.5 Malware Identification -- 5 Challenges Faced by Researchers -- 5.1 Cyber-Security Datasets -- 5.2 Security Protocols.

Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers' future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2–13.6% and 10.2–12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9–12.7% and 6.9–8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account—namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.

AbstractThis study aims to explore thematic influences on theme park visitors' satisfaction through user‐generated data. To this end, we first used an unsupervised machine learning method, structural topic modeling, and analyzed 112,000 reviews post by visitors to Shanghai Disney Resort from June 16, 2016 to March 4, 2022. Our findings are of great significance for reflecting consumer behavior through user‐generated data. Specifically, we find that visitors' satisfaction is highly related to service in the theme park and their playing feeling, and early tourists pay more attention to the experience of specific playing items while later tourists focus on the overall playing experience. In addition, an empirical study is conducted by treating the ratings associated with each review as dependent variable and each topic represented by comments as independent variables, which shows that the relationship between the customer reviews and ratings by tourists becomes less pronounced over time. In other words, as time goes, customers review can reflect their subjective feelings or experience, but the rating is not. We discover the "dynamics" of user‐generated data over time and gain a better understanding of the aspects and concerns of visitors' satisfaction over time. The findings of the study contribute to the literature on tourism, service, and consumer behavior while also providing valuable practical implications.