This research explores the intersection of artificial intelligence and finance, focusing on the emergence of intelligent investment advisers, commonly known as Robo-advisers (RAs). These RAs utilize robust computer models and artificial intelligence algorithms to deliver personalized asset management investment plans for users. Notably, Wealthfront is highlighted as a prominent platform in this field, offering automated investment management services aimed at optimizing investment returns. The study investigates the impact of users' past investment performance on their adoption of intelligent advisers, considering factors such as previous defaults and recent investment performance. It reveals that frequent adjustments to the use of intelligent advisers may hinder long-term investment objectives, emphasizing the importance of consistent usage to fully capitalize on their benefits. Furthermore, the research emphasizes the significance of transparency, user-friendly interaction design, and tailored financial services to foster user trust and enhance the optimization of intelligent advisers' design.

Intelligent manufacturing has gradually become an important development trend in the industrial field. As an artificial intelligence technology, machine vision has been widely used in the field of automation. This paper discusses the development and application of robot arm intelligent picking system based on machine vision in the field of intelligent manufacturing. The system converts the target into the image signal through the image acquisition device, and sends it to the special image processing system for digital processing. Then, the image system performs various operations on the signal to extract the features of the target, and controls the action of the field equipment according to the discriminating results. With machine vision technology as the core, the system realizes automatic picking tasks and improves production efficiency and quality.

Robust 3D object detection remains a pivotal concern in the domain of autonomous field robotics. Despite notable enhancements in detection accuracy across standard datasets, real-world urban environments, characterized by their unstructured and dynamic nature, frequently precipitate an elevated incidence of false positives, thereby undermining the reliability of existing detection paradigms. In this context, our study introduces an advanced post-processing algorithm that modulates detection thresholds dynamically relative to the distance from the ego object. Traditional perception systems typically utilize a uniform threshold, which often leads to decreased efficacy in detecting distant objects. In contrast, our proposed methodology employs a Neural Network with a self-adaptive thresholding mechanism that significantly attenuates false negatives while concurrently diminishing false positives, particularly in complex urban settings. Empirical results substantiate that our algorithm not only augments the performance of 3D object detection models in diverse urban and adverse weather scenarios but also establishes a new benchmark for adaptive thresholding techniques in field robotics.

This study is inspired by the Kaggle competition “WSDM - KKBox’s Music Recommendation Challenge”. The study focuses on doing a comparative study on music recommendation models. Based on the requirements and the given dataset from the Kaggle competition, the problem can be transferred to a classification problem, and therefore, we chose three classification models for the comparative study. The three models are K Nearest Neighbors (KNN), Random Forest, and Light Gradient Boosting Machine (LightGBM). We also did data analysis and data preparation before applying the model and used the handout method and cross-validation method for the validation. For the evaluation, the AUC score is applied to the results and the empirical results show that LightGBM is the best model among these three.

Machine learning has brought significant advancements to the field of structural health monitoring, providing flexible and efficient solutions for detecting both local and global damage in various infrastructures. Local damage detection focuses on identifying issues such as cracks and spalling in specific areas of concrete structures, including bridges, highways, and tunnels. Techniques like artificial neural networks (ANNs) and deep neural networks (DNNs) have proven effective in surface defect recognition, demonstrating their versatility across different structural environments. Additionally, cost-effective methods leveraging devices such as smartphones have been explored for rapid road integrity assessments, offering practical and affordable solutions. On a larger scale, global damage detection involves classifying structural collapse modes and types of damage, utilizing feature extraction and deep learning models to improve the accuracy of identifying large-scale failures. These advancements highlight the growing importance of machine learning and computer vision in enhancing the resilience and real-time monitoring of critical infrastructure systems.

Advancements in technology and societal changes have profoundly altered lifestyles, leading to an increased desire for global travel. Alongside this trend, the concept of the 'sharing house' has emerged as a popular alternative to traditional hotel accommodations. Sharing houses offer benefits such as flexible rental periods, a variety of housing options, and competitive pricing, making them increasingly attractive to travelers. Airbnb stands out as a leading platform facilitating this model. Analyzing Airbnb data provides valuable insights for government policy-making, urban planning, travel planning for renters, host profitability, and strategic decisions for Airbnb itself. This study utilizes data from Airbnb's London listings, focusing on seven key attributes. Employing unsupervised learning techniques like K-Means Clustering combined with Principal Component Analysis (PCA), the study identifies three principal components and two distinct clusters, achieving a silhouette score of 0.64. By visualizing these clusters on a map, the research offers guidance to the London government for a deeper understanding of host behaviors and assists renters in selecting more suitable accommodations and hosts on Airbnb.