Highway surface segmentation consists of extracting road surface at pixel-level from the surveillance camera view. Since the intelligent traffic event detection task does not require the detection of off-road scene, the segmentation of highway surface is of great demand. However, it is challenging to accurately extract road surface in real time scenarios. To cope with the above issues, Bi-Unet, a dual stream lightweight network is proposed. Firstly, the dual stream structure enhances segmentation performance on the narrow remote end of the highway and preserves the detailed border information. Then, to perform real-time segmentation, a novel lightweight module (LSM) is introduced to lighten the model and provide higher segmentation accuracy. Moreover, to ensure road segmentation for complex scenes, a Road Attention Network (RAN) module is proposed. Lastly, due to the lack of a suitable benchmark dataset serve for the highway segmentation problem, a new large and high-quality segmentation dataset named Highway-Surface-Free (HSF) is proposed in this paper, which is collected from the perspective of highway surveillance cameras under all-day and all-weather conditions. Compared with the state of arts, the extensive experimental results verify that our proposed Bi-Unet achieves the best overall performance on our proposed HSF dataset.

Most work on music recommendations has focused on the consumer side not the provider side. We develop a two-sided value-based approach to music artist recommendation for a streaming music scenario. It combines the value yielded for the music industry and consumers in an integrated model. For the industry, the approach aims to increase the conversion rate of potential listeners to adopters, which produces new revenue. For consumers, it aims to improve their utility related to recommendations they receive. We use one year of listening records for 15,000+ Last.fm users to train and test the proposed recommendation model on 143 artists. Compared to collaborative filtering, the results show some improvement in recommendation performance by considering both sides' value in conjunction with other factors, including time, location, external information and listening behavior.

In modern financial markets, decision-makers increasingly rely on quantitative methods to navigate complex trade-offs among multiple, often conflicting objectives. This paper addresses constrained multi-objective optimization (MOO) with an application to portfolio optimization for minimizing risk and maximizing return. To this end, and to address existing gaps, we propose a novel reinforcement learning (RL)-guided non-dominated sorting genetic algorithm II (NSGA-II) enhanced with gray relational coefficients (GRC), termed RL-NSGA-II-GRC, which combines an RL agent controller and GRC-based selection to improve the convergence and diversity of the Pareto-optimal fronts. The agent adapts key evolutionary parameters online using population-level metrics of hypervolume, feasibility, and diversity, while the GRC-enhanced tournament operator ranks parents via a unified score simultaneously considering dominance rank, crowding distance, and geometric proximity to ideal reference. We evaluate the framework on the Kursawe and CONSTR benchmark problems and on a NASDAQ portfolio optimization application. On the benchmarks, RL-NSGA-II-GRC achieves convergence metric improvements of about 5.8% and 4.4% over the original NSGA-II, while preserving a well-distributed set of non-dominated solutions. In the portfolio application, the method produces a smooth and densely populated efficient frontier that supports the identification of the maximum Sharpe ratio portfolio (with annualized Sharpe ratio = 1.92), as well as utility-optimal portfolios for different risk-aversion levels. The main contributions of this work are three-fold: (1) we propose an RL-NSGA-II-GRC method that integrates an RL agent into the evolutionary framework to adaptively control key parameters using generational feedback; (2) we design a GRC-enhanced binary tournament selection operator that provides a comprehensive performance indicator to efficiently guide the search toward the Pareto-optimal front; (3) we demonstrate, on benchmark MOO problems and a NASDAQ portfolio case study, that the proposed method delivers improved convergence and well-populated efficient frontiers that support actionable investment insights.

This study builds upon previous research by exploring the deeper relationship between the thematic and emotional content of top-charting music lyrics and stock price movements. Focusing on the Japan Nikkei Stock Average, this chapter investigate how thematic and emotional expressions in popular music lyrics correlate with stock market returns. Utilizing a Stream-Weighted Music Lyrics (SWML) vector as input for a Long Short-Term Memory (LSTM) model, this study forecast stock returns based on the semantic content of lyrics. This approach offers potential advancements in both theoretical understanding and practical applications for market participants. The findings make a significant contribution to the field of behavioral finance by introducing music consumption as a valuable, unconventional indicator of investor psychological states and market dynamics. The study highlights the potential of integrating psychological signals into investment strategies, offering a fresh perspective on how non-traditional data sources can enhance market analysis and decision-making.