The evolution of online social media has reshaped public behaviors, and social data infused with emotions provides crucial decision support for sentiment analysis tasks. Conventional multimodal approaches, influenced by redundant information in feature extraction during sentiment analysis, often focus solely on modality interactions without considering the unique content of each modality. This paper introduces the HGB-HFN model, addressing feature redundancy by optimizing extracted features using metaheuristic algorithm techniques, achieving a higher accuracy optimal feature dataset at lower computational costs. To counteract the neglect of modal-independent information, a hybrid fusion approach is adopted. It prioritizes visual features to extract precise semantic and emotional information from textual content, training multiple base classifiers to learn independent and diverse discriminative information. Comparative experiments on streaming datasets MOSI and MOSEI demonstrate the computational approach’s superiority in both accuracy and efficiency over existing methods.

The response time of emergency vehicles (EV) is critical to saving lives. In this paper, we address the two challenges to the reduction of EV response time: 1) path-searching for a reliable estimated time of arrival (ETA) in ever-changing traffic conditions and 2) elastic signal preemption to reduce the negative impact on the whole traffic flow introduced by prioritizing the EVs. While traditional path-searching methods aim to minimize only the mean value of ETA, our approach minimizes a combination of its mean and variance, as we found that the variance of speed has a significant impact on path searching. The simulation shows that our approach generates paths with reliable ETA, i.e., lower variance with the same level of accuracy. Furthermore, we formulate the elastic signal preemption (ESP) as a set of quadratic programming (QP) problems to find non-intrusive signal schedules to the fast track EVs through junctions without stopping. When the ESP module was applied in the real world, results showed about 30% reduction of response time with little impact on the overall traffic conditions.

Traffic signal is the key method for city traffic control. Existing signal control systems use the loop detector data as the main input which is nearsighted in terms of both space and time. Lacking of effective data collection methods has hindered the development of more sophisticated models. It is therefore very hard to develop an optimization model considering all signals in a region or even a city. In this paper, we will introduce our method for large scale traffic signal optimization, which is the major module of Alibaba's city brain solution. By integrating multiple data sources to sense the whole city's traffic conditions, a layered model is developed based on the divide-and-conquer paradigm to gradually apply different types of data-driven optimization algorithms. It is a paradigm shift to use big data to improve a traditionally closed signal control system, and its effectiveness has been proven in a field test in Shanghai city.

Relevance feedback is a powerful technique to enhance Content-Based Image Retrieval (CBIR) performance. It solicits the user’s relevance judgments on the retrieved images returned by the CBIR systems. The user’s labeling is then used to learn a classifier to distinguish between relevant and irrelevant images. However, the top returned images may not be the most informative ones. The challenge is thus to determine which unlabeled images would be the most informative (i.e., improve the classifier the most) if they were labeled and used as training samples. In this paper, we propose a novel active learning algorithm, called Laplacian Optimal Design (LOD), for relevance feedback image retrieval. Our algorithm is based on a regression model which minimizes the least square error on the measured (or, labeled) images and simultaneously preserves the local geometrical structure of the image space. Specifically, we assume that if two images are sufficiently close to each other, then their measurements (or, labels) are close as well. By constructing a nearest neighbor graph, the geometrical structure of the image space can be described by the graph Laplacian. We discuss how results from the field of optimal experimental design may be used to guide our selection of a subset of images, which gives us the most amount of information. Experimental results on Corel database suggest that the proposed approach achieves higher precision in relevance feedback image retrieval.

Recently, several manifold learning algorithms have been proposed, such as ISOMAP (Tenenbaum et al., 2000), Locally Linear Embedding (Roweis & Saul, 2000), Laplacian Eigenmap (Belkin & Niyogi, 2001), Locality Preserving Projection (LPP) (He & Niyogi, 2003), etc. All of them aim at discovering the meaningful low dimensional structure of the data space. In this paper, we present a statistical analysis of the LPP algorithm. Different from Principal Component Analysis (PCA) which obtains a subspace spanned by the largest eigenvectors of the global covariance matrix, we show that LPP obtains a subspace spanned by the smallest eigenvectors of the local covariance matrix. We applied PCA and LPP to real world document clustering task. Experimental results show that the performance can be significantly improved in the subspace, and especially LPP works much better than PCA.