In the contemporary data-driven landscape, ensuring data quality (DQ) is crucial for deriving actionable insights from vast data repositories. The objective of this study is to explore the potential for automating data quality management within data warehouses as data repository commonly used by large organizations. By conducting a systematic review of existing DQ tools available in the market and academic literature, the study assesses their capability to automatically detect and enforce data quality rules. The review encompassed 151 tools from various sources, revealing that most current tools focus on data cleansing and fixing in domain-specific databases rather than data warehouses. Only a limited number of tools, specifically ten, demonstrated the capability to detect DQ rules, not to mention implementing this in data warehouses. The findings underscore a significant gap in the market and academic research regarding AI-augmented DQ rule detection in data warehouses. This paper advocates for further development in this area to enhance the efficiency of DQ management processes, reduce human workload, and lower costs. The study highlights the necessity of advanced tools for automated DQ rule detection, paving the way for improved practices in data quality management tailored to data warehouse environments. The study can guide organizations in selecting data quality tool that would meet their requirements most.

As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources (e.g., geographical, traffic, social media, and environmental data) and modalities (e.g., spatio-temporal, visual, and textual modalities). Recently, we are witnessing a rising trend that utilizes various deep-learning methods to facilitate cross-domain data fusion in smart cities. To this end, we propose the first survey that systematically reviews the latest advancements in deep learning-based data fusion methods tailored for urban computing. Specifically, we first delve into data perspective to comprehend the role of each modality and data source. Secondly, we classify the methodology into four primary categories: feature-based, alignment-based, contrast-based, and generation-based fusion methods. Thirdly, we further categorize multi-modal urban applications into seven types: urban planning, transportation, economy, public safety, society, environment, and energy. Compared with previous surveys, we focus more on the synergy of deep learning methods with urban computing applications. Furthermore, we shed light on the interplay between Large Language Models (LLMs) and urban computing, postulating future research directions that could revolutionize the field. We firmly believe that the taxonomy, progress, and prospects delineated in our survey stand poised to significantly enrich the research community. The summary of the comprehensive and up-to-date paper list can be found at https://github.com/yoshall/Awesome-Multimodal-Urban-Computing.

Multi-modal fusion is crucial in medical data research, enabling a comprehensive understanding of diseases and improving diagnostic performance by combining diverse modalities. However, multi-modal fusion faces challenges, including capturing interactions between modalities, addressing missing modalities, handling erroneous modal information, and ensuring interpretability. Many existing researchers tend to design different solutions for these problems, often overlooking the commonalities among them. This paper proposes a novel multi-modal fusion framework that achieves adaptive adjustment over the weights of each modality by introducing the Modal-Domain Attention (MDA). It aims to facilitate the fusion of multi-modal information while allowing for the inclusion of missing modalities or intrinsic noise, thereby enhancing the representation of multi-modal data. We provide visualizations of accuracy changes and MDA weights by observing the process of modal fusion, offering a comprehensive analysis of its interpretability. Extensive experiments on various gastrointestinal disease benchmarks, the proposed MDA maintains high accuracy even in the presence of missing modalities and intrinsic noise. One thing worth mentioning is that the visualization of MDA is highly consistent with the conclusions of existing clinical studies on the dependence of different diseases on various modalities. Code and dataset will be made available.

Traffic incidents involving vulnerable road users (VRUs) constitute a significant proportion of global road accidents. Advances in traffic communication ecosystems, coupled with sophisticated signal processing and machine learning techniques, have facilitated the utilization of data from diverse sensors. Despite these advancements and the availability of extensive datasets, substantial progress is required to mitigate traffic casualties. This paper provides a comprehensive survey of state-of-the-art technologies and methodologies to enhance the safety of VRUs. The study delves into the communication networks between vehicles and VRUs, emphasizing the integration of advanced sensors and the availability of relevant datasets. It explores preprocessing techniques and data fusion methods to enhance sensor data quality. Furthermore, our study assesses critical simulation environments essential for developing and testing VRU safety systems. Our research also highlights recent advances in VRU detection and classification algorithms, addressing challenges such as variable environmental conditions. Additionally, we cover cutting-edge research in predicting VRU intentions and behaviors, which is crucial for proactive collision avoidance strategies. Through this survey, we aim to provide a comprehensive understanding of the current landscape of VRU safety technologies, identifying areas of progress and areas needing further research and development.

Deep model fusion is an emerging technique that unifies the predictions or parameters of several deep neural networks into a single model in a cost-effective and data-efficient manner. This enables the unified model to take advantage of the original models' strengths, potentially exceeding their performance. Although a variety of deep model fusion techniques have been introduced, their evaluations tend to be inconsistent and often inadequate to validate their effectiveness and robustness against distribution shifts. To address this issue, we introduce FusionBench, which is the first comprehensive benchmark dedicated to deep model fusion. FusionBench covers a wide range of tasks, including open-vocabulary image classification, text classification, and text-to-text generation. Each category includes up to eight tasks with corresponding task-specific models, featuring both full fine-tuning and LoRA fine-tuning, as well as models of different sizes, to ensure fair and balanced comparisons of various multi-task model fusion techniques across different tasks, model scales, and fine-tuning strategies. We implement and evaluate a broad spectrum of deep model fusion techniques. These techniques range from model ensemble methods, which combine the predictions to improve the overall performance, to model merging, which integrates different models into a single one, and model mixing methods, which upscale or recombine the components of the original models. FusionBench now contains 26 distinct tasks, 74 fine-tuned models, and 16 fusion techniques, and we are committed to consistently expanding the benchmark with more tasks, models, and fusion techniques. In addition, we offer a well-documented set of resources and guidelines to aid researchers in understanding and replicating the benchmark results. Homepage https://github.com/tanganke/fusion_bench

Currently, a variety of pipeline tools are available for use in data engineering. Data scientists can use these tools to resolve data wrangling issues associated with data and accomplish some data engineering tasks from data ingestion through data preparation to utilization as input for machine learning (ML). Some of these tools have essential built-in components or can be combined with other tools to perform desired data engineering operations. While some tools are wholly or partly commercial, several open-source tools are available to perform expert-level data engineering tasks. This survey examines the broad categories and examples of pipeline tools based on their design and data engineering intentions. These categories are Extract Transform Load/Extract Load Transform (ETL/ELT), pipelines for Data Integration, Ingestion, and Transformation, Data Pipeline Orchestration and Workflow Management, and Machine Learning Pipelines. The survey also provides a broad outline of the utilization with examples within these broad groups and finally, a discussion is presented with case studies indicating the usage of pipeline tools for data engineering. The studies present some first-user application experiences with sample data, some complexities of the applied pipeline, and a summary note of approaches to using these tools to prepare data for machine learning.

Text data is commonly utilized as a primary input to enhance Speech Emotion Recognition (SER) performance and reliability. However, the reliance on human-transcribed text in most studies impedes the development of practical SER systems, creating a gap between in-lab research and real-world scenarios where Automatic Speech Recognition (ASR) serves as the text source. Hence, this study benchmarks SER performance using ASR transcripts with varying Word Error Rates (WERs) on well-known corpora: IEMOCAP, CMU-MOSI, and MSP-Podcast. Our evaluation includes text-only and bimodal SER with diverse fusion techniques, aiming for a comprehensive analysis that uncovers novel findings and challenges faced by current SER research. Additionally, we propose a unified ASR error-robust framework integrating ASR error correction and modality-gated fusion, achieving lower WER and higher SER results compared to the best-performing ASR transcript. This research is expected to provide insights into SER with ASR assistance, especially for real-world applications.

Multimodal learning, integrating histology images and genomics, promises to enhance precision oncology with comprehensive views at microscopic and molecular levels. However, existing methods may not sufficiently model the shared or complementary information for more effective integration. In this study, we introduce a Unified Modeling Enhanced Multimodal Learning (UMEML) framework that employs a hierarchical attention structure to effectively leverage shared and complementary features of both modalities of histology and genomics. Specifically, to mitigate unimodal bias from modality imbalance, we utilize a query-based cross-attention mechanism for prototype clustering in the pathology encoder. Our prototype assignment and modularity strategy are designed to align shared features and minimizes modality gaps. An additional registration mechanism with learnable tokens is introduced to enhance cross-modal feature integration and robustness in multimodal unified modeling. Our experiments demonstrate that our method surpasses previous state-of-the-art approaches in glioma diagnosis and prognosis tasks, underscoring its superiority in precision neuro-Oncology.

Cancer clinics capture disease data at various scales, from genetic to organ level. Current bioinformatic methods struggle to handle the heterogeneous nature of this data, especially with missing modalities. We propose PARADIGM, a Graph Neural Network (GNN) framework that learns from multimodal, heterogeneous datasets to improve clinical outcome prediction. PARADIGM generates embeddings from multi-resolution data using foundation models, aggregates them into patient-level representations, fuses them into a unified graph, and enhances performance for tasks like survival analysis. We train GNNs on pan-Squamous Cell Carcinomas and validate our approach on Moffitt Cancer Center lung SCC data. Multimodal GNN outperforms other models in patient survival prediction. Converging individual data modalities across varying scales provides a more insightful disease view. Our solution aims to understand the patient's circumstances comprehensively, offering insights on heterogeneous data integration and the benefits of converging maximum data views.

SeeFar is an evolving collection of multi-resolution satellite images from public and commercial satellites. We specifically curated this dataset for training geospatial foundation models, unconstrained by satellite type. In recent years, advances in technology have made satellite imagery more accessible than ever. More earth-observing satellites have been launched in the last five years than in the previous fifty. Modern commercial satellites now offer up to 100 times the spatial resolution of public access satellites. However, the high cost and limited historical availability of commercial satellite imagery is a barrier to the training of foundational models, impacting what images can be used during inference. The SeeFar dataset represents a step towards training models that are satellite-agnostic by combining multi-resolution commercial and public access pre-processed images. This will enable users to utilize historical data alongside higher-resolution, more expensive satellite imagery, offering greater flexibility during inference. To achieve this, we describe a process for standardizing data from diverse satellite sources, normalizing different data formats, and aligning spectral bands to enhance interoperability. The SeeFar dataset includes images at a resolution of 384x384 pixels, spanning four spectral bands (Blue, Green, Red, and Near-Infrared) and expanding spatial resolutions (starting with 30, 10, 1.5, and 1.0 meters), all in cloud-optimized GeoTIFF format. It also provides consistent and comprehensive metadata to enhance data transparency and reliability. By aggregating data from multiple sources, SeeFar makes processed and consistent satellite data accessible to a wider range of users - from researchers to policymakers - fostering competition and innovation in satellite imagery analysis. The dataset is available at \url{coastalcarbon.ai/seefar}.