The extreme rise of the Internet of Things and the increasing access of people to web applications have led to the expanding use of diverse e-commerce solutions, which was even more obvious during the COVID-19 pandemic. Large amounts of heterogeneous data from multiple sources reside in e-commerce environments and are often characterized by data source inaccuracy and unreliability. In this regard, various fusion techniques can play a crucial role in addressing such challenges and are extensively used in numerous e-commerce applications. This paper’s goal is to conduct an academic literature review of prominent fusion-based solutions that can assist in tackling the everyday challenges the e-commerce environments face as well as in their needs to make more accurate and better business decisions. For categorizing the solutions, a novel 4-fold categorization approach is introduced including product-related, economy-related, business-related, and consumer-related solutions, followed by relevant subcategorizations, based on the wide variety of challenges faced by e-commerce. Results from the 65 fusion-related solutions included in the paper show a great variety of different fusion applications, focusing on the fusion of already existing models and algorithms as well as the existence of a large number of different machine learning techniques focusing on the same e-commerce-related challenge.

While camera and LiDAR are widely used in most of the assisted and autonomous driving systems, only a few works have been proposed to associate the temporal synchronization and extrinsic calibration for camera and LiDAR which are dedicated to online sensors data fusion. The temporal and spatial calibration technologies are facing the challenges of lack of relevance and real-time. In this paper, we introduce the pose estimation model and environmental robust line features extraction to improve the relevance of data fusion and instant online ability of correction. Dynamic targets eliminating aims to seek optimal policy considering the correspondence of point cloud matching between adjacent moments. The searching optimization process aims to provide accurate parameters with both computation accuracy and efficiency. To demonstrate the benefits of this method, we evaluate it on the KITTI benchmark with ground truth value. In online experiments, our approach improves the accuracy by 38.5\% than the soft synchronization method in temporal calibration. While in spatial calibration, our approach automatically corrects disturbance errors within 0.4 second and achieves an accuracy of 0.3-degree. This work can promote the research and application of sensor fusion.

We present a new benchmark dataset, Sapsucker Woods 60 (SSW60), for advancing research on audiovisual fine-grained categorization. While our community has made great strides in fine-grained visual categorization on images, the counterparts in audio and video fine-grained categorization are relatively unexplored. To encourage advancements in this space, we have carefully constructed the SSW60 dataset to enable researchers to experiment with classifying the same set of categories in three different modalities: images, audio, and video. The dataset covers 60 species of birds and is comprised of images from existing datasets, and brand new, expert-curated audio and video datasets. We thoroughly benchmark audiovisual classification performance and modality fusion experiments through the use of state-of-the-art transformer methods. Our findings show that performance of audiovisual fusion methods is better than using exclusively image or audio based methods for the task of video classification. We also present interesting modality transfer experiments, enabled by the unique construction of SSW60 to encompass three different modalities. We hope the SSW60 dataset and accompanying baselines spur research in this fascinating area.

Modern video-text retrieval frameworks basically consist of three parts: video encoder, text encoder and the similarity head. With the success of both visual and textual representation learning, transformerbased encoders and fusion methods have also been adopted in the field of video-text retrieval. In this paper, We propose a new CLIP-based framework called CLIP2TV, which consists of a video-text alignment module and a video-text matching module. The two modules are trained end-toend in a coordinated manner, and boost the performance to each other. Moreover, to address the impairment brought by data noise, especially false negatives introduced by vague description in some datasets, we propose similarity distillation to alleviate the problem. Extensive experimental results on various datasets validate the effectiveness of the proposed methods. Finally, on common datasets of various length of video clips, CLIP2TV achieves better or competitive results towards previous SOTA methods.

Delaget, a SaaS (software as a service) company that serves restaurant operators their data analytics through seamless automation, announced their newest partnership with Instant Financial. Instant Financial is a provider of fee-free on-demand pay solutions, on a mission to help workers get out from the cycle of living paycheck to paycheck, which impacts millions of Americans today. The Instant platform offers a pay management solution and Visa debit card that allows employees to access their hard-earned wages, when they need them, and helps them get on a path to financial independence. The addition of Instant to the Delaget Marketplace creates new opportunities for both organizations, as well as enables existing clients to integrate seamlessly, bypassing the time required for custom integrations. This partnership is timely with the rise in popularity of on-demand pay and moves toward making EWA a staple of employee benefits, particularly in the QSR space.

The Data Science and Machine-Learning Platforms Market report provides information about the Global industry, including valuable facts and figures. This research study explores the Global Market in detail such as industry chain structures, raw material suppliers, with manufacturing The Data Science and Machine-Learning Platforms Sales market examines the primary segments of the scale of the market. This intelligent study provides historical data from 2015 alongside a forecast from 2022 to 2026. With the present market standards revealed, the Data Science and Machine-Learning Platforms market research report has also illustrated the latest strategic developments and patterns of the market players in an unbiased manner. The report serves as a presumptive business document that can help the purchasers in the global market plan their next courses towards the position of the market's future.

Trasys International offers IT Consulting jobs at the European Institutions and International Organizations We strive to provide the best talent to our customers, and to do that, we need enthusiastic and competent people like you. If you feel ready for the European challenge, keep reading! The services to be provided consist in maintaining and enhancing the existing system(s) using SAS software upon which the data warehouse is built and other relevant tools.

As we speak, we're thrilled to announce a local integration with Airbyte Cloud, which permits knowledge replication from any supply into Databricks for all knowledge, analytics, and ML workloads. Airbyte Cloud, a hosted service made by Airbyte, gives an integration platform that may scale along with your customized or high-volume wants, from giant databases to a long-tail of API sources. This integration with Databricks helps break down knowledge silos by letting customers replicate knowledge into the Databricks Lakehouse Vacation spot to course of, retailer, and expose knowledge all through your group. As an open supply normal for ELT, Airbyte gives greater than 150 editable pre-built connectors – or simply create new ones in a matter of hours. With a devoted Databricks connector, joint customers can sync any knowledge supply that Airbyte helps into Databricks Delta Lake.

In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Data Engineers are at the heart of the engine room of any data-driven company.