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In the fast-evolving FinTech sector, data quality and accuracy are non-negotiable. High-quality data is fundamental to informed decision-making, regulatory compliance, and customer satisfaction. This article delves into essential strategies for maintaining data quality and accuracy in FinTech, ensuring firms can thrive in a competitive landscape.   Define Data Quality Standards To begin with, FinTech companies must establish explicit criteria for data accuracy, completeness, consistency, and timeliness. Leveraging industry standards such as DAMA DMBOK (Data Management Body of Knowledge) and ISO 8000 ensures a robust framework for evaluating and maintaining data quality. These standards provide comprehensive guidelines that help organizations define…

arXiv:2405.19376v1 Announce Type: new Abstract: Data poisoning attacks pose a significant threat to the integrity of machine learning models by leading to misclassification of target distribution test data by injecting adversarial examples during training. Existing state-of-the-art (SoTA) defense methods suffer from a variety of limitations, such as significantly reduced generalization performance, specificity to particular attack types and classifiers, and significant overhead during training, making them impractical or limited for real-world applications. In response to this challenge, we introduce a universal data purification method that defends naturally trained classifiers from malicious white-, gray-, and black-box image poisons by applying a universal…

arXiv:2405.19387v1 Announce Type: new Abstract: Video anomaly detection (VAD) holds immense importance across diverse domains such as surveillance, healthcare, and environmental monitoring. While numerous surveys focus on conventional VAD methods, they often lack depth in exploring specific approaches and emerging trends. This survey explores deep learning-based VAD, expanding beyond traditional supervised training paradigms to encompass emerging weakly supervised, self-supervised, and unsupervised approaches. A prominent feature of this review is the investigation of core challenges within the VAD paradigms including large-scale datasets, features extraction, learning methods, loss functions, regularization, and anomaly score prediction. Moreover, this review also investigates the vision language…

arXiv:2405.19425v1 Announce Type: new Abstract: Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks, while the effective design of multiple agents for a particular application remains an art. It is thus intriguing to answer a critical question: Given a task, how can we build a team of LLM agents to solve it effectively? Our new adaptive team-building paradigm offers a flexible solution, realized through a novel agent design named Captain Agent. It dynamically forms and manages teams for each step of a task-solving process, utilizing nested group conversations and reflection to…

arXiv:2405.18515v1 Announce Type: new Abstract: Existing diffusion-based text-to-3D generation methods primarily focus on producing visually realistic shapes and appearances, often neglecting the physical constraints necessary for downstream tasks. Generated models frequently fail to maintain balance when placed in physics-based simulations or 3D printed. This balance is crucial for satisfying user design intentions in interactive gaming, embodied AI, and robotics, where stable models are needed for reliable interaction. Additionally, stable models ensure that 3D-printed objects, such as figurines for home decoration, can stand on their own without requiring additional supports. To fill this gap, we introduce Atlas3D, an automatic and easy-to-implement…

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol

(Andrey Suslov/Shutterstock) Pretrained large language models (LLMs) like GPT-4 and Gemini are great, but real competitive advantage comes from combining LLMs with private data. Unfortunately, there are questions sa to how well companies have prepared their private data estates for GenAI, according to a new report from MIT Technology Review. There’s no doubt that generative AI has caught the attention of organizations, who are eager to use LLMs to build chatbots, copilots, and other types of applications. Scaling AI or GenAI is a “top priority” for 82% of the executives surveyed for MIT Technology Review’s report, which is titled “AI…

Whether you are working on a live title, pre/post production, ongoing maintenance, future releases, another version of a game, or a brand new title for the market, you're always looking for feedback from the community. There's no shortage of it out there, but it can be overwhelming and hard to sift through. For games shipped on PC and sold through Valve's Steam Store, a great source of player feedback for your title can be found in Steam's game reviews. We have built a new solution accelerator for Player Review Analysis that combines natural languages and machine learning techniques to help…

arXiv:2405.18512v1 Announce Type: new Abstract: Which transformer scaling regimes are able to perfectly solve different classes of algorithmic problems? While tremendous empirical advances have been attained by transformer-based neural networks, a theoretical understanding of their algorithmic reasoning capabilities in realistic parameter regimes is lacking. We investigate this question in terms of the network's depth, width, and number of extra tokens for algorithm execution. Our novel representational hierarchy separates 9 algorithmic reasoning problems into classes solvable by transformers in different realistic parameter scaling regimes. We prove that logarithmic depth is necessary and sufficient for tasks like graph connectivity, while single-layer transformers…