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Fooling SHAP with Output Shuffling Attacks
14 Aug

Fooling SHAP with Output Shuffling Attacks

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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
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Wavelet based inpainting detection
14 Aug

Wavelet based inpainting detection

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arXiv:2408.06429v1 Announce Type: new Abstract: With the advancement in image editing tools, manipulating digital images has become alarmingly easy. Inpainting, which is used to remove objects or fill in parts of an image, serves as a powerful tool for both image restoration and forgery. This paper introduces a novel approach for detecting image inpainting forgeries by combining DT-CWT with Hierarchical Feature segmentation and with noise inconsistency analysis. The DT-CWT offers several advantages for this task, including inherent shift-invariance, which makes it robust to minor manipulations during the inpainting process, and directional selectivity, which helps capture subtle artifacts introduced by inpainting…
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Introducing the NewsPaLM MBR and QE Dataset: LLM-Generated High-Quality Parallel Data Outperforms Traditional Web-Crawled Data
14 Aug

Introducing the NewsPaLM MBR and QE Dataset: LLM-Generated High-Quality Parallel Data Outperforms Traditional Web-Crawled Data

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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
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How AI-Driven Network Monitoring is Revolutionizing AIOps
14 Aug

How AI-Driven Network Monitoring is Revolutionizing AIOps

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Introduction  Maintaining your computer network performance is vital for smooth business operations in today's fast-changing digital world. Regular network and performance monitoring of software is important, but it often does not give enough details or early warnings to handle complicated IT setups.  Moreover, there are instances where the monitoring software is incompetent to handle the data on a daily basis. This is where Artificial Intelligence for IT Operations (AIOps) comes in. It is changing the way we manage networks and performance metrics.  Brief overview of traditional network monitoring challenges  Traditional network monitoring solutions depend on fixed rules to spot problems.…
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Implicit Neural Representation For Accurate CFD Flow Field Prediction
14 Aug

Implicit Neural Representation For Accurate CFD Flow Field Prediction

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arXiv:2408.06486v1 Announce Type: new Abstract: Despite the plethora of deep learning frameworks for flow field prediction, most of them deal with flow fields on regular domains, and although the best ones can cope with irregular domains, they mostly rely on graph networks, so that real industrial applications remain currently elusive. We present a deep learning framework for 3D flow field prediction applied to blades of aircraft engine turbines and compressors. Crucially, we view any 3D field as a function from coordinates that is modeled by a neural network we call the backbone-net. It inherits the property of coordinate-based MLPs, namely…
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Using deep learning to enhance electronic service quality: Application to real estate websites
14 Aug

Using deep learning to enhance electronic service quality: Application to real estate websites

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arXiv:2408.06364v1 Announce Type: new Abstract: Electronic service quality (E-SQ) is a strategic metric for successful e-services.Among the service quality dimensions, tangibility is overlooked. However, by incorporating visuals or tangible tools, the intangible nature of e-services can be balanced. Thanks to advancements in Deep Learning for computer vision, tangible visual features can now be leveraged to enhance the browsing and searching experience of electronic services. Users usually have specific search criteria to meet, but most services will not offer flexible search filters. This research emphasizes the importance of integrating visual and descriptive features to improve the tangibility and efficiency of e-services.…
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Chain-of-Strategy Planning with LLMs: Aligning the Generation of Psychotherapy Dialogue with Strategy in Motivational Interviewing
14 Aug

Chain-of-Strategy Planning with LLMs: Aligning the Generation of Psychotherapy Dialogue with Strategy in Motivational Interviewing

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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
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Kernel Sum of Squares for Data Adapted Kernel Learning of Dynamical Systems from Data: A global optimization approach
14 Aug

Kernel Sum of Squares for Data Adapted Kernel Learning of Dynamical Systems from Data: A global optimization approach

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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
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Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Description
14 Aug

Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Description

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arXiv:2408.06357v1 Announce Type: new Abstract: Zero sample learning is an effective method for data deficiency. The existing embedded zero sample learning methods only use the known classes to construct the embedded space, so there is an overfitting of the known classes in the testing process. This project uses category semantic similarity measures to classify multiple tags. This enables it to incorporate unknown classes that have the same meaning as currently known classes into the vector space when it is built. At the same time, most of the existing zero sample learning algorithms directly use the depth features of medical images…
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Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models
14 Aug

Does Liking Yellow Imply Driving a School Bus? Semantic Leakage in Language Models

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arXiv:2408.06518v1 Announce Type: new Abstract: Despite their wide adoption, the biases and unintended behaviors of language models remain poorly understood. In this paper, we identify and characterize a phenomenon never discussed before, which we call semantic leakage, where models leak irrelevant information from the prompt into the generation in unexpected ways. We propose an evaluation setting to detect semantic leakage both by humans and automatically, curate a diverse test suite for diagnosing this behavior, and measure significant semantic leakage in 13 flagship models. We also show that models exhibit semantic leakage in languages besides English and across different settings and…
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