technocracy

Been working on a sim to train multiple robots some task. I tried version 5.1 but it has some very annoying flickering, version 4.5 is the most stable for now, but the installation (pip based) gets corrupted with torch dll errors, whenever I try something depending on the python API. I am sure a reinstall will fix it, but atp it’s like carrying a dynamite in a pocket. I have already reinstalled it 10 times. Is the ubuntu […]

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With experience I can say that all the AI usage in Test Automation relies mostly on the Machine Learning techniques and prediction based analytics to identify bugs or security risks, experience from the previous test runs to improve the problem detection and even generate test scenarios automatically. Here I will try to simplify it further so that maximum Testers can understand the enhancements AI can offer in Automation and implement it in real time scenarios. AI implementation in […]

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arXiv:2602.06054v1 Announce Type: new Abstract: Assessing research novelty is a core yet highly subjective aspect of peer review, typically based on implicit judgment and incomplete comparison to prior work. We introduce a literature-aware novelty assessment framework that explicitly learns how humans judge novelty from peer-review reports and grounds these judgments in structured comparison to existing research. Using nearly 80K novelty-annotated reviews from top-tier AI conferences, we fine-tune a large language model to capture reviewer-aligned novelty evaluation behavior. For […]

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arXiv:2407.11735v2 Announce Type: replace-cross Abstract: In open-set semi-supervised learning (OSSL), we consider unlabeled datasets that may contain unknown classes. Existing OSSL methods often use the softmax confidence for classifying data as in-distribution (ID) or out-of-distribution (OOD). Additionally, many works for OSSL rely on ad-hoc thresholds for ID/OOD classification, without considering the statistics of the problem. We propose a new score for ID/OOD classification based on angles in feature space between data and an ID subspace. Moreover, we propose […]

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Suppose I give you a big number F and claim that F is a Fibonacci number. How could you confirm this? Before I go further, let me say what this post is really about. It’s not about Fibonacci numbers so much as it is about proofs and certificates. There’s no market for large Fibonacci numbers, and certainly no need to quickly verify that a number is a Fibonacci number. You could write a program to generate Fibonacci numbers, and run […]

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arXiv:2407.11518v2 Announce Type: replace Abstract: In this paper, we present a new ensemble-based filter method by reconstructing the analysis step of the particle filter through a transport map, which directly transports prior particles to posterior particles. The transport map is constructed through an optimization problem described by the Maximum Mean Discrepancy loss function, which matches the expectation information of the approximated posterior and reference posterior. The proposed method inherits the accurate estimation of the posterior distribution from particle […]

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arXiv:2602.15863v1 Announce Type: new Abstract: Recent studies have shown that Large Language Models (LLMs) can improve their reasoning performance through self-generated few-shot examples, achieving results comparable to manually curated in-context examples. However, the underlying mechanism behind these gains remains unclear, making it hard to decide when and how to apply the technique effectively. In this work, we argue that the key benefit arises not from the generated examples themselves but from the act of creating them. To validate […]

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This final article in the series *Model evaluation, model selection, and algorithm selection in machine learning* presents overviews of several statistical hypothesis testing approaches, with applications to machine learning model and algorithm comparisons. This includes statistical tests based on target predictions for independent test sets (the downsides of using a single test set for model comparisons was discussed in previous articles) as well as methods for algorithm comparisons by fitting and evaluating models via cross-validation. Lastly, this article […]

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Object detection has compelling applications over a range of domains, including human-computer interfaces, security and video surveillance, navigation and road traffic monitoring, transportation systems, industrial automation healthcare, the world of Augmented Reality (AR) and Virtual Reality (VR), environment monitoring and activity identification. Applications of real time object detection in all these areas provide dynamic analysis of the visual information that helps in immediate decision making. Furthermore, advanced deep learning algorithms leverage the progress in the field of object […]

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