The Logarithmic-Time Logic of Knowledge


The Logarithmic-Time Logic of Knowledge – In this paper, we discuss the theory of linearity theory and formal reasoning for the construction of logic programs for symbolic languages. In particular, we propose a general framework for reasoning about symbolic programs that contains a number of axioms and an axiomogical semantics. The axioms and the axiomogical semantics are the formal foundations of logical programming used in cognitive science and is central to various natural language algorithms, including symbolic logic programs. We then review our main result and provide a few examples of the implications of this framework from natural language.

The multi-camera systems have proven successful in many challenging aspects of the visual inspection process such as: the task of detecting objects and objects’ poses in images; the task of identifying missing items in images; and the task of detecting objects that look like objects when being examined. However, due to their multiple nature of the images, each camera is different and therefore different camera models with different functionality can have different abilities and they may have different performance characteristics. In this paper, we propose a novel method for automatically recognizing objects and objects at different positions, scale and orientation in images and videos from a single camera. The concept is to automatically make use of the camera views and attributes to extract the most relevant information from the images. To this end, we use a visual segmentation based approach that takes a series of large-scale and real-time camera views to extract various object recognition features, using a spatial and spatial-temporal framework. In experiments, the proposed method is competitive with state-of-the-art object detection methods on PASCAL VOC benchmark datasets.

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The Logarithmic-Time Logic of Knowledge

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    Automatic Image Aesthetic Assessment Based on Deep Structured AttentionsThe multi-camera systems have proven successful in many challenging aspects of the visual inspection process such as: the task of detecting objects and objects’ poses in images; the task of identifying missing items in images; and the task of detecting objects that look like objects when being examined. However, due to their multiple nature of the images, each camera is different and therefore different camera models with different functionality can have different abilities and they may have different performance characteristics. In this paper, we propose a novel method for automatically recognizing objects and objects at different positions, scale and orientation in images and videos from a single camera. The concept is to automatically make use of the camera views and attributes to extract the most relevant information from the images. To this end, we use a visual segmentation based approach that takes a series of large-scale and real-time camera views to extract various object recognition features, using a spatial and spatial-temporal framework. In experiments, the proposed method is competitive with state-of-the-art object detection methods on PASCAL VOC benchmark datasets.


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