| viXra.org > Artificial Intelligence > 2012 |
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| viXra.org > Artificial Intelligence > 2012 |
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[11] viXra:2012.0224 [pdf] submitted on 2020-12-31 11:23:18
Authors: Lipeng Pan, Xiaozhuan Gao, Yong Deng
Comments: 11 Pages.
Dempster combination rule is widely used in many applications such as information fusion and decision making. However, the computational complexity of Dempster combination rule increases exponentially with the increase of frame of discernment. To address this issue, we propose the quantum algorithm of Dempster combination rule based on quantum theory. The algorithm not only realizes most of the functions of Dempster combination rule, but also effectively reduces the computational complexity of Dempster combination rule in future quantum computer. Meanwhile, we carried out a simulation experiment on the quantum cloud platform of IBM, and the experimental results showed that the algorithm is reasonable.
Category: Artificial Intelligence
[10] viXra:2012.0207 [pdf] submitted on 2020-12-28 04:20:19
Authors: Yuanpeng He, Fuyuan Xiao
Comments: 2 Pages.
To handle uncertainties and process complex in- formation from different sources, quantum mass function, an efficient method has been proposed to address this issues. On the basis of the quantum mass function, many methods has been designed to indicate the differences among quantum evidences. Nevertheless, they are developed by quantum evidence theory to process traditional basic probability assignments (QBPAs) and not applicable in measuring quaternion BPAs (QTBPAs). Therefore, in this paper, a specific customized method is proposed for the generalized form of quantum mass function, namely quaternion mass function, to accurately demonstrate the dis- tances among disparate evidences given as QTBPAs (QED). Moreover, it is a pioneer to investigate the differences between pieces of evidences in the plane space of quaternion which is reliable and strictly satisfies the axioms of distance. Besides, if QTBPAs degenerate into QBPAs, QED also degenerate into quantum evidential evidence, which indicates the consistency in this new standard of measuring distances. Consequently, QED is derived from the quantum evidential distance and possesses an extensive capability to indicate dissimilarities among QTBPAs. Several numerical examples are offered to check the validity and practical availability of QED.
Category: Artificial Intelligence
[9] viXra:2012.0142 [pdf] submitted on 2020-12-19 11:21:13
Authors: Adrià Descals, Luis Alonso, Gustau Camps-Valls
Comments: 4 Pages.
This paper introduces a methodology for predicting the year of plantation (YOP) from remote sensing data. The application has important implications in forestry management and inventorying. We exploit hyperspectral and LiDAR data in combination with state-of-the-art machine learning classi-fiers. In particular, we present a complete processing chain to extract spectral, textural and morphological features from both sensory data. Features are then combined and fed a Gaussian Process Classifier (GPC) trained to predict YOP in a forest area in North Carolina (US). The GPC algorithm provides accurate YOP estimates, reports spatially explicit maps and associated confidence maps, and provides sensible feature rankings.
Category: Artificial Intelligence
[8] viXra:2012.0141 [pdf] submitted on 2020-12-19 11:23:27
Authors: Pablo Morales, Adrián Pérez-Suay, Rafael Molina, Gustau Camps-Valls, Aggelos K. Katsaggelos
Comments: 5 Pages.
Passive Millimeter Wave Images (PMMWIs) are being increasingly used to identify and localize objects concealed under clothing. Taking into account the quality of these images and the unknown position, shape, and size of the hidden objects, large data sets are required to build successful classification/detection systems. Kernel methods, in particular Gaussian Processes (GPs), are sound, flexible, and popular techniques to address supervised learning problems. Unfortunately, their computational cost is known to be prohibitive
for large scale applications. In this work, we present a novel approach to PMMWI classification based on the use of Gaussian Processes for large data sets. The proposed methodology relies on linear approximations to kernel functions through random Fourier features. Model hyperparameters are learned within a variational Bayes inference scheme. Our proposal is well suited for real-time applications, since its computational cost at training and test times is much lower
than the original GP formulation. The proposed approach is tested on a unique, large, and real PMMWI database containing a broad variety of sizes, types, and locations of hidden objects.
Category: Artificial Intelligence
[7] viXra:2012.0092 [pdf] submitted on 2020-12-11 21:22:56
Authors: Saty Raghavachary
Comments: 10 Pages.
Regarding intelligence as a ‘considered response’ phenomenon is the key notion that is presented in this paper. Applied to human-level intelligence, it seems to be a useful definition that can lend clarity to the following related aspects as well: mind, self/I, awareness, self-awareness, consciousness, sentience, thoughts and feelings, free will, perception, attention, cognition,
expectation, prediction, learning. Also, embodiment is argued to be an essential
component of an AGI’s agent architecture, in order for it to attain grounded cognition, a sense of self and social learning - via direct physical experience and mental processes, all based on considered response.
Category: Artificial Intelligence
[6] viXra:2012.0064 [pdf] submitted on 2020-12-09 09:08:40
Authors: Junjae Lee
Comments: 8 Pages.
Invertible Rescaling Net (IRN) modeled the downscaling and up-scaling process using
Invertible Neural Networks (INN) instead of upscaling to the traditional Singleimage super resolution (SISR) method. As a result, it showed significantly improved performance than the previous method. However, apart from its high performance, IRN requires a lot of computation. hence, to improve this, we replace the existing dense block with Pixel Attention Distillation Block (PADB). In addition, we use Charbonnier loss
instead of Mean Absolute Error (MAE) for the existing reconstruction loss. Through these improvements, we trade off the high performance and speed of the existing architecture and achieve higher performance than the lightweight SR model using the conventional method. In addition, by improving the perceptual loss and adversarial loss. we achieve perceptually satisfactory results than the model using the IRN+ method.
Category: Artificial Intelligence
[5] viXra:2012.0058 [pdf] submitted on 2020-12-08 19:58:30
Authors: Ashwin Rachha, Gaurav Vanmane
Comments: 7 Pages.
The internet today has become an unrivalled source
of information where people converse on content based websites such as Quora, Reddit, StackOverflow and Twitter asking doubts and sharing knowledge with the world. A major arising problem with such websites is the proliferation of toxic comments or instances of insincerity wherein the users instead of maintaining a sincere motive indulge in spreading toxic and divisive content.
The straightforward course of action in confronting this situation is detecting such content beforehand and preventing it from
subsisting online. In recent times Transfer Learning in Natural Language Processing has seen an unprecedented growth. Today with the existence of transformers and various state of the art
innovations, a tremendous growth has been made in various NLP domains. The introduction of BERT has caused quite a stir in the NLP community. As mentioned, when published, BERT dominated performance benchmarks and thereby inspired
many other authors to experiment with it and publish similar models. This led to the development of a whole BERT-family, each member being specialized on a different task. In this paper we solve the Insincere Questions Classification problem by fine tuning four cutting age models viz BERT, RoBERTa, DistilBERT and ALBERT.
Category: Artificial Intelligence
[4] viXra:2012.0051 [pdf] submitted on 2020-12-08 09:02:26
Authors: Ramesh Chandra Bagadi
Comments: 16 Pages.
In this research investigation, the authors present a detailed scheme of a theoretical model for an approximate one step forecasting scheme. Firstly, the authors coin notions of Similarity and Dissimilarity. The authors then coin a notion of causal one step forecast for any given sequence. Parallely, the authors define concepts of Higher Order Sequence of Primes and RL Normalization Scheme based on which alternate better formulae for one step forecast for any given sequence are derived.
Category: Artificial Intelligence
[3] viXra:2012.0048 [pdf] submitted on 2020-12-08 08:11:02
Authors: Fatih Nar, Adrián Pérez-Suay, José Antonio Padrón, Gustau Camps-Valls
Comments: 4 Pages.
This work tackles the target detection problem through the well-known global RX method.
The RX method models the clutter as a multivariate Gaussian distribution, and has been extended to nonlinear distributions using kernel methods.
While the kernel RX can cope with complex clutters, it requires a considerable amount of computational resources as the number of clutter pixels gets larger.
Here we propose random Fourier features to approximate the Gaussian kernel in kernel RX and consequently our development keep the accuracy of the nonlinearity while reducing the computational cost which is now controlled by an hyperparameter.
Results over both synthetic and real-world image target detection problems show space and time efficiency of the proposed method while providing high detection performance.
Category: Artificial Intelligence
[2] viXra:2012.0025 [pdf] submitted on 2020-12-06 12:32:48
Authors: Shiyou Lian
Comments: 19 Pages.
Imprecise-information processing will play an indispensable role in intelligent systems, especially in the anthropomorphic intelligent systems (as human-machine dialogue and intelligent robots). Traditionally, the fuzzy set theory is used to deal with imprecise information, but which has some important theoretical and technical problems not solved very well. Recently, a new theoretical and technological system of imprecise-information processing has been founded (see literature [1]) which is different from fuzzy technology. The system results from the formation principle of imprecise information and has solid mathematical and logical bases, so which has many advantages beyond fuzzy technology. The system provides a technological platform for relevant applications and lays a theoretical foundation for further research.
Category: Artificial Intelligence
[1] viXra:2012.0023 [pdf] replaced on 2020-12-16 03:11:21
Authors: Saty Raghavachary, Lurong Lei
Comments: 9 Pages.
Computational modeling of natural cognition is a crucial step towards achieving the grand goal of human-level computational intelligence. Successful ideas from existing models, and possibly newer ones, could be assembled to create a unified computational framework (eg. the Standard Model of the Mind, which attempts to unify three leading cognitive architectures) - this would be of great use in AI, robotics, neuroscience and cognitive science. This short position paper proposes the following: a VR-based system provides the most expedient, scalable and visually verifiable way to implement, test and refine a cognitive mind model (which would always be embodied in a character in a virtual world). Such a setup is discussed in the paper, including advantages and drawbacks over alternative implementations.
Category: Artificial Intelligence
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