scholarly journals An Informational Test for Random Finite Strings

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 934 ◽  
Author(s):  
Vincenzo Bonnici ◽  
Vincenzo Manca
Keyword(s):  
Natural Language ◽  
Wide Class ◽  
Quantum Measurements ◽  
Experimental Results ◽  
Real Numbers ◽  
Randomness Test ◽  
Linear Congruential Generators ◽  
Linear Congruential ◽  
Different Sources

In this paper, by extending some results of informational genomics, we present a new randomness test based on the empirical entropy of strings and some properties of the repeatability and unrepeatability of substrings of certain lengths. We give the theoretical motivations of our method and some experimental results of its application to a wide class of strings: decimal representations of real numbers, roulette outcomes, logistic maps, linear congruential generators, quantum measurements, natural language texts, and genomes. It will be evident that the evaluation of randomness resulting from our tests does not distinguish among the different sources of randomness (natural, or pseudo-casual).

scholarly journals An Informational Test for Random Finite Strings

2018 ◽  
Author(s):  
Vincenzo Bonnici ◽  
Vincenzo Manca
Keyword(s):  
Natural Language ◽  
Wide Class ◽  
Quantum Measurements ◽  
Experimental Results ◽  
Real Numbers ◽  
Randomness Test ◽  
Linear Congruential Generators ◽  
Linear Congruential ◽  
Different Sources

In this paper, by extending some results of informational genomics, we present a new randomness test based on the empirical entropy of strings and some properties of repeatability and unrepeatability of substrings of certain lengths. We give the theoretical motivations of our method and some experimental results of its application to a wide class of strings: decimal representations of real numbers, roulette outcomes, logistic maps, linear congruential generators, quantum measurements, natural language texts, and genomes. It will be evident that the evaluation of randomness resulting from our tests does not distinguish among the different sources of randomness (natural, or pseudo-casual).

A Hybrid Siamese Neural Network for Natural Language Inference in Cyber-Physical Systems

2021 ◽  
Vol 21 (2) ◽  
pp. 1-25
Author(s):  
Pin Ni ◽  
Yuming Li ◽  
Gangmin Li ◽  
Victor Chang
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Short Term Memory ◽  
Physical World ◽  
Heterogeneous Data ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Language Data ◽  
Text Language ◽  
Different Sources

Cyber-Physical Systems (CPS), as a multi-dimensional complex system that connects the physical world and the cyber world, has a strong demand for processing large amounts of heterogeneous data. These tasks also include Natural Language Inference (NLI) tasks based on text from different sources. However, the current research on natural language processing in CPS does not involve exploration in this field. Therefore, this study proposes a Siamese Network structure that combines Stacked Residual Long Short-Term Memory (bidirectional) with the Attention mechanism and Capsule Network for the NLI module in CPS, which is used to infer the relationship between text/language data from different sources. This model is mainly used to implement NLI tasks and conduct a detailed evaluation in three main NLI benchmarks as the basic semantic understanding module in CPS. Comparative experiments prove that the proposed method achieves competitive performance, has a certain generalization ability, and can balance the performance and the number of trained parameters.

scholarly journals Breakdown Detection in Negotiation Dialogues (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13969-13970
Author(s):  
Atsuki Yamaguchi ◽  
Katsuhide Fujita
Keyword(s):  
Artificial Intelligence ◽  
Natural Language ◽  
Language Model ◽  
Experimental Results ◽  
Conflicts Of Interests ◽  
Early Stages ◽  
End To End ◽  
Gated Recurrent Unit

In human-human negotiation, reaching a rational agreement can be difficult, and unfortunately, the negotiations sometimes break down because of conflicts of interests. If artificial intelligence can play a role in assisting with human-human negotiation, it can assist in avoiding negotiation breakdown, leading to a rational agreement. Therefore, this study focuses on end-to-end tasks for predicting the outcome of a negotiation dialogue in natural language. Our task is modeled using a gated recurrent unit and a pre-trained language model: BERT as the baseline. Experimental results demonstrate that the proposed tasks are feasible on two negotiation dialogue datasets, and that signs of a breakdown can be detected in the early stages using the baselines even if the models are used in a partial dialogue history.

scholarly journals Grounding Action Descriptions in Videos

2013 ◽  
Vol 1 ◽  
pp. 25-36 ◽  
Author(s):  
Michaela Regneri ◽  
Marcus Rohrbach ◽  
Dominikus Wetzel ◽  
Stefan Thater ◽  
Bernt Schiele ◽  
...  
Keyword(s):  
Natural Language ◽  
Recent Work ◽  
Visual Information ◽  
General Purpose ◽  
Experimental Results ◽  
High Quality ◽  
Improve Model ◽  
Model Predictions ◽  
Static Images

Recent work has shown that the integration of visual information into text-based models can substantially improve model predictions, but so far only visual information extracted from static images has been used. In this paper, we consider the problem of grounding sentences describing actions in visual information extracted from videos. We present a general purpose corpus that aligns high quality videos with multiple natural language descriptions of the actions portrayed in the videos, together with an annotation of how similar the action descriptions are to each other. Experimental results demonstrate that a text-based model of similarity between actions improves substantially when combined with visual information from videos depicting the described actions.

scholarly journals A Deep Paraphrase Identification Model Interacting Semantics with Syntax

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Leilei Kong ◽  
Zhongyuan Han ◽  
Yong Han ◽  
Haoliang Qi
Keyword(s):  
Neural Network ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Semantic Representation ◽  
Experimental Results ◽  
Plagiarism Detection ◽  
Linguistic Features ◽  
Syntactic Structures ◽  
Syntactic Features ◽  
Identification Model

Paraphrase identification is central to many natural language applications. Based on the insight that a successful paraphrase identification model needs to adequately capture the semantics of the language objects as well as their interactions, we present a deep paraphrase identification model interacting semantics with syntax (DPIM-ISS) for paraphrase identification. DPIM-ISS introduces the linguistic features manifested in syntactic features to produce more explicit structures and encodes the semantic representation of sentence on different syntactic structures by means of interacting semantics with syntax. Then, DPIM-ISS learns the paraphrase pattern from this representation interacting the semantics with syntax by exploiting a convolutional neural network with convolution-pooling structure. Experiments are conducted on the corpus of Microsoft Research Paraphrase (MSRP), PAN 2010 corpus, and PAN 2012 corpus for paraphrase plagiarism detection. The experimental results demonstrate that DPIM-ISS outperforms the classical word-matching approaches, the syntax-similarity approaches, the convolution neural network-based models, and some deep paraphrase identification models.

scholarly journals On lattice profile of the elliptic curve linear congruential generators

2014 ◽  
Vol 68 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Zhixiong Chen ◽  
Domingo Gomez-Perez ◽  
Gottlieb Pirsic
Keyword(s):  
Elliptic Curve ◽  
Linear Congruential Generators ◽  
Linear Congruential

Improving Text Categorization by Multicriteria Feature Selection

2005 ◽  
Vol 9 (5) ◽  
pp. 570-575
Author(s):  
Son Doan ◽  
◽  
Susumu Horiguchi ◽  
Keyword(s):  
Feature Selection ◽  
Natural Language ◽  
Text Categorization ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Experimental Results ◽  
Benchmark Data ◽  
Bayes Algorithm

Text categorization involves assigning a natural language document to one or more predefined classes. One of the most interesting issues is feature selection. We propose an approach using multicriteria ranking of eatures, a new procedure for feature selection, and apply these to text categorization. Experimental results dealing with Reuters-21578 and 20Newsgroups benchmark data and the naive Bayes algorithm show that our proposal outperforms conventional feature selection in text categorization performance.

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