scholarly journals INFORMATION AND MATHEMATICAL STRUCTURES CONTAINED IN THE NATURAL LANGUAGE WORD DOMAINS AND THEIR APPLICATIONS

2021 ◽  
Vol 37 (3) ◽  
pp. 239-278
Author(s):  
Nguyen Cat Ho
Keyword(s):  
Problem Solving ◽  
Natural Language ◽  
Algebraic Semantics ◽  
Control Data ◽  
Human Capabilities ◽  
Data Summarization ◽  
Mathematical Structures ◽  
Level Information ◽  
Classification And Regression ◽  
High Level

The study stands on the standpoint that there exist relationships between real-world structures and their provided information in reality. Such relationships are essential because the natural language plays a specifically vital and crucial role in, e.g., capturing, conveying information, and accumulating knowledge containing useful high-level information. Consequently, it must contain certain semantics structures, including linguistic (L-) variables’ semantic structures, which are fundamental, similar to the math variables’ structures. In this context, the fact that the (L-) variables’ word domains can be formalized as algebraic semantics-based structures in an axiomatic manner, called hedge algebras (HAs,)  is still a novel event and essential for developing computational methods to simulate the human capabilities in problem-solving based on the so-called natural language-based formalism. Hedge algebras were founded in 1990. Since then, HA-formalism has been significantly developed and applied to solve several application problems in many distinct fields, such as fuzzy control, data classification and regression, robotics, L-time series forecasting, and L-data summarization. The study gives a survey to summarize specific distinguishing fundamental features of HA-formalism, its applicability in problem-solving, and its performance. 

Spoken Natural Language Dialog Systems

1995 ◽  
Author(s):  
Ronnie W. Smith ◽  
D. Richard Hipp
Keyword(s):  
Problem Solving ◽  
Natural Language ◽  
Error Correction ◽  
User Model ◽  
Dialog Systems ◽  
Problem Solving Process ◽  
Processing Architecture ◽  
Dialog Processing

As spoken natural language dialog systems technology continues to make great strides, numerous issues regarding dialog processing still need to be resolved. This book presents an exciting new dialog processing architecture that allows for a number of behaviors required for effective human-machine interactions, including: problem-solving to help the user carry out a task, coherent subdialog movement during the problem-solving process, user model usage, expectation usage for contextual interpretation and error correction, and variable initiative behavior for interacting with users of differing expertise. The book also details how different dialog problems in processing can be handled simultaneously, and provides instructions and in-depth result from pertinent experiments. Researchers and professionals in natural language systems will find this important new book an invaluable addition to their libraries.

scholarly journals Natural Language Description of Videos for Smart Surveillance

2021 ◽  
Vol 11 (9) ◽  
pp. 3730
Author(s):  
Aniqa Dilawari ◽  
Muhammad Usman Ghani Khan ◽  
Yasser D. Al-Otaibi ◽  
Zahoor-ur Rehman ◽  
Atta-ur Rahman ◽  
...  
Keyword(s):  
Natural Language ◽  
Feature Recognition ◽  
Scene Recognition ◽  
Video Data ◽  
Surveillance Video ◽  
Video Footage ◽  
Parallel Pipeline ◽  
September 11 Attacks ◽  
Description Framework ◽  
High Level

After the September 11 attacks, security and surveillance measures have changed across the globe. Now, surveillance cameras are installed almost everywhere to monitor video footage. Though quite handy, these cameras produce videos in a massive size and volume. The major challenge faced by security agencies is the effort of analyzing the surveillance video data collected and generated daily. Problems related to these videos are twofold: (1) understanding the contents of video streams, and (2) conversion of the video contents to condensed formats, such as textual interpretations and summaries, to save storage space. In this paper, we have proposed a video description framework on a surveillance dataset. This framework is based on the multitask learning of high-level features (HLFs) using a convolutional neural network (CNN) and natural language generation (NLG) through bidirectional recurrent networks. For each specific task, a parallel pipeline is derived from the base visual geometry group (VGG)-16 model. Tasks include scene recognition, action recognition, object recognition and human face specific feature recognition. Experimental results on the TRECViD, UET Video Surveillance (UETVS) and AGRIINTRUSION datasets depict that the model outperforms state-of-the-art methods by a METEOR (Metric for Evaluation of Translation with Explicit ORdering) score of 33.9%, 34.3%, and 31.2%, respectively. Our results show that our framework has distinct advantages over traditional rule-based models for the recognition and generation of natural language descriptions.

scholarly journals Inference of Mood State Indices by Using a Multimodal High-Level Information Fusion Technique

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chih-Hua Tai ◽  
Kuo-Hsuan Chung ◽  
Ya-Wen Teng ◽  
Feng-Ming Shu ◽  
Yue-Shan Chang
Keyword(s):  
Information Fusion ◽  
Mood State ◽  
Fusion Technique ◽  
Level Information ◽  
Information Fusion Technique ◽  
High Level

Problem solving methods and knowledge systems: A personal journey to perceptual images as knowledge

2009 ◽  
Vol 23 (4) ◽  
pp. 331-338
Author(s):  
B. Chandrasekaran
Keyword(s):  
Problem Solving ◽  
Cognitive Architecture ◽  
Mental Image ◽  
Spatial Relations ◽  
Point Of View ◽  
Design Strategies ◽  
Symbolic Form ◽  
Knowledge Based ◽  
High Level ◽  
Use Of Knowledge

AbstractI was among those who proposed problem solving methods (PSMs) in the late 1970s and early 1980s as a knowledge-level description of strategies useful in building knowledge-based systems. This paper summarizes the evolution of my ideas in the last two decades. I start with a review of the original ideas. From an artificial intelligence (AI) point of view, it is not PSMs as such, which are essentially high-level design strategies for computation, that are interesting, but PSMs associated with tasks that have a relation to AI and cognition. They are also interesting with respect to cognitive architecture proposals such as Soar and ACT-R: PSMs are observed regularities in the use of knowledge that an exclusive focus on the architecture level might miss, the latter providing no vocabulary to talk about these regularities. PSMs in the original conception are closely connected to a specific view of knowledge: symbolic expressions represented in a repository and retrieved as needed. I join critics of this view, and maintain with them that most often knowledge is not retrieved from a base as much as constructed as needed. This criticism, however, raises the question of what is in memory that is not knowledge as traditionally conceived in AI, but can support theconstructionof knowledge in predicate–symbolic form. My recent proposal about cognition and multimodality offers a possible answer. In this view, much of memory consists of perceptual and kinesthetic images, which can be recalled during deliberation and from which internal perception can generate linguistic–symbolic knowledge. For example, from a mental image of a configuration of objects, numerous sentences can be constructed describing spatial relations between the objects. My work on diagrammatic reasoning is an implemented example of how this might work. These internal perceptions on imagistic representations are a new kind of PSM.

scholarly journals Artificial General Intelligence: A New Perspective, with Application to Scientific Discovery

2019 ◽  
Author(s):  
A. M. Khalili
Keyword(s):  
Problem Solving ◽  
Scientific Discovery ◽  
General Intelligence ◽  
Discovery Process ◽  
Artificial General Intelligence ◽  
Alternative Perspective ◽  
New Knowledge ◽  
New Perspective ◽  
High Level ◽  
Open The Doors

The dream of building machines that have human-level intelligence has inspired scientists for decades. Remarkable advances have been made recently; however, we are still far from achieving this goal. In this paper, I propose an alternative perspective on how these machines might be built focusing on the scientific discovery process which represents one of our highest abilities that requires a high level of reasoning and remarkable problem-solving ability. By trying to replicate the procedures followed by many scientists, the basic idea of the proposed approach is to use a set of principles to solve problems and discover new knowledge. These principles are extracted from different historical examples of scientific discoveries. Building machines that fully incorporate these principles in an automated way might open the doors for many advancements.

scholarly journals Solving Probability Problems in Natural Language

2017 ◽  
Author(s):  
Anton Dries ◽  
Angelika Kimmig ◽  
Jesse Davis ◽  
Vaishak Belle ◽  
Luc de Raedt
Keyword(s):  
Natural Language ◽  
Discrete Mathematics ◽  
Second Step ◽  
Programming System ◽  
Mathematics Textbooks ◽  
Intellectual Skill ◽  
Correct Model ◽  
Level Model ◽  
End To End ◽  
High Level

The ability to solve probability word problems such as those found in introductory discrete mathematics textbooks, is an important cognitive and intellectual skill. In this paper, we develop a two-step end-to-end fully automated approach for solving such questions that is able to automatically provide answers to exercises about probability formulated in natural language.In the first step, a question formulated in natural language is analysed and transformed into a high-level model specified in a declarative language. In the second step, a solution to the high-level model is computed using a probabilistic programming system. On a dataset of 2160 probability problems, our solver is able to correctly answer 97.5% of the questions given a correct model. On the end-to-end evaluation, we are able to answer 12.5% of the questions (or 31.1% if we exclude examples not supported by design).

scholarly journals Next Generation European Research Vessels: Current Status and Foreseeable Evolution

2019 ◽  
Author(s):  
Per Nieuwejaar ◽  
Valerie Mazauric ◽  
Christian Betzler ◽  
Mafalda Carapuco ◽  
Andre Cattrijsse ◽  
...  
Keyword(s):  
Position Paper ◽  
Research Vessel ◽  
Current Status ◽  
Marine Science ◽  
European Research ◽  
Human Capabilities ◽  
Policy Makers ◽  
The Future ◽  
Science Community ◽  
High Level

This position paper provides a review of the current European research vessel fleet, its capabilities and equipment, assessing its ability to support marine science across the globe now and into the future. It particularly looks at current and future capabilities in the context of deep sea and Polar research. It also takes a wider vision, assessing the importance of these vessels in the ocean and earth observing landscape. This review includes not only technological but also human capabilities, looking at training needs for crew and technicians to ensure they can continue to deliver on critical science needs. It also considers the ways in which the current European fleet is managed.This Position Paper sets out recommendations for how the fleet will need to develop in the future to ensure that it will continue to provide the same high level of support to science globally, as well as highlighting ways in which management could be made more efficient. It is aimed at national- and European-level policy makers and funders, as well as the marine science community and the research vessel operator community.

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