Topic
Perceptual computing
About: Perceptual computing is a research topic. Over the lifetime, 65 publications have been published within this topic receiving 7222 citations.
Papers published on a yearly basis
Papers
TL;DR: The point of this note is that fuzzy logic plays a pivotal role in CW and vice-versa and, as an approximation, fuzzy logic may be equated to CW.
Abstract: As its name suggests, computing with words (CW) is a methodology in which words are used in place of numbers for computing and reasoning. The point of this note is that fuzzy logic plays a pivotal role in CW and vice-versa. Thus, as an approximation, fuzzy logic may be equated to CW. There are two major imperatives for computing with words. First, computing with words is a necessity when the available information is too imprecise to justify the use of numbers, and second, when there is a tolerance for imprecision which can be exploited to achieve tractability, robustness, low solution cost, and better rapport with reality. Exploitation of the tolerance for imprecision is an issue of central importance in CW. In CW, a word is viewed as a label of a granule; that is, a fuzzy set of points drawn together by similarity, with the fuzzy set playing the role of a fuzzy constraint on a variable. The premises are assumed to be expressed as propositions in a natural language. In coming years, computing with words is likely to evolve into a basic methodology in its own right with wide-ranging ramifications on both basic and applied levels.
3,430 citations
TL;DR: This paper develops a computational technique for computing with words without any loss of information in the 2-tuple linguistic model and extends different classical aggregation operators to deal with this model.
Abstract: The fuzzy linguistic approach has been applied successfully to many problems. However, there is a limitation of this approach imposed by its information representation model and the computation methods used when fusion processes are performed on linguistic values. This limitation is the loss of information; this loss of information implies a lack of precision in the final results from the fusion of linguistic information. In this paper, we present tools for overcoming this limitation. The linguistic information is expressed by means of 2-tuples, which are composed of a linguistic term and a numeric value assessed in (-0.5, 0.5). This model allows a continuous representation of the linguistic information on its domain, therefore, it can represent any counting of information obtained in a aggregation process. We then develop a computational technique for computing with words without any loss of information. Finally, different classical aggregation operators are extended to deal with the 2-tuple linguistic model.
2,755 citations
TL;DR: This article discusses key AI research in semantic computing, cognitive computing, and perceptual computing expected to power future progress in building intelligent systems and applications for rapidly expanding markets in multiple industries.
Abstract: Rapid growth in the Internet of Things (IoT) has resulted in a massive growth of data generated by these devices and sensors put on the Internet. Physical-cyber-social (PCS) big data consist of this IoT data, complemented by relevant Web-based and social data of various modalities. Smart data is about exploiting this PCS big data to get deep insights and make it actionable, and making it possible to facilitate building intelligent systems and applications. This article discusses key AI research in semantic computing, cognitive computing, and perceptual computing. Their synergistic use is expected to power future progress in building intelligent systems and applications for rapidly expanding markets in multiple industries. Over the next two years, this column on IoT will explore many challenges and technologies on intelligent use and applications of IoT data.
183 citations
Patent•
AMIT1
TL;DR: In this paper, a computing device is used to detect a location to embed advertising content within media content and retrieve user profile data corresponding to a user of a computing devices to generate augmented media content for subsequent display to the user.
Abstract: Technologies for adaptively embedding an advertisement into media content via contextual analysis and perceptual computing include a computing device for detecting a location to embed advertising content within media content and retrieving user profile data corresponding to a user of a computing device. Such technologies may also include determining advertising content personalized for the user based on the retrieved user profile and embedding the advertising content personalized for the user into the media content at the detected location within the media content to generate augmented media content for subsequent display to the user.
143 citations
TL;DR: A comparative study shows that S-PR leads to output FOUs that resemble word FOU’s, which are obtained from subject data, much more closely than FI-PR; hence, S- PR is a better choice for a CWW engine thanFI-PR.
Abstract: Perceptual reasoning (PR) is an approximate reasoning method that can be used as a computing-with-words (CWW) engine in perceptual computing. There can be different approaches to implement PR, e.g., firing-interval-based PR (FI-PR), which has been proposed in J. M. Mendel and D. Wu, IEEE Trans. Fuzzy Syst., vol. 16, no. 6, pp. 1550-1564, Dec. 2008 and similarity-based PR (S-PR), which is proposed in this paper. Both approaches satisfy the requirement on a CWW engine that the result of combining fired rules should lead to a footprint of uncertainty (FOU) that resembles the three kinds of FOUs in a CWW codebook. A comparative study shows that S-PR leads to output FOUs that resemble word FOUs, which are obtained from subject data, much more closely than FI-PR; hence, S-PR is a better choice for a CWW engine than FI-PR.
73 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 3 |
| 2020 | 3 |
| 2019 | 3 |
| 2018 | 5 |
| 2017 | 3 |
| 2016 | 4 |