3Es for AI: Economics, Explanation, Epistemology

This article locates its roots/routes in multiple disciplinary formations and it seeks to advance critical thinking about an aspect of our contemporary socio-technical challenges by bracketing three knowledge formations—artificial intelligence (AI), economics, and epistemology—that have not often been considered together. In doing so, it responds to the growing calls for the necessity of further transdisciplinary engagements that have emanated from work in AI and also from other disciplines. The structure of the argument here is as follows. First, I begin by demonstrating how and why explanation is a problem in AI (“XAI problem”) and what directions are being taken by recent research that draws upon social sciences to address this, noting how there is a conspicuous lack of reference in this literature to economics. Second, I identify and analyze a problem of explanation that has long plagued economics too as a discipline. I show how only a few economists have ever attempted to grapple with this problem and provide their perspectives. Third, I provide an original genealogy of explanation in economics, demonstrating the changing nature of what was meant by an explanation. These systematic changes in consensual understanding of what occurs when something is said to have been “explained”, have reflected the methodological compromises that were rendered necessary to serve different epistemological tensions over time. Lastly, I identify the various relevant historical and conceptual overlaps between economics and AI. I conclude by suggesting that we must pay greater attention to the epistemologies underpinning socio-technical knowledges about the human. The problem of explanation in AI, like the problem of explanation in economics, is perhaps not only, or really, a problem of satisfactory explanation provision alone, but interwoven with questions of competing epistemological and ethical choices and related to the ways in which we choose sociotechnical arrangements and offer consent to be governed by them

Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem

Hashtag is an iconic feature to retrieve the hot topics of discussion on Twitter or other social networks. This paper incorporates the pattern mining approaches to improve the accuracy of retrieving the relevant information and speeding up the search performance. A novel algorithm called PM-HR (Pattern Mining for Hashtag Retrieval) is designed to first transform the set of tweets into a transactional database by considering two different strategies (trivial and temporal). After that, the set of the relevant patterns is discovered, and then used as a knowledge-based system for finding the relevant tweets based on users\u27 queries under the similarity search process. Extensive results are carried out on large and different tweet collections, and the proposed PM-HR outperforms the baseline hashtag retrieval approaches in terms of runtime, and it is very competitive in terms of accuracy

Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem

Hashtag is an iconic feature to retrieve the hot topics of discussion on Twitter or other social networks. This paper incorporates the pattern mining approaches to improve the accuracy of retrieving the relevant information and speeding up the search performance. A novel algorithm called PM-HR (Pattern Mining for Hashtag Retrieval) is designed to first transform the set of tweets into a transactional database by considering two different strategies (trivial and temporal). After that, the set of the relevant patterns is discovered, and then used as a knowledge-based system for finding the relevant tweets based on users' queries under the similarity search process. Extensive results are carried out on large and different tweet collections, and the proposed PM-HR outperforms the baseline hashtag retrieval approaches in terms of runtime, and it is very competitive in terms of accuracy.publishedVersio

Container Number Recognition Method Based on SSD_MobileNet and SVM

Aiming at how to realize the recognition of the container number on the container surface at the entrance and exit of the port, a method based on image affine transformation and SVM classifier is proposed. The main process includes truck target detection, box number area detection, text correction stage, image preprocessing stage and segmentation detection and recognition stage. Firstly, a kind of container truck detection program based on frame difference method and decreasing sequence of connected domain is proposed; secondly, a method of container number area detection based on SSD_MobileNet is proposed; in the case number recognition stage, a text correction method based on image affine transformation is proposed, and different processing methods are proposed for vertical sequence box number and horizontal sequence box number in image preprocessing stage In the stage of segmentation detection and recognition, a character segmentation algorithm based on connected domain segmentation and a segmentation detection and recognition algorithm based on SVM classifier are proposed. Through the detection and recognition of container images in the field monitoring video, the accuracy rate of regional detection can reach 97%, and the accuracy rate of character recognition can reach 95%, and it can achieve good real-time performance