???The original publication is available at www.springerlink.com???. Copyright Springer. [Full text of this article is not available in the UHRA]The Cognitive Dimensions of Notations framework has been created to assist the designers of notational systems and information artifacts to evaluate their designs with respect to the impact that they will have on the users of those designs. The framework emphasizes the design choices available to such designers, including characterization of the user???s activity, and the inevitable tradeoffs that will occur between potential design options. The resulting framework has been under development for over 10 years, and now has an active community of researchers devoted to it. This paper first introduces Cognitive Dimensions. It then summarizes the current activity, especially the results of a one-day workshop devoted to Cognitive Dimensions in December 2000, and reviews the ways in which it applies to the field of Cognitive Technology
The Cognitive Dimensions framework has inspired research both more and less varied than expected. In this paper we revisit the original aims and briefly describe some subsequent research, to consider whether the original aims were too austere in rejecting knowledge-based dimensions; whether the dimensions can be shown to have real-world relevance; and whether their definitions can be improved, either piecemeal or by refactoring the entire set. We mention some issues that remain unexplored, and conclude by describing two different ventures into defining clear procedures for real-life application, operating in very different milieux but both accepting that the framework should be developed from its original formulation.
Abstract-In a variety of emergency settings robot assistance has been identified as highly valuable, providing remote, and thus safe, access and operation. There are many different forms of human-robot interactions, allowing a team of humans and robots to take advantage of skills of each team member. A relatively new area of research considers interactions between human and a team of robots performing as a swarm.This work is concerned with the interactive use of autonomous robots in fire emergency settings. In particular, we consider a swarm of robots that are capable of supporting and enhancing fire fighting operations co-operatively and we investigate how firefighters in the field work with such a swarm.This paper outlines some of the key characteristics of this emergency setting. It discusses possible forms of interactions with swarm robotics being examined in the GUARDIANS project. The paper addresses the use of assistive swarm robotics to support firefighters with navigation and search operations. It reports on existing firefighters operations and how humanswarm interactions are to be used during such operations. The design approaches for human-swarm interaction are described and the preliminary work in the area are outlined. The paper ends by linking current expertise with common features of emergency related interaction design.
With the increase in the number of users on social networks, sentiment analysis has been gaining attention. Sentiment analysis establishes the aggregation of these opinions to inform researchers about attitudes towards products or topics. Social network data commonly contain authors’ opinions about specific subjects, such as people’s opinions towards steps taken to manage the COVID-19 pandemic. Usually, people use dialectal language in their posts on social networks. Dialectal language has obstacles that make opinion analysis a challenging process compared to working with standard language. For the Arabic language, Modern Standard Arabic tools (MSA) cannot be employed with social network data that contain dialectal language. Another challenge of the dialectal Arabic language is the polarity of opinionated words affected by inverters, such as negation, that tend to change the word’s polarity from positive to negative and vice versa. This work analyzes the effect of inverters on sentiment analysis of social network dialectal Arabic posts. It discusses the different reasons that hinder the trivial resolution of inverters. An experiment is conducted on a corpus of data collected from Facebook. However, the same work can be applied to other social network posts. The results show the impact that resolution of negation may have on the classification accuracy. The results show that the F1 score increases by 20% if negation is treated in the text.
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