Social Life of Values

The case of the Danish “cartoon war†was a premonition of things to come: accelerated social construction of inequalities and their accelerated symbolic communication, translation and negotiation. New uses of values in organizing and managing inequalities emerge. Values lead active social life as bourgeois virtues (McCloskey, 2006), their subversive alternatives or translated “memes†of cultural history. Since social life of values went global and online, tracing their hybrid manifestations requires cross-culturally competent domestication (Magala, 2005) as if they were “memes†manipulated for further reengineering. Hopes are linked to emergent concepts of “microstorias†(Boje,2002), bottom-up, participative, open citizenship (Balibar,2004), disruption of stereotypical branding in mass-media (Sennett, 2006). However, Kuhn’s opportunistic deviation from Popperian evolutionary epistemology should fade away with other hidden injuries of Cold War, to free our agenda for the future of social sciences in general and organizational sciences in particular (Fuller, 2000, 2003).Complex Identities;Cross-Cultural Competence;Intersubjective Falsificationism;Managing Inequalities;Political Paradigms;Professional Evolution

AI Hype: Public Relations and AI's doomsday machine

This chapter broadens current professional debates by highlighting a different but vital relationship between the PR profession and AI, one in which PR professionals – acting as AI cheerleaders – are deeply implicated in generating AI hype. My discussion explores recent market studies research on disruption and hype cycles, before delving into the latest, somewhat disturbing phase in AI’s hype cycle, in which end-of-the-world scenarios are invoked to stimulate a climate of fear around AI. The chapter concludes by exploring some ethical concerns with promoting AI and automation as humanity’s inevitable future

Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems

A generic mechanism - networked buffering - is proposed for the generation of robust traits in complex systems. It requires two basic conditions to be satisfied: 1) agents are versatile enough to perform more than one single functional role within a system and 2) agents are degenerate, i.e. there exists partial overlap in the functional capabilities of agents. Given these prerequisites, degenerate systems can readily produce a distributed systemic response to local perturbations. Reciprocally, excess resources related to a single function can indirectly support multiple unrelated functions within a degenerate system. In models of genome:proteome mappings for which localized decision-making and modularity of genetic functions are assumed, we verify that such distributed compensatory effects cause enhanced robustness of system traits. The conditions needed for networked buffering to occur are neither demanding nor rare, supporting the conjecture that degeneracy may fundamentally underpin distributed robustness within several biotic and abiotic systems. For instance, networked buffering offers new insights into systems engineering and planning activities that occur under high uncertainty. It may also help explain recent developments in understanding the origins of resilience within complex ecosystems. \ud \u

Identification of Informativeness in Text using Natural Language Stylometry

In this age of information overload, one experiences a rapidly growing over-abundance of written text. To assist with handling this bounty, this plethora of texts is now widely used to develop and optimize statistical natural language processing (NLP) systems. Surprisingly, the use of more fragments of text to train these statistical NLP systems may not necessarily lead to improved performance. We hypothesize that those fragments that help the most with training are those that contain the desired information. Therefore, determining informativeness in text has become a central issue in our view of NLP. Recent developments in this field have spawned a number of solutions to identify informativeness in text. Nevertheless, a shortfall of most of these solutions is their dependency on the genre and domain of the text. In addition, most of them are not efficient regardless of the natural language processing problem areas. Therefore, we attempt to provide a more general solution to this NLP problem. This thesis takes a different approach to this problem by considering the underlying theme of a linguistic theory known as the Code Quantity Principle. This theory suggests that humans codify information in text so that readers can retrieve this information more efficiently. During the codification process, humans usually change elements of their writing ranging from characters to sentences. Examples of such elements are the use of simple words, complex words, function words, content words, syllables, and so on. This theory suggests that these elements have reasonable discriminating strength and can play a key role in distinguishing informativeness in natural language text. In another vein, Stylometry is a modern method to analyze literary style and deals largely with the aforementioned elements of writing. With this as background, we model text using a set of stylometric attributes to characterize variations in writing style present in it. We explore their effectiveness to determine informativeness in text. To the best of our knowledge, this is the first use of stylometric attributes to determine informativeness in statistical NLP. In doing so, we use texts of different genres, viz., scientific papers, technical reports, emails and newspaper articles, that are selected from assorted domains like agriculture, physics, and biomedical science. The variety of NLP systems that have benefitted from incorporating these stylometric attributes somewhere in their computational realm dealing with this set of multifarious texts suggests that these attributes can be regarded as an effective solution to identify informativeness in text. In addition to the variety of text genres and domains, the potential of stylometric attributes is also explored in some NLP application areas---including biomedical relation mining, automatic keyphrase indexing, spam classification, and text summarization---where performance improvement is both important and challenging. The success of the attributes in all these areas further highlights their usefulness

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

Bibliometric Survey on Incremental Learning in Text Classification Algorithms for False Information Detection

The false information or misinformation over the web has severe effects on people, business and society as a whole. Therefore, detection of misinformation has become a topic of research among many researchers. Detecting misinformation of textual articles is directly connected to text classification problem. With the massive and dynamic generation of unstructured textual documents over the web, incremental learning in text classification has gained more popularity. This survey explores recent advancements in incremental learning in text classification and review the research publications of the area from Scopus, Web of Science, Google Scholar, and IEEE databases and perform quantitative analysis by using methods such as publication statistics, collaboration degree, research network analysis, and citation analysis. The contribution of this study in incremental learning in text classification provides researchers insights on the latest status of the research through literature survey, and helps the researchers to know the various applications and the techniques used recently in the field

COVID-19 datasets : a brief overview

The outbreak of the COVID-19 pandemic affects lives and social-economic development around the world. The affecting of the pandemic has motivated researchers from different domains to find effective solutions to diagnose, prevent, and estimate the pandemic and relieve its adverse effects. Numerous COVID-19 datasets are built from these studies and are available to the public. These datasets can be used for disease diagnosis and case prediction, speeding up solving problems caused by the pandemic. To meet the needs of researchers to understand various COVID-19 datasets, we examine and provide an overview of them. We organise the majority of these datasets into three categories based on the category of ap-plications, i.e., time-series, knowledge base, and media-based datasets. Organising COVID-19 datasets into appropriate categories can help researchers hold their focus on methodology rather than the datasets. In addition, applications and COVID-19 datasets suffer from a series of problems, such as privacy and quality. We discuss these issues as well as potentials of COVID-19 datasets. © 2022, ComSIS Consortium. All rights reserved

When users control the algorithms: Values expressed in practices on the twitter platform

Recent interest in ethical AI has brought a slew of values, including fairness, into conversations about technology design. Research in the area of algorithmic fairness tends to be rooted in questions of distribution that can be subject to precise formalism and technical implementation. We seek to expand this conversation to include the experiences of people subject to algorithmic classification and decision-making. By examining tweets about the “Twitter algorithm” we consider the wide range of concerns and desires Twitter users express. We find a concern with fairness (narrowly construed) is present, particularly in the ways users complain that the platform enacts a political bias against conservatives. However, we find another important category of concern, evident in attempts to exert control over the algorithm. Twitter users who seek control do so for a variety of reasons, many well justified. We argue for the need for better and clearer definitions of what constitutes legitimate and illegitimate control over algorithmic processes and to consider support for users who wish to enact their own collective choices