945 research outputs found
NEMISA Digital Skills Conference (Colloquium) 2023
The purpose of the colloquium and events centred around the central role that data plays
today as a desirable commodity that must become an important part of massifying digital
skilling efforts. Governments amass even more critical data that, if leveraged, could
change the way public services are delivered, and even change the social and economic
fortunes of any country. Therefore, smart governments and organisations increasingly
require data skills to gain insights and foresight, to secure themselves, and for improved
decision making and efficiency. However, data skills are scarce, and even more
challenging is the inconsistency of the associated training programs with most curated for
the Science, Technology, Engineering, and Mathematics (STEM) disciplines.
Nonetheless, the interdisciplinary yet agnostic nature of data means that there is
opportunity to expand data skills into the non-STEM disciplines as well.College of Engineering, Science and Technolog
A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics
In today's competitive and fast-evolving business environment, it is a
critical time for organizations to rethink how to make talent-related decisions
in a quantitative manner. Indeed, the recent development of Big Data and
Artificial Intelligence (AI) techniques have revolutionized human resource
management. The availability of large-scale talent and management-related data
provides unparalleled opportunities for business leaders to comprehend
organizational behaviors and gain tangible knowledge from a data science
perspective, which in turn delivers intelligence for real-time decision-making
and effective talent management at work for their organizations. In the last
decade, talent analytics has emerged as a promising field in applied data
science for human resource management, garnering significant attention from AI
communities and inspiring numerous research efforts. To this end, we present an
up-to-date and comprehensive survey on AI technologies used for talent
analytics in the field of human resource management. Specifically, we first
provide the background knowledge of talent analytics and categorize various
pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant
research efforts, categorized based on three distinct application-driven
scenarios: talent management, organization management, and labor market
analysis. In conclusion, we summarize the open challenges and potential
prospects for future research directions in the domain of AI-driven talent
analytics.Comment: 30 pages, 15 figure
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Detecting Team Conflict From Multiparty Dialogue
The emergence of online collaboration platforms has dramatically changed the dynamics of human teamwork, creating a veritable army of virtual teams composed of workers in different physical locations. The global world requires a tremendous amount of collaborative problem solving, primarily virtual, making it an excellent domain for computer scientists and team cognition researchers who seek to understand the dynamics involved in collaborative tasks to provide a solution that can support effective collaboration. Mining and analyzing data from collaborative dialogues can yield insights into virtual teams\u27 thought processes and help develop virtual agents to support collaboration. Good communication is indubitably the foundation of effective collaboration. Over time teams develop their own communication styles and often exhibit entrainment, a conversational phenomenon in which humans synchronize their linguistic choices. This dissertation presents several technical innovations in the usage of machine learning towards analyzing, monitoring, and predicting collaboration success from multiparty dialogue by successfully handling the problems of resource scarcity and natural distribution shifts. First, we examine the problem of predicting team performance from embeddings learned from multiparty dialogues such that teams with similar conflict scores lie close to one another in vector space. We extract the embeddings from three types of features: 1) dialogue acts 2) sentiment polarity 3) syntactic entrainment. Although all of these features can be used to predict team performance effectively, their utility varies by the teamwork phase. We separate the dialogues of players playing a cooperative game into stages: 1) early (knowledge building), 2) middle (problem-solving), and 3) late (culmination). Unlike syntactic entrainment, both dialogue act and sentiment embeddings effectively classify team performance, even during the initial phase. Second, we address the problem of learning generalizable models of collaboration. Machine learning models often suffer domain shifts; one advantage of encoding the semantic features is their adaptability across multiple domains. We evaluate the generalizability of different embeddings to other goal-oriented teamwork dialogues. Finally, in addition to identifying the features predictive of successful collaboration, we propose multi-feature embedding (MFeEmb) to improve the generalizability of collaborative task success prediction models under natural distribution shifts and resource scarcity. MFeEmb leverages the strengths of semantic, structural, and textual features of the dialogues by incorporating the most meaningful information from dialogue acts (DAs), sentiment polarities, and vocabulary of the dialogues. To further enhance the performance of MFeEmb under a resource-scarce scenario, we employ synthetic data generation and few-shot learning. We use the method proposed by Bailey and Chopra (2018) for few-shot learning from the FsText python library. We replaced the universal embedding with our proposed multi-feature embedding to compare the performance of the two. For data augmentation, we propose using synonym replacement from collaborative dialogue vocabulary instead of synonym replacement from WordNet. The research was conducted on several multiparty dialogue datasets, including ASIST, SwDA, Hate Speech, Diplomacy, Military, SAMSum, AMI, and GitHub. Results show that the proposed multi-feature embedding is an excellent choice for the meta-training stage of the few-shot learning, even if it learns from a small train set of size as small as 62 samples. Also, our proposed data augmentation method showed significant performance improvement. Our research has potential ramifications for the development of conversational agents that facilitate teaming as well as towards the creation of more effective social coding platforms to better support teamwork between software engineers
Fringe platforms: An analysis of contesting alternatives to the mainstream social media platforms in a platformized public sphere
Social media companies are ubiquitous in our social lives and public debate. They provide spaces for discussion and grant us access to journalism. In his 1962 Strukturwandel der Öffentlichkeit, Jürgen Habermas described how the public sphere was transformed through the introduction of modern communication systems. With the advent of social media platforms, the public sphere has transformed again through ‘platformization’. Platformization is the process by which Big Tech companies infiltrate infrastructures, economic processes and governmental frameworks of entire public sectors, structuring them around their own practices and logics. This dissertation studies the contemporary platformized public sphere, not by focusing at the center of the public sphere, but by looking at the edges of the platform ecology, where radical or counter platform technology are situated. I do this through the concept of ‘fringe platforms’, which are defined as; alternative platform services that are established as an explicit critique of the ideological premises and practices of mainstream platform services, which strive to cause a shift in the norms of the platform ecology they contest by offering an ideologically different technology. One such platform is alt-right microblogging service Gab.com, which was subjected to a process of 'deplatformization' in 2018, when its user base was implicated in white supremacist terrorism. Deplatformization refers to tech companies’ efforts to reduce toxic content by pushing back controversial platforms and their communities to the edges of the ecosystem by denying them access to the basic infrastructural services required to function online. By studying Gab through three case studies this dissertation poses the following research questions: What is the role of fringe social media platforms in a platformized public sphere? What hierarchies and shifts in power do they signify? And how can they inform us about the platform ecosystem? In the first case study, I explore Gab as an ecosystem, and conclude that the study of fringe platforms entails a more explicit role in the analyses for a platform’s self-positioning and narrative, as well as a shift in focus from a platform as an ecosystem towards a lens that takes into account the (infra)structural consequences of a platform as part of an ecosystem of services. In the second and third case study, I oblige to this conclusion and examine Gab as part of the platform ecosystem, shifting the analytical lens to the power dynamics and infrastructures of the platformized public sphere. There, I conclude that deplatformization demonstrates how the power and influence of private technology platforms reaches far beyond their own boundaries, which reveals platform power as infrastructural and rule-setting power. In the conclusion chapter, I argue that the aforementioned fringe lens is useful, not only for the analysis of fringe platforms, but also for the platformized public sphere as a whole, as it makes the structures and infrastructures of the platformized public sphere visible; highlights power and discourse; focuses on dynamics, conflict and breakdown; and incorporates the dominant and democratically productive as well as the marginal and illiberal, in its analyses
Figurative Language Detection using Deep Learning and Contextual Features
The size of data shared over the Internet today is gigantic. A big bulk of it comes from postings on social networking sites such as Twitter and Facebook. Some of it also comes from online news sites such as CNN and The Onion. This type of data is very good for data analysis since they are very personalized and specific. For years, researchers in academia and various industries have been analyzing this type of data. The purpose includes product marketing, event monitoring, and trend analysis. The highest usage for this type of analysis is to find out the sentiments of the public about a certain topic or product. This field is called sentiment analysis. The writers of such posts have no obligation to stick to only literal language. They also have the freedom to use figurative language in their publications. Hence, online posts can be categorized into two: Literal and Figurative. Literal posts contain words or sentences that are direct or straight to the point. On the contrary, figurative posts contain words, phrases, or sentences that carry different meanings than usual. This could flip the whole polarity of a given post. Due to this nature, it can jeopardize sentiment analysis works that focus primarily on the polarity of the posts. This makes figurative language one of the biggest problems in sentiment analysis. Hence, detecting it would be crucial and significant. However, the study of figurative language detection is non-trivial. There have been many existing works that tried to execute the task of detecting figurative language correctly, with different methodologies used. The results are impressive but still can be improved. This thesis offers a new way to solve this problem. There are essentially seven commonly used figurative language categories: sarcasm, metaphor, satire, irony, simile, humor, and hyperbole. This thesis focuses on three categories. The thesis aims to understand the contextual meaning behind the three figurative language categories, using a combination of deep learning architecture with manually extracted features and explore the use of well know machine learning classifiers for the detection tasks. In the process, it also aims to describe a descending list of features according to the importance. The deep learning architecture used in this work is Convolutional Neural Network, which is combined with manually extracted features that are carefully chosen based on the literature and understanding of each figurative language. The findings of this work clearly showed improvement in the evaluation metrics when compared to existing works in the same domain. This happens in all of the figurative language categories, proving the framework’s possession of quality
- …