Context-aware Knowledge-based Systems: A Literature Review

Context awareness systems, a subcategory of intelligent systems, are concerned with suggesting relevant products/services to users' situations as smart services. One key element for improving smart services’ quality is to organize and manipulate contextual data in an appropriate manner to facilitate knowledge generation from these data. In this light, a knowledge-based approach, can be used as a key component in context-aware systems. Context awareness and knowledge-based systems, in fact, have been gaining prominence in their respective domains for decades. However, few studies have focused on how to reconcile the two fields to maximize the benefits of each field. For this reason, the objective of this paper is to present a literature review of how context-aware systems, with a focus on the knowledge-based approach, have recently been conceptualized to promote further research in this area. In the end, the implications and current challenges of the study will be discussed

Where is the Bot in our Team? Toward a Taxonomy of Design Option Combinations for Conversational Agents in Collaborative Work

With rapid progress in machine learning, language technologies and artificial intelligence, conversational agents (CAs) gain rising attention in research and practice as potential non-human teammates, facilitators or experts in collaborative work. However, designers of CAs in collaboration still struggle with a lack of comprehensive understanding of the vast variety of design options in the dynamic field. We address this gap with a taxonomy to help researchers and designers understand the design space and the interrelations of different design options and recognize useful design option combinations for their CAs. We present the iterative development of a taxonomy for the design of CAs grounded in state of the art literature and validated with domain experts. We identify recurring design option combinations and white spots from the classified objects that will inform further research and development efforts

Automatic Context-Driven Inference of Engagement in HMI: A Survey

An integral part of seamless human-human communication is engagement, the process by which two or more participants establish, maintain, and end their perceived connection. Therefore, to develop successful human-centered human-machine interaction applications, automatic engagement inference is one of the tasks required to achieve engaging interactions between humans and machines, and to make machines attuned to their users, hence enhancing user satisfaction and technology acceptance. Several factors contribute to engagement state inference, which include the interaction context and interactants' behaviours and identity. Indeed, engagement is a multi-faceted and multi-modal construct that requires high accuracy in the analysis and interpretation of contextual, verbal and non-verbal cues. Thus, the development of an automated and intelligent system that accomplishes this task has been proven to be challenging so far. This paper presents a comprehensive survey on previous work in engagement inference for human-machine interaction, entailing interdisciplinary definition, engagement components and factors, publicly available datasets, ground truth assessment, and most commonly used features and methods, serving as a guide for the development of future human-machine interaction interfaces with reliable context-aware engagement inference capability. An in-depth review across embodied and disembodied interaction modes, and an emphasis on the interaction context of which engagement perception modules are integrated sets apart the presented survey from existing surveys

The dawn of the human-machine era: a forecast of new and emerging language technologies

New language technologies are coming, thanks to the huge and competing private investment fuelling rapid progress; we can either understand and foresee their effects, or be taken by surprise and spend our time trying to catch up. This report scketches out some transformative new technologies that are likely to fundamentally change our use of language. Some of these may feel unrealistically futuristic or far-fetched, but a central purpose of this report - and the wider LITHME network - is to illustrate that these are mostly just the logical development and maturation of technologies currently in prototype. But will everyone benefit from all these shiny new gadgets? Throughout this report we emphasise a range of groups who will be disadvantaged and issues of inequality. Important issues of security and privacy will accompany new language technologies. A further caution is to re-emphasise the current limitations of AI. Looking ahead, we see many intriguing opportunities and new capabilities, but a range of other uncertainties and inequalities. New devices will enable new ways to talk, to translate, to remember, and to learn. But advances in technology will reproduce existing inequalities among those who cannot afford these devices, among the world's smaller languages, and especially for sign language. Debates over privacy and security will flare and crackle with every new immersive gadget. We will move together into this curious new world with a mix of excitement and apprehension - reacting, debating, sharing and disagreeing as we always do. Plug in, as the human-machine era dawn

Analysis and automatic identification of spontaneous emotions in speech from human-human and human-machine communication

383 p.This research mainly focuses on improving our understanding of human-human and human-machineinteractions by analysing paricipants¿ emotional status. For this purpose, we have developed andenhanced Speech Emotion Recognition (SER) systems for both interactions in real-life scenarios,explicitly emphasising the Spanish language. In this framework, we have conducted an in-depth analysisof how humans express emotions using speech when communicating with other persons or machines inactual situations. Thus, we have analysed and studied the way in which emotional information isexpressed in a variety of true-to-life environments, which is a crucial aspect for the development of SERsystems. This study aimed to comprehensively understand the challenge we wanted to address:identifying emotional information on speech using machine learning technologies. Neural networks havebeen demonstrated to be adequate tools for identifying events in speech and language. Most of themaimed to make local comparisons between some specific aspects; thus, the experimental conditions weretailored to each particular analysis. The experiments across different articles (from P1 to P19) are hardlycomparable due to our continuous learning of dealing with the difficult task of identifying emotions inspeech. In order to make a fair comparison, additional unpublished results are presented in the Appendix.These experiments were carried out under identical and rigorous conditions. This general comparisonoffers an overview of the advantages and disadvantages of the different methodologies for the automaticrecognition of emotions in speech

Language Processing and the Artificial Mind: Teaching Code Literacy in the Humanities

Humanities majors often find themselves in jobs where they either manage programmers or work with them in close collaboration. These interactions often pose difficulties because specialists in literature, history, philosophy, and so on are not usually code literate. They do not understand what tasks computers are best suited to, or how programmers solve problems. Learning code literacy would be a great benefit to humanities majors, but the traditional computer science curriculum is heavily math oriented, and students outside of science and technology majors are often math averse. Yet they are often interested in language, linguistics, and science fiction. This thesis is a case study to explore whether computational linguistics and artificial intelligence provide a suitable setting for teaching basic code literacy. I researched, designed, and taught a course called “Language Processing and the Artificial Mind.” Instead of math, it focuses on language processing, artificial intelligence, and the formidable challenges that programmers face when trying to create machines that understand natural language. This thesis is a detailed description of the material, how the material was chosen, and the outcome for student learning. Student performance on exams indicates that students learned code literacy basics and important linguistics issues in natural language processing. An exit survey indicates that students found the course to be valuable, though a minority reacted negatively to the material on programming. Future studies should explore teaching code literacy with less programming and new ways to make coding more interesting to the target audience