Educational Dialogue Systems for Visually Impaired Students: Introducing a Task-Oriented User-Agent Corpus

This paper describes a corpus consisting of real-world dialogues in English between users and a task-oriented conversational agent, with interactions revolving around the description of finite state automata. The creation of this corpus is part of a larger research project aimed at developing tools for an easier access to educational content, especially in STEM fields, for users with visual impairments. The development of this corpus was precisely motivated by the aim of providing a useful resource to support the design of such tools. The core feature of this corpus is that its creation involved both sighted and visually impaired participants, thus allowing for a greater diversity of perspectives and giving the opportunity to identify possible differences in the way the two groups of participants interacted with the agent. The paper introduces this corpus, giving an account of the process that led to its creation, i.e. the methodology followed to obtain the data, the annotation scheme adopted, and the analysis of the results. Finally, the paper reports the results of a classification experiment on the annotated corpus, and an additional experiment to assess the annotation capabilities of three large language models, in view of a further expansion of the corpus. The corpus is released under the Creative Commons Attribution Non Commercial 4.0 International license and available, only for research purposes, at: https://zenodo.org/records/10822733

Natural language generation in dialogue systems for customer care

In this paper we discuss the role of natural language generation (NLG) in modern dialogue systems (DSs). In particular, we will study the role that a linguistically sound NLG architecture can have in a DS. Using real examples from a new corpus of dialogue in customer-care domain, we will study how the non-linguistic contextual data can be exploited by using NLG

Accelerated Sizing of a Power Split Electrified Powertrain

Component sizing generally represents a demanding and time-consuming task in the development process of electrified powertrains. A couple of processes are available in literature for sizing the hybrid electric vehicle (HEV) components. These processes employ either time-consuming global optimization techniques like dynamic programming (DP) or near-optimal techniques that require iterative and uncertain tuning of evaluation parameters like the Pontryagin's minimum principle (PMP). Recently, a novel near-optimal technique has been devised for rapidly predicting the optimal fuel economy benchmark of design options for electrified powertrains. This method, named slope-weighted energy-based rapid control analysis (SERCA), has been demonstrated producing results comparable to DP, while limiting the associated computational time by near two orders of magnitude. In this paper, sizing parameters for a power split electrified powertrain are considered that include the internal combustion engine size, the two electric motor/generator sizes, the transmission ratios, and the final drive ratio. The SERCA approach is adopted to rapidly evaluate the fuel economy capabilities of each sizing option in various driving missions considering both type-approval drive cycles and real-world driving profiles. While screening out for optimal sizing options, the implemented methodology includes drivability criteria along with fuel economy potential. Obtained results will demonstrate the agility of the developed sizing tool in identifying optimal sizing options compared to state-of-the-art sizing tools for electrified powertrains

Machine learning algorithms distinguish discrete digital emotional fingerprints for web pages related to back pain

Back pain is the leading cause of disability worldwide. Its emergence relates not only to the musculoskeletal degeneration biological substrate but also to psychosocial factors; emotional components play a pivotal role. In modern society, people are significantly informed by the Internet; in turn, they contribute social validation to a “successful” digital information subset in a dynamic interplay. The Affective component of medical pages has not been previously investigated, a significant gap in knowledge since they represent a critical biopsychosocial feature. We tested the hypothesis that successful pages related to spine pathology embed a consistent emotional pattern, allowing discrimination from a control group. The pool of web pages related to spine or hip/knee pathology was automatically selected by relevance and popularity and submitted to automated sentiment analysis to generate emotional patterns. Machine Learning (ML) algorithms were trained to predict page original topics from patterns with binary classification. ML showed high discrimination accuracy; disgust emerged as a discriminating emotion. The findings suggest that the digital affective “successful content” (collective consciousness) integrates patients’ biopsychosocial ecosystem, with potential implications for the emergence of chronic pain, and the endorsement of health-relevant specific behaviors. Awareness of such effects raises practical and ethical issues for health information providers

A system dynamics and participatory action research approach to promote healthy living and a healthy weight among 10–14-year-old adolescents in Amsterdam: The LIKE programme

This paper describes the design of the LIKE programme, which aims to tackle the complex problem of childhood overweight and obesity in 10–14-year-old adolescents using a systems dynamics and participatory approach. The LIKE programme focuses on the transition period from 10-years-old to teenager and was implemented in collaboration with the Amsterdam Healthy Weight Programme (AHWP) in Amsterdam-East, the Netherlands. The aim is to develop, implement and evaluate an integrated action programme at the levels of family, school, neighbourhood, health care and city. Following the principles of Participatory Action Research (PAR), we worked with our population and societal stakeholders as co-creators. Applying a system lens, we first obtained a dynamic picture of the pre-existing systems that shape adolescents’ behaviour relating to diet, physical activity, sleep an

Preferences in Temporal Relational Databases

Despite the huge amount of work devoted to the treatment of time within the relational context, some relevant phenomena remain to be fully addressed. We focus on one of them, i.e., temporal indeterminacy with preferences. In several domains (e.g., workflows, guidelines) and tasks (e.g., planning, scheduling), the exact time of occurrence of facts is not known: only an interval of possible values for their starting time, and a range of possible durations is available. Additionally, preferences can be assigned to the different temporal possibilities. We propose the first relational temporal database approach coping with such issues. We introduce a new data model to cope with indeterminate time with preferences, considering a family of preference functions, and we propose new definitions of relational algebraic operators to query the new data model. We also ascertain the properties of the new model and algebra, with emphasis on reducibility, and on the correctness of the algebraic operators

Temporal Reasoning with Layered Preferences

Temporal representation and temporal reasoning is a central in Artificial Intelligence. The literature is moving to the treatment of “non-crisp” temporal constraints, in which also preferences or probabilities are considered. However, most approaches only support numeric preferences, while, in many domain applications, users naturally operate on “layered” scales of values (e.g., Low, Medium, High), which are domain- and task-dependent. For many tasks, including decision support, the evaluation of the minimal network of the constraints (i.e., the tightest constraints) is of primary importance. We propose the first approach in the literature coping with layered preferences on quantitative temporal constraints. We extend the widely used simple temporal problem (STP) framework to consider layered user-defined preferences, proposing (i) a formal representation of quantitative constraints with layered preferences, and (ii) a temporal reasoning algorithm, based on the general algorithm Compute-Summaries, for the propagation of such temporal constraints. We also prove that our temporal reasoning algorithm evaluates the minimal network