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Without any more delay, individuals are urged to adopt more sustainable behaviors to fight climate change. New digital systems mixed with engaging and gamification mechanisms could play an important role in achieving such an objective. In particular, Conversational Agents, like Smart Home Assistants, are a promising tool that encourage sustainable behaviors within household settings. In recent years, large language models (LLMs) have shown great potential in enhancing the capabilities of such assistants, making them more effective in interacting with users. We present the design and implementation of GreenIFTTT, an application empowered by GPT4 to create and control home automation routines. The agent helps users understand which energy consumption optimization routines could be created and applied to make their home appliances more environmentally sustainable. We performed an exploratory study (Italy, December 2023) with N = 13 participants to test our application’s usability and UX. The results suggest that GreenIFTTT is a usable, engaging, easy, and supportive tool, providing insight into new perspectives and usage of LLMs to create more environmentally sustainable home automation.

Data Science is increasingly applied for solving real-life problems, in industry and in academic research, but mastering Data Science requires an interdisciplinary education that is still scarce on the market. Thus, there is a growing need for user-friendly tools that allow domain experts to directly apply data analysis methods to their datasets, without involving a Data Science expert. In this scenario, we present DSBot, an assistant that can analyze the user data and produce answers by mastering several Data Science techniques. DSBot understands the research question with the help of conversation interaction, produces a data science pipeline and automatically executes the pipeline in order to generate analysis. The strength of DSBot lies in the design of a rich domain specific language for modeling data analysis pipelines, the use of a suitable neural network for machine translation of research questions, the availability of a vast dictionary of pipelines for matching the translation output, and the use of natural language technology provided by a conversational agent. We empirically evaluated the translation capabilities and the autoML performances of the system. In the translation task, it obtains a BLEU score of 0.8. In prediction tasks, DSBot outperforms TPOT, an autoML tool, in 19 datasets out of 30.

Abstract Background With the advancements of Next Generation Techniques, a tremendous amount of genomic information has been made available to be analyzed by means of computational methods. Bioinformatics Tertiary Analysis is a complex multidisciplinary process that represents the final step of the whole bioinformatics analysis pipeline. Despite the popularity of the subject, the Bioinformatics Tertiary Analysis process has not yet been specified in a systematic way. The lack of a reference model results into a plethora of technological tools that are designed mostly on the data and not on the human process involved in Tertiary Analysis, making such systems difficult to use and to integrate. Methods To address this problem, we propose a conceptual model that captures the salient characteristics of the research methods and human tasks involved in Bioinformatics Tertiary Analysis. The model is grounded on a user study that involved bioinformatics specialists for the elicitation of a hierarchical task tree representing the Tertiary Analysis process. The outcome was refined and validated using the results of a vast survey of the literature reporting examples of Bioinformatics Tertiary Analysis activities. Results The final hierarchical task tree was then converted into an ontological representation using an ontology standard formalism. The results of our research provides a reference process model for Tertiary Analysis that can be used both to analyze and to compare existing tools, or to design new tools. Conclusions To highlight the potential of our approach and to exemplify its concrete applications, we describe a new bioinformatics tool and how the proposed process model informed its design.

Due to the difficulties in differentiating bilingual children with Developmental Language Disorder (DLD) from bilingual children with temporary language difficulties that may be caused by heterogeneous language input, language assessments of bilingual children are challenging for clinicians. Research demonstrates that assessments of bilingual children should be in all the languages a bilingual child speaks. This can be arduous for clinicians, but computerised screening approaches provide potential solutions. MuLiMi is a new web-based platform designed to automatise screening procedures for bilingual children at risk of DLD. To validate this procedure and investigate its reliability, 36 Spanish-speaking children, aged 4–6 years old, living in Italy, were tested remotely using the Italian–Spanish MuLiMi DLD screening. Sixteen of the participants were previously diagnosed with DLD. L2 (second or societal language) as well as L1 (first or family language) language abilities in static (nonword repetition, grammaticality judgement, and verb comprehension) as well as dynamic tasks (dynamic novel word learning) were assessed. Speed and accuracy of the children’s responses were automatically recorded (except nonword repetition). Significant associations emerged between the results obtained in the screening tasks when comparing them to parental questionnaires and standardised tests. An exploratory analysis of the diagnostic accuracy indicates that the single screening scores as well as the overall total score significantly contribute to DLD (risk) identification.

Ideally, language and reading skills in bilingual children are assessed in both languages spoken in order to avoid misdiagnoses of communication or learning disorders. Due to limited capacity of clinical and educational staff, computerized screenings that allow for automatic evaluation of the children’s performance on reading tasks (accuracy and speed) might pose a useful alternative in clinical and school settings. In this study, a novel web-based screening platform for language and reading assessment is presented. This tool has been preliminarily validated with monolingual Italian, Mandarin–Italian and English–Italian speaking primary school children living and schooled in Italy. Their performances in the screening tasks in Italian and—if bilingual—in their native language were compared to the results of standardized/conventional reading assessment tests as well as parental and teacher questionnaires. Correlations revealed the tasks that best contributed to the identification of risk for the presence of reading disorders and showed the general feasibility and usefulness of the computerized screening. In a further step, both screening administrators (Examiners) and child participants (Examinees) were invited to participate in usability studies, which revealed general satisfaction and provided suggestions for further improvement of the screening platform. Based on these findings, the potential of the novel web-based screening platform is discussed.