Development of a smart tourism information chatbot for Mauritius

Due to the current COVID-19 situation worldwide, the tourism industry has been heavily impacted worldwide. Chatbots help to minimise the spread of the virus, by limiting physical interaction, whilst help to promote the industry and make available tourism information in an accessible familiar manner. This paper aims to analyse the various aspects of the tourism industry and identify the gaps that need to be addressed in order to improve the customer experiences in Mauritius. The aim was deploy a tourism information chatbot that will provide the necessary information and recommendations to tourists coming to Mauritius and attract potential tourists plan their next trip in a few steps, using off-the-shelf technologies. The main advantage of the developed Chatbot is that is built on off the shelf technologies (Rasa, Telegram, etc), but with the ability to be further extended with APIs. Thus the chatbot developed exhibits a number of innovations for a Tourism chatbot, such as Google search, weather acquisition based on location and COVID-19 statistics

Conversational Agent as a Black Hat: Can Criticising Improve Idea Generation?

The Ideate phase of Design Thinking is the source of many idea creations. In this context, criticism is considered a creativity killer, yet recent studies show that criticism can be beneficial. An example of this is the black hat of one creativity method: Six Thinking Hats. It points out the weaknesses of an idea so that they are eliminated by further refining. Previous research shows that conversational agents have an advantage over humans when criticizing because of their perceived neutrality. To investigate this, we developed and implemented a conversational agent and evaluated it using an A/B test. The results of the study show that the prototype is perceived as less neutral when it criticizes. Criticizing by the conversational agent can lead to higher quality ideas. This work contributes to a better understanding of conversational agents in the black hat role as well as of their neutrality

The Effects of Health Care Chatbot Personas With Different Social Roles on the Client-Chatbot Bond and Usage Intentions: Development of a Design Codebook and Web-Based Study

Background The working alliance refers to an important relationship quality between health professionals and clients that robustly links to treatment success. Recent research shows that clients can develop an affective bond with chatbots. However, few research studies have investigated whether this perceived relationship is affected by the social roles of differing closeness a chatbot can impersonate and by allowing users to choose the social role of a chatbot. Objective This study aimed at understanding how the social role of a chatbot can be expressed using a set of interpersonal closeness cues and examining how these social roles affect clients’ experiences and the development of an affective bond with the chatbot, depending on clients’ characteristics (ie, age and gender) and whether they can freely choose a chatbot’s social role. Methods Informed by the social role theory and the social response theory, we developed a design codebook for chatbots with different social roles along an interpersonal closeness continuum. Based on this codebook, we manipulated a fictitious health care chatbot to impersonate one of four distinct social roles common in health care settings—institution, expert, peer, and dialogical self—and examined effects on perceived affective bond and usage intentions in a web-based lab study. The study included a total of 251 participants, whose mean age was 41.15 (SD 13.87) years; 57.0% (143/251) of the participants were female. Participants were either randomly assigned to one of the chatbot conditions (no choice: n=202, 80.5%) or could freely choose to interact with one of these chatbot personas (free choice: n=49, 19.5%). Separate multivariate analyses of variance were performed to analyze differences (1) between the chatbot personas within the no-choice group and (2) between the no-choice and the free-choice groups. Results While the main effect of the chatbot persona on affective bond and usage intentions was insignificant (P=.87), we found differences based on participants’ demographic profiles: main effects for gender (P=.04, ηp2=0.115) and age (P<.001, ηp2=0.192) and a significant interaction effect of persona and age (P=.01, ηp2=0.102). Participants younger than 40 years reported higher scores for affective bond and usage intentions for the interpersonally more distant expert and institution chatbots; participants 40 years or older reported higher outcomes for the closer peer and dialogical-self chatbots. The option to freely choose a persona significantly benefited perceptions of the peer chatbot further (eg, free-choice group affective bond: mean 5.28, SD 0.89; no-choice group affective bond: mean 4.54, SD 1.10; P=.003, ηp2=0.117). Conclusions Manipulating a chatbot’s social role is a possible avenue for health care chatbot designers to tailor clients’ chatbot experiences using user-specific demographic factors and to improve clients’ perceptions and behavioral intentions toward the chatbot. Our results also emphasize the benefits of letting clients freely choose between chatbots

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining

Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approach

The sparsity of labelled data is an obstacle to the development of Relation Extraction models and the completion of databases in various biomedical areas. While being of high interest in drug-discovery, the natural-products literature, reporting the identification of potential bioactive compounds from organisms, is a concrete example of such an overlooked topic. To mark the start of this new task, we created the first curated evaluation dataset and extracted literature items from the LOTUS database to build training sets. To this end, we developed a new sampler inspired by diversity metrics in ecology, named Greedy Maximum Entropy sampler, or GME-sampler (https://github.com/idiap/gme-sampler). The strategic optimization of both balance and diversity of the selected items in the evaluation set is important given the resource-intensive nature of manual curation. After quantifying the noise in the training set, in the form of discrepancies between the input abstracts text and the expected output labels, we explored different strategies accordingly. Framing the task as an end-to-end Relation Extraction, we evaluated the performance of standard fine-tuning as a generative task and few-shot learning with open Large Language Models (LLaMA 7B-65B). In addition to their evaluation in few-shot settings, we explore the potential of open Large Language Models (Vicuna-13B) as synthetic data generator and propose a new workflow for this purpose. All evaluated models exhibited substantial improvements when fine-tuned on synthetic abstracts rather than the original noisy data. We provide our best performing (f1-score=59.0) BioGPT-Large model for end-to-end RE of natural-products relationships along with all the generated synthetic data and the evaluation dataset. See more details at https://github.com/idiap/abroad-re

Supporting learning activities in virtual worlds: methods, tools and evaluation

2011 - 2012Continuing advances and reduced costs in computational power, graphics and network bandwidth let 3D immersive multi‐user Virtual Worlds (VWs) become increasingly accessible while offering an improved and engaging quality of experience. Excited at the prospects of engaging their Net Generation, students and educators worldwide are attempting to exploit the affordances of three‐dimensional (3D) VWs. Environments such as Second Life (SL) are increasingly used in education, often for their flexibility in facilitating student‐directed, self‐paced learning and their communication features. Research on the educational value of VWs has revealed their potential as learning platforms. However, further studies are always needed in order to assess their effectiveness, satisfactorily and social engagement, not only in the general didactic use of the environment, but also for each specific learning subjects, activities and modality. A major question in using VWs in education is finding appropriate value‐added educational applications. The main challenge is to determine learning approaches in which learning in a VW presents added value with respect to traditional education, and to effectively utilize the third dimension to avoid using the environment simply as a communication platform. In addition, the educational VW activities become more and more sophisticated, starting from the early ones based only on information displaying and teaching resources to simulated laboratory and scenarios. The more complex the learning activities are, the more the challenge of guiding students during their learning trajectories increases and there is the need of providing them with appropriate support and guidance. The main contributions of this thesis are summarized as follows: (i) we propose an appropriate value‐added educational application that supports individual learning activities effectively exploiting the third dimension. In particular, we adopt a VW to support the learning of engineering practices based on technical drawing. The proposed system called VirtualHOP trains the students in the way of learning‐by‐doing methodology to build the required 3D objects; (ii) we enhance an helping system with the avatar appearance and AI for helping the exploration of environments and fruition of distance didactic activities in SL; (iii) we empirically evaluate the didactic value and the user perceptions concerning both the learning setting and the avatar‐based virtual assistant. The results demonstrate the usefulness of both the didactic experiences offered in SL and a positive attitude of the learners in terms of enjoyment and ease‐of‐use. [edited by author]XI n.s

A inteligência artificial e os sistemas enterprise resouce planning

A Inteligência Artificial (IA), isto é, a inteligência que se assemelha à inteligência humana na resolução de problemas e tomada de decisão, apresentada por máquinas e softwares, onde se englobam o Natural Language Processing, o Machine Learning, os Chatbots, Redes Neurais Artificiais, entre ouros. A IA já está presente na vida quotidiana das pessoas e empresas, tais como as sugestões para ver filmes na Netflix ou as Assistentes Virtuais que permitem às empresas agendar reuniões. Os sistemas Enterprise Resource Planning (ERP) permitem sincronizar e otimizar todos os dados sobre os processos de negócios das organizações, simplificando o acesso à informação, e, consequentemente, facilitando o controlo e a gestão organizacional. Embora os sistemas ERP condensem todos os dados da organização num só local e já consigam disponibilizar indicadores sobre a organização aos seus gestores, os mesmos não foram concebidos para realizar operações de forma autónoma e automatizada, como o lançamento automatizado de faturas ou automatização do cumprimento de obrigações fiscais, apresentando-se a integração de aplicações de IA como a resposta a este tipo de situações. Neste sentido, o presente estudo tem como objetivo caracterizar a utilização das aplicações de IA nos sistemas ERP e os benefícios das mesmas para os utilizadores destes sistemas. Para o efeito, foi realizado um inquérito, junto de utilizadores de sistemas ERP de diferentes empresas. Os resultados obtidos com este estudo revelam que a maior parte dos utilizadores deste tipo de sistemas já utiliza estas aplicações de IA e que considera importante a sua utilização. Do conjunto de aplicações de IA disponibilizadas aos respondentes deste estudo a que foi considerada como mais importante foi a “Classificação automática de registos contabilísticos”. Já no que concerne aos benefícios das aplicações de IA destacou-se o benefício “Melhoria na verificação/deteção de erros nas contas”