Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time

Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs. A promising direction is to combine the two approaches for high quality, low latency, and low cost solutions. In this paper, we introduce Evorus, a crowd-powered conversational assistant built to automate itself over time by (i) allowing new chatbots to be easily integrated to automate more scenarios, (ii) reusing prior crowd answers, and (iii) learning to automatically approve response candidates. Our 5-month-long deployment with 80 participants and 281 conversations shows that Evorus can automate itself without compromising conversation quality. Crowd-AI architectures have long been proposed as a way to reduce cost and latency for crowd-powered systems; Evorus demonstrates how automation can be introduced successfully in a deployed system. Its architecture allows future researchers to make further innovation on the underlying automated components in the context of a deployed open domain dialog system.Comment: 10 pages. To appear in the Proceedings of the Conference on Human Factors in Computing Systems 2018 (CHI'18

A conceptual model for e-learning supporting tools design based on cue model and Kansei engineering

The Covid-19 pandemic has triggered changes in learning due to the practice of social distancing to curb the spread of the virus. E-learning platforms have become the main platform for learning throughout the pandemic. However, e-learning does have challenges when it comes to ensuring student’s optimum participation throughout the learning experience that require extensive research about techniques and methods for an optimum e-learning experience. This includes various e-learning supporting tools that provides easy communication and immediate assistance to enhance user experience. The supporting tools or software usability and functionality design determined as imperative in enhancing the e-learning user experience. Thus, this research proposes a conceptual model for designing the e-learning supporting tools based on the CUE Model, integrated with Kansei Engineering for optimum user experience that can serve as a guideline for the e-learning supporting tools designer. The outcome of this research will create new research fields that incorporate multiple domains, including the e-learning domain, software and supporting tools design, emotions and user experience

Allyn, A Recommender Assistant for Online Bookstores

Treballs Finals del Grau d'Economia i Estadística. Doble titulació interuniversitària, Universitat de Barcelona i Universitat Politècnica de Catalunya. Curs: 2017-2018. Tutors: Esteban Vegas Lozano; Salvador Torra Porras(eng) Recommender Systems are information filtering engines used to estimate user preferences on items they have not seen: books, movies, restaurants or other things for which individuals have different tastes. Collaborative and Content-based Filtering have been the two popular memory-based methods to retrieve recommendations but these suffer from some limitations and might fail to provide effective recommendations. In this project we present several variations of Artificial Neural Networks, and in particular, of Autoencoders to generate model-based predictions for the users. We empirically show that a hybrid approach combining this model with other filtering engines provides a promising solution when compared to a standalone memory-based Collaborative Filtering Recommender. To wrap up the project, a chatbot connected to an e-commerce platform has been implemented so that, using Artificial Intelligence, it can retrieve recommendations to users.(cat) Els Sistemes de Recomanació són motors de filtratge de la informació que permeten estimar les preferències dels usuaris sobre ítems que no coneixen a priori. Aquests poden ser des de llibres o películes fins a restaurants o qualsevol altre element en el qual els usuaris puguin presentar gustos diferenciats. El present projecte es centra en la recomanació de llibres. Es comença a parlar dels Sistemes de Recomanació al voltant de 1990 però és durant la darrera dècada amb el boom de la informació i les dades massives que comencen a tenir major repercussió. Tradicionalment, els mètodes utilitzats en aquests sistemes eren dos: el Filtratge Col·laboratiu i el Filtratge basat en Contingut. Tanmateix, ambdós són mètodes basats en memòria, fet que suposa diverses limitacions que poden arribar a portar a no propocionar recomanacions de manera eficient o precisa. En aquest projecte es presenten diverses variacions de Xarxes Neuronals Artificials per a generar prediccions basades en models. En concret, es desenvolupen Autoencoders, una estructura particular d’aquestes que es caracteritza per tenir la mateixa entrada i sortida. D’aquesta manera, els Autoencoders aprenen a descobrir els patrons subjacents en dades molt esparses. Tots aquests models s’implementen utilitzant dos marcs de programació: Keras i Tensorflow per a R. Es mostra empíricament que un enfocament híbrid que combina aquests models amb altres motors de filtratge proporciona una solució prometedora en comparació amb un recomanador que utilitza exclusivament Filtratge Col·laboratiu. D’altra banda, s’analitzen els sistemes de recomanació des d’un punt de vista econòmic, emfatitzant especialment el seu impacte en empreses de comerç electrònic. S’analitzen els sistemes de recomanació desenvolupats per quatre empreses pioneres del sector així com les tecnologies front-end en què s’implementen. En concret, s’analitza el seu ús en chatbots, programes informàtics de missatgeria instantània que, a través de la Intel·ligència Artificial simulen la conversa humana. Per tancar el projecte, es desenvolupa un chatbot propi implementat en una aplicació de missatgeria instantània i connectat a una empresa de comerç electrònic, capaç de donar recomanacions als usuaris fent ús del sistema de recomanació híbrid dut a terme

A Framework for Leveraging Artificial Intelligence in Project Management

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis dissertation aims to support the project manager in their daily tasks. As we use artificial intelligence (AI) and machine learning (ML) in everyday life, it is necessary to include them in business and change traditional ways of working. For the purpose of this study, it is essential to understand challenges and areas of project management and how artificial intelligence can contribute to them. A theoretical overview, applying the knowledge of project management, will show a holistic view of the current situation in the enterprises. The research is about artificial intelligence applications in project management, the common activities in project management, the biggest challenges, and how AI and ML can support it. Understanding project managers help create a framework that will contribute to optimizing their tasks. After designing and developing the framework for applying artificial intelligence to project management, the project managers were asked to evaluate. This study is essential to increase awareness among the stakeholders and enterprises on how automation of the processes can be improved and how AI and ML can decrease the possibility of risk and cost along with improving the happiness and efficiency of the employees

Optimising user experience with: conversational Interfaces

Dissertação de Mestrado em Engenharia InformáticaUser Experience is one of the main aspects that maintain a customer loyal to cloud based solutions or SaaS (Software as a Service). With the rise of the natural language processing techniques, the industry is looking at automated chatbot solutions to boost and expand their services. This thesis presents a practical case study of the implementation of a chatbot solution to complement a CRM (Customer Relationship Management) software called FOXAIO, and then quantify, following the most appropriate guides and solutions available, the User Experience (UX) optimisation. In order to create a robust and scalable solution based on the constraints created by the company in the case, we reviewed the current deep learning techniques, tools and libraries available to help the development process. The most proven techniques in the field of Natural Language Processing (NLP) will be introduced. To achieve the goals of this solution without "reinventing the wheel", we present possible architectures to use at the top of some open source and available tools on the market, with a special relief in the framework RASA. Also we discussed some of possible techniques to create the intent classifier, where we detail the better performance in the top of the rasa tensorflow embedding pipeline for this particular case. The conversational system, also, required a channel to interact with the final user. To achieve that, we also implemented a basic chat interface created on the top of the socket protocol, which communicate with the conversation system. In any case, it would be possible to extend to the other channel’s available on the market, like messenger, slack, telegram. Finally, we detail with a few use cases, that’s hypothetically possible to improve the user experience of an existing software system (FOXAIO) using a conversational interface on the top of that. Also, we achieved some highlights about the preference to use a conversational interface because of his simplicity, defended by a better score in the SUS scale, 70 against 58 to the traditional UI, and good indicatives by the HEART framework.O User Experience é possivelmente um dos principais aspetos para fidelizar um cliente numa solução cloud, as chamadas soluções SaaS (Software as a Service). O crescimento acentuado deste tipo de soluções aquece a rivalidade entre competidores e cada vez mais pretende-se oferecer as formas mais revolucionárias para premiar a qualidade de um serviço. Com o crescimento acentuado das técnicas na área do NLP (Natural Language Processing) a indústria começa a olhar para os chatbots como uma possível solução de automatizar, impulsionar e expandir as suas ofertas. A presente tese visa a apresentar uma implementação prática de um chatbot sobre um software com semelhanças de um CRM (Customer Relationship Management) existente intitulado por FOXAIO. Com o objetivo de desenvolver uma solução robusta e escalável tendo em atenção as condições elaboradas pela empresa em questão, um longo e detalhado estudo foi elaborado sobre as mais diversas técnicas de deep learning usadas no ramo de Processamento de Linguagem Natural (NLP). Atribuindo um particular ênfase às redes neurais recorrentes (RNN) e com a devida extensão Long Short Term Memory (LSTM) que juntas, formam e trabalham muito bem na resolução dos problemas de um sistema de inteligência artificial, como é o caso. Para a sua implementação sobre um software já existente, foi necessário o desenvolvimento de uma pequena interface conversacional com o objetivo de mais tarde a complementar sobre a interface do utilizador do mesmo. Para esse efeito, foi implementado um canal sobre o sistema conversacional de comunicação em protocolo de socket, criando uma classe para o efeito que mais tarde seria útil para gerar logs de análise. Durante a implementação do sistema conversacional foram feitas várias comparações sobre as variantes dos seus módulos desde o Dialog Management (DM) ao Intent Classifier onde várias arquiteturas foram expostas e comparadas com o intuito de corresponder à melhor solução possível para um chatbot de língua portuguesa em primeira instância, foi optado pela escolha de um Dialog Management híbrido face ao domínio e à existência de conversas contextuais contínuas onde, por exemplo, se torna bastante difícil de desenvolver sobre outros paradigmas. Quanto ao Intent Classifier, foi usada a técnica rasa tensorflow embedding, esta técnica (que treina palavras do princípio) usada obteve melhores resultados para o particular caso estudado na presente tese (CRM), do que por exemplo o uso um modelo de dados com palavras já treinadas. Finalmente, conseguimos apresentar hipoteticamente, possíveis melhorias do UX no uso de uma interface conversacional sobre uma interface tradicional, usando as várias ferramentas de análise disponíveis, onde por exemplo com o auxílio da framework HEART (criada pelo Google), conseguimos obter indicativos bastante satisfatórios por 34 pessoas que fizeram os primeiros testes no chatbot desenvolvido. Examinando o feedback desses mesmos utilizadores em ambiente de teste, conseguimos obter um resultado na escala de SUS (System Usability Scale) com um valor de 70, enquanto a interface tradicional arrecadou 58, notando então que as pessoas se sentiram mais capazes no uso do sistema conversacional

Artificial intelligence applications in marketing: the chatbot of the Department of Economics and Management "Marco Fanno”

openL'intelligenza artificiale (AI) offre numerose applicazioni nel marketing, ma allo stesso tempo ci sono diverse limitazioni da considerare nella sua adozione. Dopo la prima parte di analisi generale delle applicazioni e degli aspetti negativi dell'AI e dei chatbot, la tesi si concentra sul caso dell'implementazione di un chatbot da parte del Dipartimento di Economia e Management “Marco Fanno” dell'Università di Padova. La domanda di ricerca è volta a capire se il chatbot implementato dal Dipartimento sia stato efficace nell'alleggerire e supportare il lavoro dell'ufficio amministrativo e nel rispondere alle domande degli studenti. A tal fine, il documento analizza se il numero di email è diminuito dopo l'introduzione del chatbot. Inoltre è stato svolto un questionario per valutare l'esperienza che gli studenti del Dipartimento hanno avuto con il chatbot di ateneo. Il sondaggio ha anche chiesto agli studenti quali servizi vorrebbero che il chatbot aggiungesse a quelli attuali. Inoltre, è stata condotta un'analisi economica su benefici e costi per valutare se il chatbot genererà un risultato economico positivo. Questo studio consente di valutare l'impatto che un chatbot potrebbe avere nel campo dell'istruzione. In particolare, può fornire informazioni alle università sul fatto che un chatbot possa migliorare il coinvolgimento con gli studenti, liberare il personale da compiti ripetitivi e generare benefici economici netti nel lungo periodo. Il questionario stesso è stato condotto attraverso un sondaggio web su Google Forms e un sondaggio attraverso un chatbot. In questo modo ho anche analizzato quale dei due metodi sia il più efficace per condurre un'indagine. Alcune prove rivelano come i sondaggi condotti attraverso un chatbot possano portare a risposte più accurate da parte degli intervistati. Confrontando i risultati ottenuti della due modalità di sondaggio ho potuto verificare queste evidenze con un nuovo campione di partecipanti, gli studenti di Economia. I risultati della tesi non hanno mostrato prove chiare del fatto che il chatbot consentisse di ridurre il numero di e-mail. Ma si suggerisce un'indagine su un periodo più lungo. Successivamente i risultati hanno evidenziato un buon apprezzamento degli studenti per il chatbot e hanno suggerito l'introduzione di notifiche push che ricordano delle scadenze universitarie come le tasse. La stima dell'analisi costi-benefici prevedeva un risultato netto positivo su tre anni con un ROI del 29%. Inoltre, il sondaggio chatbot ha parzialmente confermato la tendenza ad ottenere risposte più accurate rispetto ad un classico sondaggio web.Artificial intelligence (AI) offers numerous applications in marketing, but at the same time, there are several limitations to consider in its adoption. After the first part about a general analysis of the applications and negative aspects of AI and chatbots, the thesis focuses on the case of the implementation of a chatbot by the Department of Economics and Management “Marco Fanno” of the University of Padua. The research question turns towards understanding whether the chatbot implemented by the Department was effective in easing and supporting the work of the administrative office and answering students questions. For this purpose, the paper analyses if the number of emails is decreased after the chatbot introduction. In addition, a questionnaire was carried out to evaluate the experience that the students of the Department have had with the university chatbot. The survey also asked students what services they would like the chatbot to add to their current ones. Moreover, an economic analysis on benefits and costs was conducted to estimate whether the chatbot will generate a positive outcome. This study allows evaluating the impact a chatbot could have in the education field. In particular, it can provide insight to universities on whether a chatbot could enhance the engagement with students, offload staff from repetitive tasks and generate net economic benefits in the long period. The questionnaire itself was conducted through a web survey on Google Forms and a chatbot survey. In this way, it could also be verified which of the two methods is the most effective to conduct a survey. Some evidence finds how chatbot surveys can lead to less satisfactory answers by respondents. Comparing the two survey results, I can verify these past findings with a different sample of participants, the students of Economics. The results did not show clear evidence of whether the chatbot allowed reducing the number of emails. But an investigation over a longer period is suggested. Then, findings highlighted a good appreciation of students for the chatbot and suggested the introduction of push notifications that remember university deadlines such as taxes. The estimation of the benefits-cost analysis forecasted a net positive outcome over three years with an ROI of 29%. Also, the chatbot survey partially confirmed the encouraging finding in reducing satisficing by respondents.

The Impact of Providing Chatbot Content on Developing the English Communication Skills Among Al-Azhar Kindergarten Teachers

This paper focuses on investigating the impact of using Chatbot content on improving the English communication skills of Al-Azhar Al-Sharif kindergarten teachers. The researcher used a quasi-experimental design to explain the differences that occurred in the teachers` performance before and after the intervention of the Chatbot content. This design required the researcher to develop a performance observation checklist, which is the main tool of this research. In addition, the researcher constructed the Chatbot content and the e-training programme. After the research design was completed, thirty-three (33) female Azhari kindergarten teachers participated in this experiment. The performance observation checklist was used for evaluating the teachers` performance before and after the intervention of the Chatbot content and for measuring the teachers` retention of the acquired skills. At the end of the research, a statistical analysis of the results was applied. The results showed a statistically significant difference at the level of 0.05 on the performance observation checklist`s mean scores of the sample teachers' pre-and post-application of the Chatbot content in favour of the postapplication. The second statistical analysis of the performance observation checklist showed that there is no significant difference between the sample teachers` mean scores in the post- and follow-up application of the Chatbot content. These results are proof that validate the research`s main assumption that Chatbot content can improve the English communication skills of kindergarten teachers

Exploring Public Opinions Toward the Use of Generative Artificial Intelligence Chatbot in Higher Education:An Insight from Topic Modelling and Sentiment Analysis

The Generative Artificial Intelligence chatbots (GAI chatbots) have emerged as promising tools in various domains, including higher education, so this study aims to investigate the role of Bard, a newly developed GAI chatbot, in higher education. English tweets were collected from Twitter's free streaming Application Programming Interface (API). The Latent Dirichlet Allocation (LDA) algorithm was applied to extract latent topics from the tweets. User sentiments were extracted using the NRC Affect Intensity Lexicon and SentiStrength tools. This study explored the benefits, challenges, and future implications of integrating GAI chatbots in higher education. The findings shed light on the potential power of such tools, exemplified by Bard, in enhancing the learning process and providing support to students throughout their educational journe

Intentional dialogues in multi-agent systems based on ontologies and argumentation

Some areas of application, for example, healthcare, are known to resist the replacement of human operators by fully autonomous systems. It is typically not transparent to users how artificial intelligence systems make decisions or obtain information, making it difficult for users to trust them. To address this issue, we investigate how argumentation theory and ontology techniques can be used together with reasoning about intentions to build complex natural language dialogues to support human decision-making. Based on such an investigation, we propose MAIDS, a framework for developing multi-agent intentional dialogue systems, which can be used in different domains. Our framework is modular so that it can be used in its entirety or just the modules that fulfil the requirements of each system to be developed. Our work also includes the formalisation of a novel dialogue-subdialogue structure with which we can address ontological or theory-of-mind issues and later return to the main subject. As a case study, we have developed a multi-agent system using the MAIDS framework to support healthcare professionals in making decisions on hospital bed allocations. Furthermore, we evaluated this multi-agent system with domain experts using real data from a hospital. The specialists who evaluated our system strongly agree or agree that the dialogues in which they participated fulfil Cohen’s desiderata for task-oriented dialogue systems. Our agents have the ability to explain to the user how they arrived at certain conclusions. Moreover, they have semantic representations as well as representations of the mental state of the dialogue participants, allowing the formulation of coherent justifications expressed in natural language, therefore, easy for human participants to understand. This indicates the potential of the framework introduced in this thesis for the practical development of explainable intelligent systems as well as systems supporting hybrid intelligence