A web-based AI assistant Application using Python and JavaScript

Our research is mainly based on a chatbot which is powered by Artificial Intelligence. Nowadays, Artificial Intelligence assistants such as Apple’s Siri, Google’s Now and Amazon’s Alexa are currently fast-growing and widely integrated with many smart devices. These assistants are built with the primary purpose of being personal assistants for every individual user in certain contexts. In this research, we would highlight the development process of the chatbots, features, problems, case studies and limitations. This research delivers the information, helps developers to build answer bots and integrate chatbots with business accounts. The aim is to assist users and allow transactions between client companies and their customers. As a result, users can accomplish results to queries as well as clients can grow their business

Customisable chatbot as a research instrument

Abstract. Chatbots are proliferating rapidly online for a variety of different purposes. This thesis presents a customisable chatbot that was designed and developed as a research instrument for online customer interaction research. The developed chatbot facilitates creation of different bot personas, data management tools, and a fully functional online chat user interface. Customer-facing bots in the system are rulebased, with basic input processing and text response selection based on best match. The system uses its own database to store user-chatbot dialogue history. Further, bots can be assigned unique dialogue scripts and their profiles can be customised concerning name, description and profile image. In the presented validation studies, participants completed a task by taking part in a conversation with different bots, as hosted by the system and invoked through distinct URL parameters. Second, the participants filled in a questionnaire on their experience with the bot, designed to reveal differences in how the bots were perceived. Our results suggest that the chatbot’s personality impacted how customers experienced the interactions. Therefore, the developed system can facilitate research scenarios that deal with investigating participant responses to different chatbot personas. Future work is necessary for a wider range of applications and enhanced response control.Personoitava chatbot tutkimustyökaluna. Tiivistelmä. Chatbotit yleistyvät nopeasti Internetissä ja niitä käytetään enenevissä määrin useissa eri käyttötarkoituksissa. Tämä diplomityö esittelee personoitavan chatbotin, joka on kehitetty tutkimustyökaluksi verkon yli tapahtuvaan vuorovaikutustutkimukseen. Kehitetty chatbot sisältää erilaisten bottipersoonien luonnin, apuvälineitä datan käsittelyn, ja itse botin käyttöliittymän. Järjestelmän käyttäjille vastailevat bottipersoonat ovat sääntöihin perustuvia, niiden syötteet käsitellään suoraviivaisesti ja vastaukseksi valitaan vertailun mukaan paras ennaltamääritellyn skriptin mukaisesti. Järjestelmä käyttää omaa tietokantaa tallentamaan käyttäjä-botti keskusteluhistorian. Lisäksi boteille voidaan asettaa uniikki dialogimalli, ja niiden profiilista voidaan personoida URL-parametrillä nimi, botin kuvaus ja profiilikuva. Chatbotin tekninen toiminta todettiin tutkimuksella, jossa osallistujat suorittivat annetun tehtävän seuraamalla osittain valmista käsikirjoitusta eri bottien kanssa. Tämän jälkeen osallistujat täyttivät käyttäjäkyselyn liittyen heidän kokemukseensa botin kanssa. Kysely oli suunniteltu paljastamaan mahdolliset eroavaisuudet siinä, kuinka botin käyttäytyminen miellettiin keskustelun aikana. Käyttäjätestin tulokset viittaavat siihen, että chatbotin persoonalla oli vaikutus käyttäjien kokemukseen. Kehitetty järjestelmä siis pystyy mahdollistamaan tutkimusasetelmia, joissa tutkitaan osallistujien reaktioita erilaisten chattibottien persooniin. Jatkotyö kehitetyn chatbotin yhteydessä keskittyy monimutkaisempien käyttötarkoitusten lisäämiseen ja botin vastausten parantamiseen edistyksellisemmän luonnollisen kielen käsittelyn avulla

An expectation-based editing interface for OpenStreetMap

Building an open-source world map was one of the main reasons OpenStreetMap (OSM) was founded. Over 1.3 million contributors participate in editing the the world map collaboratively. Unfortunately, there is no support or any assistive technology solutions that helps blind and visually impaired users to blend into the OSM community. The aim of this thesis is to provide them with an assistive OSM editing application with an adaptive user interface that matches their needs. A mobile application for OSM editing was developed with an assistive recommendation system that helps predicting changes users might need to commit. The thesis describes in details the application design, decisions made, workflow and modularity

Non-Visual Representation of Complex Documents for Use in Digital Talking Books

Essential written information such as text books, bills, and catalogues needs to be accessible by everyone. However, access is not always available to vision-impaired people. As they require electronic documents to be available in specific formats. In order to address the accessibility issues of electronic documents, this research aims to design an affordable, portable, standalone and simple to use complete reading system that will convert and describe complex components in electronic documents to print disabled users

A Model of Persuasion for Speaking Rate Adaptation

Proper speaking rate is a key attribute of effective communication. Emerging persuasive technologies use computers as a tool to induce human behavioural and attitude changes. This thesis established a computational framework which can persuade people to slow down their speech and communicate more effectively. We defined a conceptual model and implemented a computer software system, both serving as the cornerstones of our persuasion framework. The computer system is designed to persuade people to be aware of their speaking rate and to slow down their speech. The combination of computer technology and persuasive technologies and theories are embedded in the system. In order to conduct effective persuasion, a number of computer-based survey questions were asked and a short tailored letter was generated for each participant. A virtual coach system monitored and reminded the participant to slow down. A few adaptive cues were used to enhance the effects of the persuasion. We evaluated the feasibility and effectiveness of the overall system. At the same time, we evaluated the feasibility of individual elements. A total of 22 participants was selected to make up the sample. The experiments were conducted under controlled conditions. The results indicated that our system is effective in persuading people to speak more slowly. The feedback from users indicated that our system raised their awareness about speaking rate

An adaptive computational system for automated, learner-customized segmental perception training in words and sentences: Design, implementation, assessment

Segmental perception training is important as many phonemic errors are common in second language pronunciation and the perception of foreign phonemic contrasts is often difficult to acquire without instruction (Best & Tyler, 2007; Birdsong, 1992, 2006; Flege, 1988, 1995). Numerous computer-assisted programs exist that provide training for segmental perception, but few of them have made effective use of already-existing language resources. There has been a call for the creation of a computer-assisted pronunciation teaching (CAPT) program that provides individualized needs-based training based on first language and learner proficiency (Levis, 2007; Munro, Derwing, & Thomson, 2015). A perception training model is yet to be developed that takes into account the major components important to intelligibility, the use of technology, and the state-of-the-art research findings on perception training. Specifically, the ideal training model first needs to account for learners’ L1 backgrounds since L2 segmental errors are often L1-specific (Swan & Smith, 2002). Second, the training model should also be tailored to individual needs as not everyone sharing the same L1 will certainly have the L1-predicted errors (Munro, 2018; Munro, Derwing, & Thomson, 2015). Third, the functional load theory (King, 1967) suggests that not all phonemic errors affect intelligibility equally and that perception training should not target all errors as if they had an equal impact on intelligibility. Fourth, the training model should leverage a high-variability phonetic training design, defined as a technique of using multiple voice models for perception training (Pisoni & Lively, 1995), which has been found to be efficacious in improving perception (Thomson, 2012; Wang & Munro, 2004) as well as production (Thomson, 2011). This study introduces an innovative online perception training system that uses computational approaches to deliver high variability phonetic training designed to improve learners’ ability to discriminate and identify segmental contrasts. The system was designed with five major features. First, the system was developed with intelligibility-driven goals by only focusing on high functional load segmental errors. Second, the system offered training customized to individual learners’ pre-training diagnostic performance and then adapted the training content and intensity based on individual learners’ errors during real-time learning. Third, in recognition of the efficacy of multi-voice models for perception acquisition (Thomson, 2011, 2012; Wang & Munro, 2004), the system utilized high-variability phonetic training exercises developed using two North American text-to-speech voices. Fourth, the training system was self-contained and could be accessed and used by learners flexibly and independently based on their own pace with little teacher guidance. Fifth, immediate individualized feedback was available on every item during training. In addition, the stimuli used for the training system were automatically extracted from a phonetically transcribed dictionary with word frequency controlled. Specifically, only words among the top 5,000 lemmas in the Contemporary Corpus and American English were selected by the system to ensure that all the training and test stimuli were likely to be familiar to the participants in the study so that they would be able to recognize the stimuli aurally during perception tests and training without seeing the words spelled out. Four types of exercises created with text-to-speech minimal pairs, automatically extracted from the Illinois Speech and Language Engineering Dictionary, were used for training. The training exercises came in four types: same-different discrimination, oddity discrimination, simple identification, and yes/no identification. The voices and words of the training stimuli were controlled for in order to examine the learners’ potential transfer of perception gains to three novel conditions: to trained words spoken with untrained voices, to untrained words spoken with trained voices, and to trained items in sentences. The training system was used for approximately three months by 266 Chinese-L1 English majors from three universities located in three cities (Harbin, Soochow, and Guangzhou). The learners were placed into either an experimental group or a control group based on their institution, and used the system for perception training on nine English consonant and vowel contrasts that were predicted to be challenging for the learners. An analysis of the participants’ diagnostic and training performance revealed substantial variation among the learners’ actual segmental errors and pace of learning. This suggests that L2 phonemic acquisition is not merely L1-specific or dialect-specific but is a process distinctive to individual learners but that was not correlated with time on training, highlighting the importance of incorporating adaptability in the design and delivery of pronunciation training materials. Descriptive and inferential statistics on training effect, retention and transfer of test gains showed that an average of 143 minutes of focused effort led to robust improvement and retention of phonemic perception for most of the segmental contrasts under investigation. L2 segmental acquisition was sensitive to the linguistic context of a segment and the training in the study helped the learners transfer perception gains to untrained contexts (new voices, new words, and the untrained sentence contexts). The results showed that high-variability input materials and the text-to-speech technology can be effectively used to develop perception training materials. The study also showed that exercises designed to specifically sharpen aural sensitivity to contrasting phonemes may facilitate learners’ ability in self correcting phonemic issues even without explicit training on the issues. Findings in the study were discussed within the exemplar theory (Bybee, 2000), the analogical modeling theory (Skousen, 1989), the TRACE model within the connectionist framework (Joanisse & McClelland, 2015), the item versus system learning theory (Cruttenden, 1981), the U-shaped Learning Theory (Gass & Selinker, 2008), and the Speech Learning Model (Flege, 1995). Future research is encouraged to investigate the effect of adaptive perception training in improving learner response latency and productive performance that are essential to real life pronunciation and communication competence