Праектаванне натуральна-моўных інтэрфейсаў для даведкавых сістэм

The description of the technology of modern natural language interfaces for intelligent systems and language interfaces for question-answering intelligent systems is presented, as well as methods and principles for their design. Analysis of the intelligent systems with natural language interfaces used in different areas are given. These areas are medicine, smart home technology, education, industry, fast adaptation to new technologies. The list of the most popular services with the natural language interfaces is presented. Each service can be used as a ready-to-use personal assistant or as a core for the development of a new customized natural language interface. The research of the natural language interfaces was conducted from the point of view of the natural language usage for the interaction between a user and the machine. The main problems here are the bias in natural language and the difficulties in the design of natural language interfaces that meet user expectations. The main principles of modeling of natural language interfaces are considered. As an intelligent system the interface consists of the database, knowledge machine and user interface. Speech recognition and speech synthesis components make natural language interfaces more convenient from the point of view of usability.Разглядаюцца існуючыя натуральна-моўныя і маўленчыя інтэрфейсы для пытальна-адказных сістэм даведкавага прызначэння, а таксама падыходы да іх праектавання. Праводзіцца кароткі аналіз найбольш вядомых у розных сферах дзейнасці інтэлектуальных сістэм з натуральна-моўным інтэрфейсам: пры выкарыстанні ў медыцыне, тэхналогіях разумнага дому, адукацыі, прамысловасці, хуткай адаптацыі да новых тэхналогій у паўсядзѐнным жыцці. Даецца спіс асноўных існуючых сэрвісаў, якія могуць выкарыстоўвацца як персанальныя асістэнты, а таксама як аснова для пабудавання ўжо сваіх маўленчых інтэрфейсаў. Натуральна-моўныя інтэрфейсы даследуюцца з пункту гледжання выкарыстання натуральнай мовы для арганізацыі дыялогу карыстальніка з камп’ютарнай сістэмай. Пры гэтым абмяркоўваюцца асноўныя складанасці, звязаныя з неадназначнасцю натуральнай мовы і неадпаведнасцю магчымасцей рэалізацыі натуральна-моўнага інтэрфейсу спадзяванням карыстальніка. Прыводзяцца галоўныя прынцыпы мадэлявання натуральна-моўнага інтэрфейсу, які, будучы інтэлектуальнай сістэмай, у якасці асноўных сваіх кампанентаў складаецца з базы ведаў, машыны апрацоўкі ведаў і карыстальніцкага інтэрфейсу

CROWDSOURCING BASED MICRO NAVIGATION SYSTEM FOR VISUALLY IMPAIRED

Mobility and safety are primary concerns for blind and visually impaired (BVI) users when navigating in unfamiliar environments. Typically, a sighted person can locate a place of interest if they are provided guidance while approaching within a few meters of the location. However, this resolution of guidance is often insufficient for blind travelers. In this thesis, we propose a crowdsourcing based micro navigation system for BVI users in both indoor and outdoor environments. To achieve this goal, our system includes three parts: crowdsourcing reports generated by volunteers using the volunteer application, landmarks validation performed by the system administrator using the admin application, and the BVI user navigation obtained through the BVI user application. In addition, we provide accessible audio navigation for indoor and outdoor environments required to deliver real time step by step landmark information to BVI users. Crowdsourcing is enabled by the contribution of many volunteers which use the proposed volunteer application to report specific landmarks in the environment including their location, description and surrounding landmarks. These reports which are uploaded to the server database, are validated by the admin application which updates the server database and deploy BLE tags for indoor environment. The BVI user application localizes users by GPS outdoors and BLE proximity technology indoors. Using the real-time location of users and the landmark node graph we built from updated server database, this application provides the shortest route to the destination and real time “micro-navigation” information describing how to get to the next landmark’s location with corresponding distance & orientation. This information is used ix to make users well aware of where they are, and guide users to their chosen destination within a cane’s distance. This application will improve the confidence and safety of BVI users by enabling them to explore and get navigation in both indoor and outdoor environments

Scalable and Vision Free User Interface Approaches for Indoor Navigation Systems for the Visually Impaired

This thesis introduces scalable and vision free user interface approaches for indoor navigation systems for the visually impaired. Using an Android Smartphone that runs the indoor navigation system – Percept Application with accessibility features, the blind user obtains navigation instructions generated automatically by our navigation generation algorithm to the chosen destination when touching specific landmarks tagged with Near Field Communication tags. This thesis also introduces an Orientation & Mobility Survey Tool that can help O&M Instructors survey the building and deploy such indoor navigation system. The system was deployed and tested in a large building at the University of Massachusetts at Amherst

Multiple Trajectory Prediction of Moving Agents with Memory Augmented Networks

Pedestrians and drivers are expected to safely navigate complex urban environments along with several non cooperating agents. Autonomous vehicles will soon replicate this capability. Each agent acquires a representation of the world from an egocentric perspective and must make decisions ensuring safety for itself and others. This requires to predict motion patterns of observed agents for a far enough future. In this paper we propose MANTRA, a model that exploits memory augmented networks to effectively predict multiple trajectories of other agents, observed from an egocentric perspective. Our model stores observations in memory and uses trained controllers to write meaningful pattern encodings and read trajectories that are most likely to occur in future. We show that our method is able to natively perform multi-modal trajectory prediction obtaining state-of-the art results on four datasets. Moreover, thanks to the non-parametric nature of the memory module, we show how once trained our system can continuously improve by ingesting novel patterns