The study examines the main scenarios and implementation of software prototype aimed at managing the weakly planned and deferred activities through the integration of adaptive user-configured notifications and time and location context-aware reminders. Based on the previous work on elicitation of the typical scenarios of fuzzily planned activities, we formalize these situations using jointly the models borrowed from linguistics and ontology design. The software prototype aimed at assessing the suggested models and interfaces is implemented as an Android mobile application. We suggest an approach to evaluation of the designed tools based on the standard metrics commonly used for notification-issuing applications and quantified metrics reflecting the user’s subjective experience.

Digital transformation suggests a high degree of automation while managing social behavior and planning individual or collaborative activities. Most scheduling and planning solutions are based on time, task and resource management. In this work, we consider the scenarios of so-called “soft” planning, which assumes the exact timing of activities might be unknown and dependent on flexible conditions. We conceptualize the project with the help of such instruments as common-sense ontology, domain-specific ontology, software requirements visualization, and visual statechart formalism. This particular contribution focuses on the challenges of developing a mobile notification service for managing activities “pre-programmed” by the user, in which notifications are issued if the user enters a location suitable for implementing the desired deferred action. The suggested ontology-based model does not assume using or improving formal optimal time or task scheduling, but suggests an approach for informal practical computer-assisted decision-making involving typical scenarios appearing in everyday life. We piloted a number of prototypes for location-based user-oriented reminder setup and notification management, partially fitting the requirements and major scenarios of a soft planning system. Based on the experiments with the developed prototype apps for Android, we elicit a number of important aspects of further work towards achieving the location-based situational planning and notification management solutions adopted for practical use.

This study focuses on a variety of aspects of teaching programming which can connect the agenda of programming classes to foreign language learning and the broader scope of applied linguistics. In the domain of developing computer-assisted interactive tools for language learning, we can discover a number of interesting and non-trivial problems that can be suggested to students participating in programming classes. Specifically, this study reports an example from our project on the automatic generation of data series trend descriptions to accompany presentation graphs and charts. Originally designed for language learning purposes, this project can be revisited for possible applications as programming exercises that serve as vehicles for a problem-based learning approach to class organization. In general, we believe that through working on, and hopefully, solving practical problems students can apply the content studied in other classes. This application can significantly enhance the knowledge and skills students obtain in programming classes; and thus, improve their professional skill set.

Environmental sound classification has received more attention in recent years.Analysis of environmental sounds is difficult because of its unstructured nature.However, the presence of strong spectro-temporal patterns makes the classification possible.Since LSTM neural networks are efficient at learning temporal dependencies we propose and examine a LSTM model for urban sound classification.The model is trained on magnitude mel-spectrograms extracted from UrbanSound8K dataset audio.The proposed network is evaluated using 5-fold cross-validation and compared with the baseline CNN.It is shown that the LSTM model outperforms a set of existing solutions and is more accurate and confident than the CNN.

This paper examines the perspectives of intonation processing for automatic keyword extraction.Based on a discourse intonation model from D. Brazil, automatic tone pattern recognition in speech stream is performed.It is shown that automatic classification of tone patterns can be done using simple polynomials and correlation.The original software tool PitchKey-wordExtractor (PKE) was applied to academic discourse (on-line lectures) to extract keywords.The results were compared to the output of popular tools for speech analytics: VoiceBase and IBM Watson.All the records were processed also with Praat software and annotated by human experts.Experiments show that none of the automatic systems outperforms the others and PKE, VoiceBase and IBM Watson have the identical error rates with respect to human expert opinion.It motivates further research and supports the tendency to integrate intonation and, more generally, prosody processing in automatic keyword extraction.

Keyword extraction is widely used for information indexing, compressing, summarizing, etc. Existing keyword extraction techniques apply various text-based algorithms and metrics to locate the keywords.At the same time, some types of audio and audiovisual content, e. g. lectures, talks, interviews and other speech-oriented information, allow to perform keyword search by prosodic accents made by a speaker.This paper presents PitchKeywordExtractor -an algorithm with its software prototype for prosody-based automatic keyword extraction in speech content.It operates together with a third-party automatic speech recognition system, handles speech prosody by a pitch detection algorithm and locates the keywords using pitch contour cross-correlation with four tone units taken from D. Brazil discourse intonation model.

In this paper we examine a number of solutions used in developing multimedia guiding systems for travelers.We pay particular attention to existing mobile audioguide systems and the capabilities they provide for tour creators.The major contribution of this work is to propose a model that would be appropriate for better tour recommendation and playback automation of outdoor travel tours with using geo-positioning.Specifically, we make an effort to advance a just walk concept, and to resolve a number of client-server data synchronization issues in process of tour modification by both tour creators and its users.

Contemporary travel information services are connected to huge amount of travel related data used for improving personalized suggestions.Such suggestions include finding better routes, access to amusement and educational amenities implemented as digital services, as well as the features for people collaboration, and for planning leisure time with respect to existing attractiveness evaluation algorithms under time-budget constraints.Much effort is required for supporting personalized itineraries construction in such a way which would leverage existing cultural and technological user experience.In this paper we analyze the underlying algorithms and major components being an implementation of the proposed model investigated with particular attention to annotated leisure walk route construction, traveler collaboration and travel meeting management.In sum, we make an effort to address a number of complex issues in the area of developing models, interfaces and algorithms required by modern travel services considered as an essential application of a human-centric computing multidisciplinary paradigm.

This paper focuses on a generalized approach to providing user interface to a web-based expert system (WBES).We examine MVC and MVP design patterns used traditionally to construct a web application user interface.In order to leverage the strength of the MVC/MVP design patterns we propose a special ontology representing a user communication domain.We describe a self-service networked infrastructure for automatic deployment of command line interface (CLI) applications.We demonstrate how to apply the proposed ontology for the design of a WBES aimed at supporting client software re-execution in clouds.In particular, we address the problems existing in the area of software development for music information retrieval algorithms implementation.

This paper is focused on using computer timeline based interfaces in the domain of history learning.The paper explains what kind of problems do historians and learners face when they deal with chronologically ordered historical information.We review existing approaches and software tools that support timelines.Based on the timeline metaphor ontological model, we examine major features of existing software tools and introduce some novel elements that might be considered as requirements for further implementations.