RoDaFlow: A framework for development of dataflow network agents in Smart-M3 with substitution method

The paper describes RoDaFlow - a framework for development of dataflow network agents on Smart-M3 platform. The agents created with the use of the framework support the substitution mechanism that allows to keep the dataflow network based systems in working conditions when some of its agents fall out. The RoDaFlow framework is implemented in Java with the use of Smart-M3 Java KPI library. It allows to develop primary and substitute agents by implementing only their programs, which define how agents process incoming information. The paper also describes a prototype of home light control system based on the dataflow network that allows to effectively control the light in a home. This system uses a number of sensors, actuators, remote control units and computational agents. The agents use information from remote control units and sensors to control the actuators and thus the light level in the home rooms. The agents for the prototype were developed with the use of proposed framework

Improved algorithm for heart rate measurement using mobile phone camera

Nowadays a lot of different ways to measure person's heart rate are exist. One of such ways is using mobile phone. It is very easy for the person and do not require any special skills or buying special devices. All that is needed for heart rate measurement is mobile phone with on-board camera with flash equipped. In this paper we overview existing algorithms for heart rate measuring using mobile phone and propose improved algorithm, that is more efficient, than reviewed ones

Recursive Sentiment Detection Algorithm for Russian Sentences

The article is devoted to the task of sentiment detection of Russian sentences. The sentiment is conceived as the author's attitude to the topic of a sentence. This assay considers positive, neutral, and negative sentiment classes, i.e., the task of three-classes classification is solved. The article introduces a rule-based sentiment detection algorithm for Russian sentences. The algorithm is based on the assumption that the sentiment of a phrase can be determined by the sentiments of its parts by the recursive application of appropriate semantic rules to the sentiments of its parts organized as a constituency parse tree. The utilized set of semantic rules was constructed based on a discussion with experts in linguistics. The experiments showed that the proposed recursive algorithm performs slightly worse on the hotel reviews corpus than the adapted rule-based approach: weighted $F_1$-measures are 0.75 and 0.78, respectively. To measure the algorithm efficiency on complex sentences, we created OpenSentimentCorpus based on OpenCorpora, an open corpus of sentences extracted from Russian news and periodicals. On OpenSentimentCorpus the recursive algorithm performs better than the adapted approach does: $F_1$-measures are 0.70 and 0.63, respectively. This indicates that the proposed algorithm has an advantage in case of more complex sentences with more subtle ways of expressing the sentiment

Mechanism for robust dataflow operation on smart spaces

Smart Space applications could use different architectures during their operations. The dataflow network pattern allows to modularize the system and provide seamless support for replaceable components. One of information sources for ubiquitous environments are the sensors, whose readings are processed by a number of computational units. The sensors and processing nodes together form the dataflow network. In this paper we describe the implementation of asynchronous dataflow network on top of RDF store. Possible causes of dataflow disruption are discussed. The mechanism for maintaining network operation when data processing unit looses connection with RDF store is proposed. The corresponding modification of node operation is provided

Method and tools for automated end-to-end testing of applications for sailfish OS

The automated end-to-end testing of applications allows to detect regressions early during the development and provide solid foundation for future modifications. However, implementation of such tests for mobile applications on Sailfish OS platform is related to some issues, especially when the application contain custom QML components written in C++. In the paper the authors present a method to resolve these issues, including two approaches to provide custom QML types in the testing environment and corresponding architectural considerations that make the code testable. The authors also describe an open source tool for running end-to-end tests on the integration server that supports the described method and supplements tooling of the Sailfish OS SDK

Neural Network-Based Sentiment Classification of Russian Sentences into Four Classes

The paper is devoted to the classification of Russian sentences into four classes: positive, negative, mixed, and neutral. Unlike the majority of modern study in this area, the mixed sentiment class is introduced. Mixed sentiment sentences contain positive and negative sentiments simultaneously.To solve the problem, the following tools were applied: the attention-based LSTM neural network, the dual attention-based GRU neural network, the BERT neural network with several modifications of the output layer to provide classification into four classes. The experimental comparison of the efficiency of various neural networks were performed on three corpora of Russian sentences. Two of them consist of users’ reviews: one with wear reviews and another with hotel reviews. The third corpus contains news from Russian media. The highest weighted F-measure in experiments (0.90) was achieved when using BERT on the wear reviews corpus, as well as the highest weighted F-measure for positive and negative sentences (0.92 and 0.93, respectively). The best classification results for neutral and mixed sentences were achieved on the news corpus. For them F-measure was 0.72 and 0.58, respectively. As a result of experiments, the significant superiority of the BERT transfer network was demonstrated in comparison with older neural networks LTSM and GRU, especially for classification of sentences with weakly expressed sentiments. The error analysis showed that “adjacent” (positive/negative and mixed) classes are worse classified with BERT than “opposite” classes (positive and negative, neutral and mixed)

Medicine tracker for Smart TV

In this paper we estimate relevance of Smart TV as a platform for mobile healthcare applications. Suitability estimation is based on considering the roles that Smart TV can play in mobile healthcare area and benefits it provides. On top of our analysis we propose a prototype of a mobile healthcare application. Then, we present the actual application that is being developed accordingly to these specifications. Finally, we make a conclusion about relevance of Smart TV as a platform for mHealth applications

A conceptual framework for development of context-aware location-based services on smart-M3 platform

The paper presents a conceptual framework for development of context-aware location-based services. This framework provides relevant objects from the database to the user taking his/her preferences and context into account. It is based on the framework for context-aware preference queries, which provides a model of context- and preference-aware system based on the database, and the open source Smart-M3 platform, which allows to develop intelligent services using the smart space paradigm. The main components of the proposed framework include context-aware preference term generators that translate context information into context-aware preference terms, and a preference query executor that combines all preference terms and conducts their execution using PreferenceSQL JDBC driver. Evaluation of the proposed approach is made using the case study of context-aware restaurant data retrieval

Mobile phone sensors in health applications

One of the most important device in our lives is a mobile phone. For now, it is a powerful computing platform equipped with various sensors. Embedded sensors can be used in multiple domains, such as environmental monitoring, social networks, safety and also healthcare. In this paper we survey the main use cases of mobile phone sensors in mobile healthcare. We classify the proposed mHealth sensing applications according to sensor types they use and discuss the main advantages provided by these applications

An approach to automated thesaurus construction using clusterization-based dictionary analysis

In the paper an automated approach for construction of the terminological thesaurus for a specific domain is proposed. It uses an explanatory dictionary as the initial text corpus and a controlled vocabulary related to the target lexicon to initiate extraction of the terms for the thesaurus. Subdivision of the terms into semantic clusters is based on the CLOPE clustering algorithm. The approach diminishes the cost of the thesaurus creation by involving the expert only once during the whole construction process, and only for analysis of a small subset of the initial dictionary. To validate the performance of the proposed approach the authors successfully constructed a thesaurus in the cardiology domain