Hikester - the event management application

Today social networks and services are one of the most important part of our everyday life. Most of the daily activities, such as communicating with friends, reading news or dating is usually done using social networks. However, there are activities for which social networks do not yet provide adequate support. This paper focuses on event management and introduces "Hikester". The main objective of this service is to provide users with the possibility to create any event they desire and to invite other users. "Hikester" supports the creation and management of events like attendance of football matches, quest rooms, shared train rides or visit of museums in foreign countries. Here we discuss the project architecture as well as the detailed implementation of the system components: the recommender system, the spam recognition service and the parameters optimizer

NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings

Current approaches for service composition (assemblies of atomic services) require developers to use: (a) domain-specific semantics to formalize services that restrict the vocabulary for their descriptions, and (b) translation mechanisms for service retrieval to convert unstructured user requests to strongly-typed semantic representations. In our work, we argue that effort to developing service descriptions, request translations, and matching mechanisms could be reduced using unrestricted natural language; allowing both: (1) end-users to intuitively express their needs using natural language, and (2) service developers to develop services without relying on syntactic/semantic description languages. Although there are some natural language-based service composition approaches, they restrict service retrieval to syntactic/semantic matching. With recent developments in Machine learning and Natural Language Processing, we motivate the use of Sentence Embeddings by leveraging richer semantic representations of sentences for service description, matching and retrieval. Experimental results show that service composition development effort may be reduced by more than 44\% while keeping a high precision/recall when matching high-level user requests with low-level service method invocations.Comment: This paper will appear on SCC'19 (IEEE International Conference on Services Computing) on July 1

Translating Video Recordings of Mobile App Usages into Replayable Scenarios

Screen recordings of mobile applications are easy to obtain and capture a wealth of information pertinent to software developers (e.g., bugs or feature requests), making them a popular mechanism for crowdsourced app feedback. Thus, these videos are becoming a common artifact that developers must manage. In light of unique mobile development constraints, including swift release cycles and rapidly evolving platforms, automated techniques for analyzing all types of rich software artifacts provide benefit to mobile developers. Unfortunately, automatically analyzing screen recordings presents serious challenges, due to their graphical nature, compared to other types of (textual) artifacts. To address these challenges, this paper introduces V2S, a lightweight, automated approach for translating video recordings of Android app usages into replayable scenarios. V2S is based primarily on computer vision techniques and adapts recent solutions for object detection and image classification to detect and classify user actions captured in a video, and convert these into a replayable test scenario. We performed an extensive evaluation of V2S involving 175 videos depicting 3,534 GUI-based actions collected from users exercising features and reproducing bugs from over 80 popular Android apps. Our results illustrate that V2S can accurately replay scenarios from screen recordings, and is capable of reproducing $\approx$ 89% of our collected videos with minimal overhead. A case study with three industrial partners illustrates the potential usefulness of V2S from the viewpoint of developers.Comment: In proceedings of the 42nd International Conference on Software Engineering (ICSE'20), 13 page

Integration of mobile devices in home automation with use of machine learning for object recognition

The concept of smart homes is increasingly expanding and the number of objects we have at home that are connected grows exponentially. The so-called internet of things is increasingly englobing more home devices and the need to control them is also growing. However, there are numerous platforms that integrate numerous protocols and devices in many ways, many of them being unintuitive. Something that we always carry with us is our mobile devices and with the evolution of technology, they have become increasingly powerful and equipped with lots of sensors. One of the bridges to the real world in these devices is the camera and its many potentials. The amount of information gathered can be used in a variety of ways and one topic that has also gathered tremendous relevance is Artificial Intelligence and Machine Learning algorithms. Thus, with the correct processing, data collected by the sensors could be used intuitively to interact with such devices present at home. This dissertation presents the prototype of a system that integrates mobile devices in home automation platforms by detecting objects in the information collected by their cameras, consequently allowing the user to interact with them in an intuitive way. The main contribution of the work developed is the non-explored until then integration, in the home automation context, of cutting-edge algorithms capable of easily outperforming humans into analyzing and processing data acquired by our mobile devices. Throughout the dissertation the referred concepts are explored as well as the potentiality of this integration and the results obtained.O conceito de casas inteligentes está cada vez mais em constante expansão e o número de objetos que temos em casa que estão conectados cresce exponencialmente. A tão chamada internet das coisas abrange cada vez mais dispositivos domésticos crescendo também a necessidade de os controlar. No entanto existem inúmeras plataformas que integram inúmeros protocolos e dispositivos, de inúmeras maneiras, muitas delas pouco intuitivas. Algo que transportamos sempre connosco são os nossos dispositivos móveis e com a evolução da tecnologia, estes vieram-se tornando cada vez mais potentes e munidos de variados sensores. Uma das portas para o mundo real nestes dispositivos é a câmara e as suas inúmeras potencialidades. Uma temática que tem vindo também a ganhar enorme relevância é a Inteligência Artificial e os algoritmos de Aprendizagem Máquina. Assim, com o processamento correto os dados recolhidos pelos sensores poderiam ser utilizados de maneira intuitiva para interagir com os tais dispositivos presentes em casa. Nesta dissertação é apresentado o protótipo de um sistema que integra os dispositivos móveis nas plataformas de automação de casas através da deteção de objetos na informação recolhida pela câmara dos mesmos, permitindo assim ao utilizador interagir com eles de forma intuitiva. A principal contribuição do trabalho desenvolvido é a integração não explorada até então, no contexto da automação de casas, de algoritmos de ponta capazes de superar facilmente os seres humanos na análise e processamento de dados adquiridos pelos nossos dispositivos móveis. Ao longo da dissertação são explorados os conceitos referidos, bem como a potencialidade dessa integração e os resultados obtidos

Integration of mobile devices in home automation with use of machine learning for object recognition

The number of smart homes is increasingly expanding, with even more connected devices and available control options. Mobile devices have unfortunately been up to now generally regarded as mere remote controls in these environments. This paper addresses this shortcoming, by presenting a novel integration architecture and prototype where the potential of mobile devices sensors can be better explored in home automation platforms, in particular by detecting objects in the information collected by their cameras that subsequently allow for users to interact with them in an intuitive way. The detection is performed at the mobile side, using a lightweight machine learning solution. The obtained accuracy and processing time are comparable to that obtained at server side. But the advantage here is that the interactive experience of users can be dramatically improved, with the absence of round-trip time required if server processing would be used.info:eu-repo/semantics/publishedVersio