Many researchers and product developers are striving toward achieving ICT-enabled independence of older adults by setting up Enhanced Living Environments (ELEs). Technological solutions, which are often based on the Internet of Things (IoT), show great potential in providing support for Active Aging. To enhance the quality of life for older adults and overcome challenges in enabling individuals to achieve their full potential in terms of physical, social, and mental well-being, numerous proof-of-concept systems have been built. These systems, often labeled as Ambient Assisted Living (AAL), vary greatly in targeting different user needs. This paper presents our contribution using SmartHabits, which is an intelligent privacy-aware home care assistance system. The novel system comprising smart home-based and cloud-based parts uses machine-learning technology to provide peace of mind to informal caregivers caring for persons living alone. It does so by learning the user’s typical daily activity patterns and automatically issuing warnings if an unusual situation is detected. The system was designed and implemented from scratch, building upon existing practices from IoT reference architecture and microservices. The system was deployed in several homes of real users for six months, and we will be sharing our findings in this paper.

Background: Software engineering is searching for general principles that apply across contexts, for example, to help guide software quality assurance. Fenton and Ohlsson presented such observations on fault distributions, which have been replicated once. Objectives: We aimed to replicate their study again to assess the robustness of the findings in a new environment, five years later. Method: We conducted a literal replication, collecting defect data from five consecutive releases of a large software system in the telecommunications domain, and conducted the same analysis as in the original study. Results: The replication confirms results on unevenly distributed faults over modules, and that fault proneness distributions persist over test phases. Size measures are not useful as predictors of fault proneness, while fault densities are of the same order of magnitude across releases and contexts. Conclusions: This replication confirms that the uneven distribution of defects motivates uneven distribution of quality assurance efforts, although predictors for such distribution of efforts are not sufficiently precise.

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With the increased usage of cloud computing in production environments, both for scientific workflows and industrial applications, the focus of application providers shifts towards service cost optimisation. One of the ways to achieve minimised service execution cost is to optimise the placement of the service in the resource pool of the cloud data centres. An increasing number of research approaches is focusing on using machine learning algorithms to deal with dynamic cloud workloads by allocating resources to services in an adaptive way. Many of such solutions are intended for cloud infrastructure providers and deal only with specific types of cloud services. In this paper, we present a model-based approach aimed at the providers of applications hosted in the cloud, which is applicable in early phases of the service lifecycle and can be used for any cloud application service. Using several machine learning methods, we create models to predict cloud service cost and response times of two cloud applications. We also explore how to extract knowledge about the effect that the cloud application context has on both service cost and quality of service so that the gained knowledge can be used in the service placement decision process. The experimental results demonstrate the ability of providing relevant information about the impact of cloud application context parameters on service cost and quality of service. The results also indicate the relevance of our approach for applications in preproduction phase since application providers can gain useful insights regarding service placement decision without acquiring extensive training datasets.

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Recent studies confirm the importance of satellite positioning in location-based services (LBS) development. A field study was conducted in suburban and rural areas near Zagreb, Croatia in order to examine the real-time data compliance with recently established positioning performance requirements for LBS quality of service (QoS). Data analysis was based on comparison between actual positioning performance and pre-specified positioning parameter values using defined comparative procedures. The results presented here confirm a good correlation between the actual and required positioning performance, even without implementation of any of augmentation or assistance positioning methods.1. satellite. 2. positioning. 3. performance. 4. LBS. I N T R O D U C T I O N.In the history of location-based services (LBS) development, satellite positioning has been presumed to be a foundation positioning method (Beatty, 2002). The importance of satellite positioning in LBS development was established using third-party simulations and local field trials described in references. In order to confirm this presumption, a Zagreb field trial was conducted on 12 June, 2003. Dynamical positioning performance of satellite navigation in semiurban and rural environments is analysed in this paper. Four basic LBS positioning performance parameters were pre-defined, and their definitions were applied on a set of data collected during the field trial. The paper concludes with the plan of future activities in relation to obtained results of the field trial data analysis. P R E V I O U S W O R K.Satellite positioning is the most promising positioning method for LBS currently available (Filjar et al, 2001). The positioning performance of satellite navigation systems, GPS in particular, is comprehensively described in the related specifications (Department of Defense, 2001) and thoroughly examined during numerous field trials worldwide. However, the implementation of satellite positioning as the foundation of the location-based services (LBS) has not, so far, been appropriately challenged. Special requirements for LBS development

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