SEGSys: A mapping system for segmentation analysis in energy

Customer segmentation analysis can give valuable insights into the energy efficiency of residential buildings. This paper presents a mapping system, SEGSys that enables segmentation analysis at the individual and the neighborhood levels. SEGSys supports the online and offline classification of customers based on their daily consumption patterns and consumption intensity. It also supports the segmentation analysis according to the social characteristics of customers of individual households or neighborhoods, as well as spatial geometries. SEGSys uses a three-layer architecture to model the segmentation system, including the data layer, the service layer, and the presentation layer. The data layer models data into a star schema within a data warehouse, the service layer provides data service through a RESTful interface, and the presentation layer interacts with users through a visual map. This paper showcases the system on the segmentation analysis using an electricity consumption data set and validates the effectiveness of the system

A knowledge-based approach towards human activity recognition in smart environments

For many years it is known that the population of older persons is on the rise. A recent report estimates that globally, the share of the population aged 65 years or over is expected to increase from 9.3 percent in 2020 to around 16.0 percent in 2050 [1]. This point has been one of the main sources of motivation for active research in the domain of human activity recognition in smart-homes. The ability to perform ADL without assistance from other people can be considered as a reference for the estimation of the independent living level of the older person. Conventionally, this has been assessed by health-care domain experts via a qualitative evaluation of the ADL. Since this evaluation is qualitative, it can vary based on the person being monitored and the caregiver\u2019s experience. A significant amount of research work is implicitly or explicitly aimed at augmenting the health-care domain expert\u2019s qualitative evaluation with quantitative data or knowledge obtained from HAR. From a medical perspective, there is a lack of evidence about the technology readiness level of smart home architectures supporting older persons by recognizing ADL [2]. We hypothesize that this may be due to a lack of effective collaboration between smart-home researchers/developers and health-care domain experts, especially when considering HAR. We foresee an increase in HAR systems being developed in close collaboration with caregivers and geriatricians to support their qualitative evaluation of ADL with explainable quantitative outcomes of the HAR systems. This has been a motivation for the work in this thesis. The recognition of human activities \u2013 in particular ADL \u2013 may not only be limited to support the health and well-being of older people. It can be relevant to home users in general. For instance, HAR could support digital assistants or companion robots to provide contextually relevant and proactive support to the home users, whether young adults or old. This has also been a motivation for the work in this thesis. Given our motivations, namely, (i) facilitation of iterative development and ease in collaboration between HAR system researchers/developers and health-care domain experts in ADL, and (ii) robust HAR that can support digital assistants or companion robots. There is a need for the development of a HAR framework that at its core is modular and flexible to facilitate an iterative development process [3], which is an integral part of collaborative work that involves develop-test-improve phases. At the same time, the framework should be intelligible for the sake of enriched collaboration with health-care domain experts. Furthermore, it should be scalable, online, and accurate for having robust HAR, which can enable many smart-home applications. The goal of this thesis is to design and evaluate such a framework. This thesis contributes to the domain of HAR in smart-homes. Particularly the contribution can be divided into three parts. The first contribution is Arianna+, a framework to develop networks of ontologies - for knowledge representation and reasoning - that enables smart homes to perform human activity recognition online. The second contribution is OWLOOP, an API that supports the development of HAR system architectures based on Arianna+. It enables the usage of Ontology Web Language (OWL) by the means of Object-Oriented Programming (OOP). The third contribution is the evaluation and exploitation of Arianna+ using OWLOOP API. The exploitation of Arianna+ using OWLOOP API has resulted in four HAR system implementations. The evaluations and results of these HAR systems emphasize the novelty of Arianna+

Web data extraction systems versus research collaboration in sustainable planning for housing: Smart governance takes it all

To date, there are no clear insights in the spatial patterns and micro-dynamics of the housing market. The objective of this study is to collect real estate micro-data for the development of policy-support indicators on housing market dynamics at the local scale. These indicators can provide the requested insights in spatial patterns and micro-dynamics of the housing market. Because the required real estate data are not systematicly published as statistical data or open data, innovative forms of data collection are needed. This paper is based on a case study approach of the greater Leuven area (Belgium). The research question is what are suitable methods or strategies to collect data on micro-dynamics of the housing market. The methodology includes a technical approach for data collection, being Web data extraction, and a governance approach, being explorative interviews. A Web data extraction system collects and extracts unstructured or semi-structured data that are stored or published on Web sources. Most of the required data are publicly and readily available as Web data on real estate portal websites. Web data extraction at the scale of the case study succeeded in collecting the required micro-data, but a trial run at the regional scale encountered a number of practical and legal issues. Simultaneously with the Web data extraction, the dialogue with two real estate portal websites was initiated, using purposive sampling and explorative semi-structured interviews. The interviews were considered as the start of a transdisciplinary research collaboration process. Both companies indicated that the development of indicators about housing market dynamics was a good and relevant idea, yet a challenging task. The companies were familiar with Web data extraction systems, but considered it a suboptimal technique to collect real estate data for the development of housing dynamics indicators. They preferred an active collaboration instead of passive Web scraping. In the frame of a users’ agreement, we received one company’s dataset and calculated the indicators for the case study based on this dataset. The unique micro-data provided by the company proved to be the start of a collaborative planning approach between private partners, the academic world and the Flemish government. All three win from this collaboration on the long run. Smart governance can gain from smart technologies, but should not loose sight of active collaborations

Semantic multimedia modelling & interpretation for search & retrieval

With the axiomatic revolutionary in the multimedia equip devices, culminated in the proverbial proliferation of the image and video data. Owing to this omnipresence and progression, these data become the part of our daily life. This devastating data production rate accompanies with a predicament of surpassing our potentials for acquiring this data. Perhaps one of the utmost prevailing problems of this digital era is an information plethora. Until now, progressions in image and video retrieval research reached restrained success owed to its interpretation of an image and video in terms of primitive features. Humans generally access multimedia assets in terms of semantic concepts. The retrieval of digital images and videos is impeded by the semantic gap. The semantic gap is the discrepancy between a user’s high-level interpretation of an image and the information that can be extracted from an image’s physical properties. Content- based image and video retrieval systems are explicitly assailable to the semantic gap due to their dependence on low-level visual features for describing image and content. The semantic gap can be narrowed by including high-level features. High-level descriptions of images and videos are more proficient of apprehending the semantic meaning of image and video content. It is generally understood that the problem of image and video retrieval is still far from being solved. This thesis proposes an approach for intelligent multimedia semantic extraction for search and retrieval. This thesis intends to bridge the gap between the visual features and semantics. This thesis proposes a Semantic query Interpreter for the images and the videos. The proposed Semantic Query Interpreter will select the pertinent terms from the user query and analyse it lexically and semantically. The proposed SQI reduces the semantic as well as the vocabulary gap between the users and the machine. This thesis also explored a novel ranking strategy for image search and retrieval. SemRank is the novel system that will incorporate the Semantic Intensity (SI) in exploring the semantic relevancy between the user query and the available data. The novel Semantic Intensity captures the concept dominancy factor of an image. As we are aware of the fact that the image is the combination of various concepts and among the list of concepts some of them are more dominant then the other. The SemRank will rank the retrieved images on the basis of Semantic Intensity. The investigations are made on the LabelMe image and LabelMe video dataset. Experiments show that the proposed approach is successful in bridging the semantic gap. The experiments reveal that our proposed system outperforms the traditional image retrieval systems

Sustainable Development of Real Estate

Research, theoretical and practical tasks of sustainable real estate development process are revised in detail in this monograph; particular examples are presented as well. The concept of modern real estate development model and a developer is discussed, peculiarities of the development of built environment and real estate objects are analyzed, as well as assessment methods, models and management of real estate and investments in order to increase the object value. Theoretical and practical analyses, presented in the monograph, prove that intelligent and augmented reality technologies allow business managers to reach higher results in work quality, organize a creative team of developers, which shall present more qualitative products for the society. The edition presents knowledge on economic, legal, technological, technical, organizational, social, cultural, ethical, psychological and environmental, as well as its management aspects, which are important for the development of real estate: publicly admitted sustainable development principles, urban development and aesthetic values, territory planning, participation of society and heritage protection. It is admitted that economical crises are inevitable, and the provided methods shall help to decrease possible loss. References to the most modern world scientific literature sources are presented in the monograph. The monograph is prepared for the researchers, MSc and PhD students of construction economics and real estate development. The book may be useful for other researchers, MSc and PhD students of economics, management and other specialities, as well as business specialist of real estate business. The publication of monograph was funded by European Social Fund according to project No. VP1-2.2-ŠMM-07-K-02-060 Development and Implementation of Joint Master’s Study Programme “Sustainable Development of the Built Environment”

Access beyond geographic accessibility: understanding opportunities to human needs in a physical-virtual world

Access to basic human needs, such as food and healthcare, is conceptually understood to be comprised of multiple spatial and aspatial dimensions. However, research in this area has traditionally been explored with spatial accessibility measures that almost exclusively focus on just two dimensions. Namely, the availability of resources, services, and facilities, and the accessibility or ease to which locations of these opportunities can be reached with existing land-use and transport systems under temporal constraints and considering individual characteristics of people. These calculated measures are insufficient in holistically capturing available opportunities as they ignore other components, such as the emergence of virtual space to carry out activities and interactions enabled by modern information and communication technologies (ICT). Human dynamics today exist in a hybrid physical-virtual space, and recent research has highlighted the importance of understanding ICT, individual behavior, local context, social relations, and human perceptions in identifying opportunities available to people. However, there lacks a holistic approach that relates these different aspects to access research. This dissertation addresses this gap by proposing a new conceptual framework for the geography of access for various kinds of human needs, using food access as a case study to illustrate how the proposed framework can be applied to address critical societal issues. An interactive multispace geographic information system (GIS) web application is developed to better understand and visualize individual potential food access based on the conceptual framework. This dissertation contributes to the body of research with a proposed conceptual framework of access in a hybrid physical-virtual world, integration of various big and small data sources to reveal information relating to the access of people, and novel development of a multi-space GIS to analyze and visualize access to opportunities