Home Occupant Archetypes:

This research is aimed at better understanding how occupants use energy in their homes from a comfort-driven perspective, in order to propose customized environmental characteristics that could improve the occupants’ comfort while reducing energy consumption. To propose such bespoke environmental features and feedback, occupant archetypes were produced based on the intentions and motivations behind comfort behaviours. Building upon the aim of this thesis, the following main research question was proposed: How can energy behaviours be studied from a comfort-driven perspective in order to facilitate the development of environmental features that support more efficient occupant behaviours and that provide the comfort needs of the person? A mixed-methods human-centered design approach was developed for which four steps were required to answer the main research question, reflecting also the four parts of this dissertation. 1. An extensive and multidisciplinary literature review investigated behavioural theories and comfort theories to find out what the drivers behind behaviours are and to understand comfort from a holistic and integrative lens, including social and psychological comfort. Additionally, an overview of energy use in residential buildings was presented, along with the links between energy consumption and occupant behaviours, thus explaining the problems of performance gaps and the rebound effect. The review eventually proposes that energy consumption, behaviours, and comfort are elements of an interacting system, as many behavioural expressions exercised at home are comfort-driven and several of these comfortdriven behaviours result in energy use. This part was the platform on which a questionnaire was developed based on constructs that motivate behaviour: locus of control, attitudes towards energy, environmental needs, and emotions towards home, in addition to other variables such as health status, demographics, and energy consuming habitual actions. Thus, the questionnaire is a tool that consolidates in a single instrument a self-reported assessment of energy consumption patterns and comfort behaviours. The resulting questionnaire was composed of previously validated instruments that were adapted to the context to assess the corresponding constructs and was composed of 65 variables. 2. The newly developed questionnaire was pilot tested with a population consisting of master students of the faculty of Architecture and the Built Environment of the TU Delft. The pilot was launched to make corrections and adjust the questionnaire and to validate the effectiveness of the analysis method to cluster respondents. The TwoStep cluster analysis was chosen as it is a method normally used in the segmentation of health behaviours and was originally developed to group customers in marketing. More recently, it has been used in studies assessing different types of behaviours, especially in the healthcare field. The pilot ensured that the segmentation method was appropriate for the types of variables involved. The cluster analysis produced a model of six clusters, which was successfully validated according to a process that ensures that the groups are both stable and reliable. Subsequently, the questionnaire was administered to the full sample of 761 respondents –mainly composed of students and employees- and was analysed accordingly with the method. The final model was also validated. The final model resulted in five distinct home occupant clusters, which differed on their comfort needs, attitudes towards energy, environmental control beliefs, and emotions towards their home environment. These clusters were the basis of the forthcoming archetypes. 3. In order to better develop the archetypes, occupant-related qualitative data and environment-related quantitative data was needed. A field study was designed to interview occupiers in their homes and to gather building data. To gather building data, a comprehensive checklist inventoried building characteristics related to energy expenditure, such as type of glazing, type of ventilation, type of appliances, etc. Additionally, the indoor environmental parameters (relative humidity, carbon dioxide, and temperature) were monitored, and finally, actual energy consumption readings were taken for a month during the summer period. Parallelly, in-depth and semi-structured interviews were conducted, which are techniques used to gather qualitative behavioural data from the home occupants. Questions related to their energy consuming habits and practices were asked, as well as about their environmental needs for comfort and energy attitudes. Interviews were analysed with a text mining technique: sentiment analysis, which allows assessing the sentiments associated with the topics discussed. Both qualitative and quantitative data were used to complete the previously found statistical clusters, in order to develop the five final archetypes that are the following: Archetype 1: Restrained Conventionals; Archetype 2: Incautious realists; Archetype 3: Positive savers; Archetype 4: Sensitive wasters; Archetype 5: Vulnerable pessimists. 4. Self-reported data and interviews allow collecting explicit knowledge: a type of knowledge that is readily available and is related to facts and memories. When verbally expressed, these facts and memories tend to be processed through biases and conscious filters. As a result, to produce more accurate and complete archetypes, another type of knowledge is also needed: tacit knowledge. This is a type of knowledge is related to feelings, intuitions, and emotions, which tends to be difficult to express with verbalizations. To collect it, focus group sessions were designed to assess the home occupants’ tacit knowledge in terms of what it means to use energy in their homes and what the ideal home experience is. This was collected with the generation of collages that the participants produced with visual and tactile materials, after which they described the process and meanings of their creations. The data was analysed with the use of affinity diagrams that allows to group large amounts of qualitative data into manageable categories and to see the relations between the categories. The results showed two categories: building and occupant, with five sub-categories in total: behavioural aspects, psychological aspects, energy aspects, financial aspects, and home aspects. Each of these subcategories was composed of codes extracted from the collages produced and from the verbal explanations given by the participants. Finally, the data was related back to each of the archetypes, in order to produce final fully-fledged archetypes. The results show that each archetype has different needs, expectations, and experiences as to how they appraise energy and how they desire comfort in their own houses. Consequently, this gives insights into the fact that each of the archetypes is different, they each need differing environmental features to satisfy their comfort needs, to achieve that comfort, and to perceive the impact of their comfort behaviours on the energy outputs of their household. The differing characteristics that each archetype exhibited were translated into preliminary customized design parameters or bespoke environmental features for each of them. They are summed up as follows: the Restrained Conventional needs large windows for a view and a connection to the outside. Because they value personal space and social interaction at home, yet have low environmental control, the plan of the home needs to give a transition from private to social. They are conservative in the energy use and concerned about their finances: energy feedback can be given to them relating their practices to monetary consequences. The Incautious Realist places importance on having the right size and layout for particular purposes: therefore, they need modularity that they can manually control, due to their high external control. They also value safety and privacy, so the interactions with façade elements need to ensure them that their environment is safe and private. They have a high concern about finances, yet they have a high expenditure. To boost their consumption and their need for control, their home can be equipped with a control station from which they can control appliances, and see their consumption as a financial reflection. The Positive Saver places value on the cleanliness and orderliness of the place, thus they need surfaces and spaces that are easy to clean and reach. They are the biggest savers of all the archetypes and this seems to be due to their environmental concerns. To reduce even further their consumption, feedback can be given to them by translating their comfort actions –oven use, etc. - into environmental consequences. The Sensitive Waster needs softness and tactile sensations in their house. They also place importance on having high freedom of their practices in their house. They are the largest energy waster, and they do not worry about their finances, however, they do value the environment and the future. A smart feature can be designed for them to save more energy by equating their practices to ecological consequences to have a more conservative energy use. The Vulnerable Pessimist places emphasis on the aesthetics of the house, the technologies, and the gadgets. They also value a sense of community and connectedness to their neighbourhood. As result, they need homes that allow for these interactions, in small complexes or pavilions. They do not worry about financial aspects, however their expenditure is middle-range: to improve it; they can receive feedback from the consumption of their community as an awareness tool. The findings of this study can help to improve energy predictions, by making more accurate models with different types of occupants. Furthermore, for the existing housing stock, corporations can use the archetypes to tailor the indoor environmental features and interfaces to the future occupant; or, similarly, different occupants can be better allocated to better matching existing dwellings. As for the design of the future stock, architects and contractors can make use of the archetypes by having a more inclusive design process, by answering real needs of the future occupant and improving the decision making of architects. For policies and energy efficiency programs, knowing that there are different types of occupants can allow to bridge gaps between occupant and provider, by encouraging a participatory or inclusive research and design phase, for the design of devices, feedbacks, and interfaces tailored to the specific archetype

Sentiment Analysis Using Machine Learning Techniques

Before buying a product, people usually go to various shops in the market, query about the product, cost, and warranty, and then finally buy the product based on the opinions they received on cost and quality of service. This process is time consuming and the chances of being cheated by the seller are more as there is nobody to guide as to where the buyer can get authentic product and with proper cost. But now-a-days a good number of persons depend upon the on-line market for buying their required products. This is because the information about the products is available from multiple sources; thus it is comparatively cheap and also has the facility of home delivery. Again, before going through the process of placing order for any product, customers very often refer to the comments or reviews of the present users of the product, which help them take decision about the quality of the product as well as the service provided by the seller. Similar to placing order for products, it is observed that there are quite a few specialists in the field of movies, who go though the movie and then finally give a comment about the quality of the movie, i.e., to watch the movie or not or in five-star rating. These reviews are mainly in the text format and sometimes tough to understand. Thus, these reports need to be processed appropriately to obtain some meaningful information. Classification of these reviews is one of the approaches to extract knowledge about the reviews. In this thesis, different machine learning techniques are used to classify the reviews. Simulation and experiments are carried out to evaluate the performance of the proposed classification methods. It is observed that a good number of researchers have often considered two different review datasets for sentiment classification namely aclIMDb and Polarity dataset. The IMDb dataset is divided into training and testing data. Thus, training data are used for training the machine learning algorithms and testing data are used to test the data based on the training information. On the other hand, polarity dataset does not have separate data for training and testing. Thus, k-fold cross validation technique is used to classify the reviews. Four different machine learning techniques (MLTs) viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), and Linear Discriminant Analysis (LDA) are used for the classification of these movie reviews. Different performance evaluation parameters are used to evaluate the performance of the machine learning techniques. It is observed that among the above four machine learning algorithms, RF technique yields the classification result, with more accuracy. Secondly, n-gram based classification of reviews are carried out on the aclIMDb dataset..

Networks and trust: systems for understanding and supporting internet security

Includes bibliographical references.2022 Fall.This dissertation takes a systems-level view of the multitude of existing trust management systems to make sense of when, where and how (or, in some cases, if) each is best utilized. Trust is a belief by one person that by transacting with another person (or organization) within a specific context, a positive outcome will result. Trust serves as a heuristic that enables us to simplify the dozens decisions we make each day about whom we will transact with. In today's hyperconnected world, in which for many people a bulk of their daily transactions related to business, entertainment, news, and even critical services like healthcare take place online, we tend to rely even more on heuristics like trust to help us simplify complex decisions. Thus, trust plays a critical role in online transactions. For this reason, over the past several decades researchers have developed a plethora of trust metrics and trust management systems for use in online systems. These systems have been most frequently applied to improve recommender systems and reputation systems. They have been designed for and applied to varied online systems including peer-to-peer (P2P) filesharing networks, e-commerce platforms, online social networks, messaging and communication networks, sensor networks, distributed computing networks, and others. However, comparatively little research has examined the effects on individuals, organizations or society of the presence or absence of trust in online sociotechnical systems. Using these existing trust metrics and trust management systems, we design a set of experiments to benchmark the performance of these existing systems, which rely heavily on network analysis methods. Drawing on the experiments' results, we propose a heuristic decision-making framework for selecting a trust management system for use in online systems. In this dissertation we also investigate several related but distinct aspects of trust in online sociotechnical systems. Using network/graph analysis methods, we examine how trust (or lack of trust) affects the performance of online networks in terms of security and quality of service. We explore the structure and behavior of online networks including Twitter, GitHub, and Reddit through the lens of trust. We find that higher levels of trust within a network are associated with more spread of misinformation (a form of cybersecurity threat, according to the US CISA) on Twitter. We also find that higher levels of trust in open source developer networks on GitHub are associated with more frequent incidences of cybersecurity vulnerabilities. Using our experimental and empirical findings previously described, we apply the Systems Engineering Process to design and prototype a trust management tool for use on Reddit, which we dub Coni the Trust Moderating Bot. Coni is, to the best of our knowledge, the first trust management tool designed specifically for use on the Reddit platform. Through our work with Coni, we develop and present a blueprint for constructing a Reddit trust tool which not only measures trust levels, but can use these trust levels to take actions on Reddit to improve the quality of submissions within the community (a subreddit)

SOCIAL NETWORK INFLUENCE ON RIDESHARING, DISASTER COMMUNICATIONS, AND COMMUNITY INTERACTIONS

The complex topology of real networks allows network agents to change their functional behavior. Conceptual and methodological developments in network analysis have furthered our understanding of the effects of interpersonal environment on normative social influence and social engagement. Social influence occurs when network agents change behavior being influenced by others in the social network and this takes place in a multitude of varying disciplines. The overarching goal of this thesis is to provide a holistic understanding and develop novel techniques to explore how individuals are socially influenced, both on-line and off-line, while making shared-trips, communicating risk during extreme weather, and interacting in respective communities. The notion of influence is captured by quantifying the network effects on such decision-making and characterizing how information is exchanged between network agents. The methodologies and findings presented in this thesis will benefit different stakeholders and practitioners to determine and implement targeted policies for various user groups in regular, special, and extreme events based on their social network characteristics, properties, activities, and interactions

What have hosts overlooked for improving stay experience in accommodation-sharing? Empirical evidence from Airbnb customer reviews

Purpose In accommodation-sharing, hosts must provide satisfactory stay experiences for guests, who will then express intentions to revisit (behavioral loyalty) and/or recommend the experiences to others (attitudinal loyalty) in their reviews. Through the lens of expectation-confirmation theory, this study aims to investigate the service dimensions customers focus on in their reviews and their relationships with customer-loyalty manifestations in accommodation-sharing. Design/methodology/approach This study uses topic modeling to discover distinctive dimensions from Airbnb reviews from a micro perspective and map them onto overarching themes from a macro perspective, and further examine the relationships among topics using cluster analysis. Findings This study reveals “information” as an important theme rarely mentioned in the literature. Besides, “homeliness” is a unique dimension associated with behavioral and attitudinal loyalty toward accommodation-sharing. Practical implications The findings help accommodation-sharing platforms and hosts identify customer concerns and the drivers of customer loyalty in accommodation-sharing. Originality/value In the existing literature, customer perceptions and loyalty are largely determined through surveys, and the findings are not univocal due to the inconsistencies of measurement items used, the potential response bias and limited sample sizes. This study capitalizes on the wealth of user-generated content and extracts service dimensions and customer loyalty directly from textual reviews, overcoming previous research limitations

Statistical data mining for Sina Weibo, a Chinese micro-blog: sentiment modelling and randomness reduction for topic modelling

Before the arrival of modern information and communication technology, it was not easy to capture people’s thoughts and sentiments; however, the development of statistical data mining techniques and the prevalence of mass social media provide opportunities to capture those trends. Among all types of social media, micro-blogs make use of the word limit of 140 characters to force users to get straight to thepoint, thus making the posts brief but content-rich resources for investigation. The data mining object of this thesis is Weibo, the most popular Chinese micro-blog. In the first part of the thesis, we attempt to perform various exploratory data mining on Weibo. After the literature review of micro-blogs, the initial steps of data collection and data pre-processing are introduced. This is followed by analysis of the time of the posts, analysis between intensity of the post and share price, term frequency and cluster analysis. Secondly, we conduct time series modelling on the sentiment of Weibo posts. Considering the properties of Weibo sentiment, we mainly adopt the framework of ARMA mean with GARCH type conditional variance to fit the patterns. Other distinct models are also considered for negative sentiment for its complexity. Model selection and validation are introduced to verify the fitted models. Thirdly, Latent Dirichlet Allocation (LDA) is explained in depth as a way to discover topics from large sets of textual data. The major contribution is creating a Randomness Reduction Algorithm applied to post-process the output of topic models, filtering out the insignificant topics and utilising topic distributions to find out the most persistent topics. At the end of this chapter, evidence of the effectiveness of the Randomness Reduction is presented from empirical studies. The topic classification and evolution is also unveiled

Term Association Modelling in Information Retrieval

Many traditional Information Retrieval (IR) models assume that query terms are independent of each other. For those models, a document is normally represented as a bag of words/terms and their frequencies. Although traditional retrieval models can achieve reasonably good performance in many applications, the corresponding independence assumption has limitations. There are some recent studies that investigate how to model term associations/dependencies by proximity measures. However, the modeling of term associations theoretically under the probabilistic retrieval framework is still largely unexplored. In this thesis, I propose a new concept named Cross Term, to model term proximity, with the aim of boosting retrieval performance. With Cross Terms, the association of multiple query terms can be modeled in the same way as a simple unigram term. In particular, an occurrence of a query term is assumed to have an impact on its neighboring text. The degree of the query term impact gradually weakens with increasing distance from the place of occurrence. Shape functions are used to characterize such impacts. Based on this assumption, I first propose a bigram CRoss TErm Retrieval (CRTER2) model for probabilistic IR and a Language model based model CRTER2LM. Specifically, a bigram Cross Term occurs when the corresponding query terms appear close to each other, and its impact can be modeled by the intersection of the respective shape functions of the query terms. Second, I propose a generalized n-gram CRoss TErm Retrieval (CRTERn) model recursively for n query terms where n>2. For n-gram Cross Term, I develop several distance metrics with different properties and employ them in the proposed models for ranking. Third, an enhanced context-sensitive proximity model is proposed to boost the CRTER models, where the contextual relevance of term proximity is studied. The models are validated on several large standard data sets, and show improved performance over other state-of-art approaches. I also discusse the practical impact of the proposed models. The approaches in this thesis can also provide helpful benefit for term association modeling in other domains

CREATE: Concept Representation and Extraction from Heterogeneous Evidence

Traditional information retrieval methodology is guided by document retrieval paradigm, where relevant documents are returned in response to user queries. This paradigm faces serious drawback if the desired result is not explicitly present in a single document. The problem becomes more obvious when a user tries to obtain complete information about a real world entity, such as person, company, location etc. In such cases, various facts about the target entity or concept need to be gathered from multiple document sources. In this work, we present a method to extract information about a target entity based on the concept retrieval paradigm that focuses on extracting and blending information related to a concept from multiple sources if necessary. The paradigm is built around a generic notion of concept which is defined as any item that can be thought of as a topic of interest. Concepts may correspond to any real world entity such as restaurant, person, city, organization, etc, or any abstract item such as news topic, event, theory, etc. Web is a heterogeneous collection of data in different forms such as facts, news, opinions etc. We propose different models for different forms of data, all of which work towards the same goal of concept centric retrieval. We motivate our work based on studies about current trends and demands for information seeking. The framework helps in understanding the intent of content, i.e. opinion versus fact. Our work has been conducted on free text data in English. Nevertheless, our framework can be easily transferred to other languages