This Degree Project presents the antiracist practices in the Italian social media scenario, and specifically on Instagram to create new contributions on the literature of antiracism and social media, serving as a framework to identifying racist behaviours and discourse. The strategies and tools of antiracist practice are used for the analysis, in addition to critical race theory and critical discourse analysis for the methodology. Texts and specific keywords are analysed through different social media accounts and posts, in order to find out what terminology is used and whether different minorities use the same kind of language or what changes. The focus for the analysis is on what ways minorities are represented and in what media format, what types of representation, words and stereotypes are used and what forms of counter-storytelling are the authors creating. Counter-stories stand in opposition to dominant narratives, the so-called majoritarian stories and represent the main thread across all social media posts analysed, with the aim of undermining racist behaviours and practice. Racial discrimination in everyday life and personal stories of racism discrimination appeared to be among the main categories chosen by the creators to build up social media posts and to create counter-storytelling. The three organisations selected are aware of the antiracist discourse and key words repeated include racism, discrimination and white (privilege), also in hashtags. The engagement rate and reels views reveal how involved the followers are with the content and what type of posts are more successful, including twice the prevalence of video content over static one. On top of Instagram, 4 YouTube videos were also analysed, showing how different minorities in Italy are perceived by Italian white people, with the focus on what kind of biases and stereotypes emerge the most.

<p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

I fordonsbranschen pågår en förändring av den 120-åriga försäljningsmodell som lagt grunden för en bilhandlarstruktur som idag finns etablerad över hela världen. Det finns skarpa indikationer på att fordonsbranschen med fokus på återförsäljarleden har påbörjat en digitalisering som innebär att rollen för de traditionella bilåterförsäljarna står inför en betydande förändring. Uppsatsen syftar till att studera drivkrafter och hinder för digitalisering hos bilåterförsäljare och undersöker hur digitaliseringen påverkar företagen på en övergripande nivå, framförallt i fordonsbranschens försäljningsled. Syftet är också att identifiera hur bilåterförsäljare resonerar kring och tillämpar den digitala bilaffären. För att ge möjlighet till en fördjupad förståelse har empiriskt material samlats in genom fyra kvalitativa intervjuer med branschaktörer som är tidigt ute med att utnyttja digitaliseringens möjligheter. Utifrån en litteraturstudie diskuteras rådande utvecklingstendenser för det empiriska resultatet i ljuset av utvalda teorier. Resultatet visar att digitaliseringen lett fordonsbranschen in i en omfattande branschförändring. Den digitala bilaffären är utifrån Christensens (1997) modell att betrakta som en disruptiv innovation som kommer att leda till irreversibla branschförändringar över tid. Innovationsspridningen är i skedet där tidiga användare tagit till sig innovationen och kommer att påverka hela branschen under en lång tid innan den sena majoriteten tagit till sig innovationen. Den tydligaste drivkraften är att den digitala bilaffären motiverar biltillverkarna att rationalisera ett led i nybilsförsäljningen och öka förtjänstmöjligheterna genom att sälja direkt till kunderna. Detta driver på återförsäljarna att hitta sina sätt att dra nytta av den digitala bilaffären och identifiera vilka nya affärsmöjligheter som kan utvecklas. Ett tydligt hinder som kommer att bromsa användningen av digitala lösningar är osäkerheten hos kunder när det gäller att våga köpa en bil utan att se och provköra. Uppsatsen har även mynnat ut i en enkel modell med fyra nyckelområden för utveckling av den digitala bilaffären; nya konsumentbeteenden, digital närvaro, transparens, förändrade värdeerbjudanden. Med denna som utgångspunkt kan befintliga affärsmodeller och marknadsföringsstrategier utvärderas och anpassas till branschens omställning genom att ställa sig frågan hur den pågående förändringen faktiskt påverkar ”oss” som återförsäljare. / In the automotive industry, there is an ongoing change focusing the 120-year-old sales model that laid the foundation for a car dealer structure that is established all over the world. There are sharp indications that the automotive industry, with a focus on the car retailers, has begun digitalization, meaning that the role of traditional car retailers is facing a significant change. The study aims at studying the driving forces and obstacles to digitalization among car retailers and examines how digitalization affects companies on an overall level, especially among retailers within the automotive industry. The purpose is also to identify how car retailers reason about and apply the digital car retail. To provide an opportunity for an in-depth understanding, empirical data has been collected through four qualitative interviews with industry actors who are early adapters of the possibilities provided by digitalization. Based on a literature study, prevailing development trends are discussed in the empirical result in the light of selected theories. The results show that digitalization has led the automotive industry into a comprehensive industry change. Based on Christensen's (1997) model, the digital car business is to be regarded as a disruptive innovation that will lead to irreversible industry changes over time. The spread of innovation is at the stage where early adopters have embraced the innovation and will affect the entire industry for a long time before the late majority embraces the innovation. The clearest driving force is that digital car retailing motivates car manufacturers to rationalize one stage in new car sales and increase profit opportunities by selling directly to customers. This pushes retailers to find their ways to take advantage of digital car retailing and identify what new business opportunities can be developed. A clear obstacle that will slow down the use of digital solutions is the uncertainty among customers when it comes to dare buying a car without seeing and test driving. The study has also resulted in a simple model with four key areas for the development of digital car retailing; new consumer behaviors, digital presence, transparency, changed value offerings. With this as a starting point, existing business models and marketing strategies can be evaluated and adapted to the industry's transformation by asking the question how the ongoing change actually affects “us” as car retailers.

The amount of garbage is rapidly increasing in China  nowadays, which has led to severe environmental  problems. Garbage recycling is imperative in dealing with the problem and garbage classification plays an essential role in the recycling process. However, most Chinese have weak awareness of garbage classification. Therefore, how to motivate Chinese to classify garbage correctly has been of concern. Persuasive technology is used to intentionally change user’s behaviors or/and attitudes by designed persuasion. This project is aimed at exploring whether persuasive technology can be employed to appeal to the motivation of Chinese for garbage classification. Persuasive strategy in this thesis is based on literature research. Besides, a prototype is developed to implement the persuasive strategy. Through user testing and interviews, it shows that persuasive technology can indeed appeal to the users’ motivation for garbage classification. / Mängden skräp ökar snabbt i Kina för närvarande, vilket har lett till allvarliga miljöproblem. Återvinning av avfall är avgörande för att hantera problemet och skräpklassificering spelar en viktig roll i återvinningsprocessen. De flesta kineser har dock svag medvetenhet om skräpsklassificering. Därför har det varit oroande hur man kan motivera kineser att klassificera skräp korrekt. Övertalande teknik används för att avsiktligt ändra användarens beteenden eller attityder genom utformad övertalning. Projektet syftar till att undersöka om övertygande teknik kan användas för att vädja till motiv för kineser för skräpklassificering. Övertalande strategi i denna avhandling bygger på litteraturforskning. Dessutom utvecklas en prototyp för att implementera den övertygande strategin. Genom användartestning och intervjuer visar det att övertygande teknik verkligen kan vädja användarnas motivation för skräpsklassificering.

As social media more or less in the last decade has become an integral part in many people's lives, discussions have taken place about what a healthy amount of screen time is. Especially among young users, excessive social media use has been linked with a decline in mental health. In general, it is common that people find their phones distractive, and no wonder, as social media apps are deliberately designed to keep users on for as much time as possible. In the year 2018, the discussions increased in intensity, putting more pressure on large tech-companies to take responsibility. As a result of this, several functions have been integrated into different smartphones such as the iPhone and Android phones. One of these functions is a grayscale-filter that removes the addictive colors from the screen and turns it black and white. Since earlier studies have been made on this topic, this study has been narrowed down to focus on university students that use Instagram on a daily basis, to examine whether this grayscale-filter is effective in reducing their screen time on the platform. 17 students were included in the study. First, data on their regular screen time on Instagram was recorded along with qualitative information on their relation with the platform. The participants were then instructed to use the grayscale-mode for 7 days, whereafter their screentime on Instagram for those 7 days was recorded. Afterwards, the participants were again asked qualitative questions. Brief interviews were held with 5 arbitrarily chosen participants to get more elaborate answers. The result of the study shows that the students spent less time on Instagram during a one week period with the grayscale-mode turned on. However, a large majority of the students experienced that the setting was needlessly worsening other aspects of their phone use. Because of this, most of the students were, when asked, not inclined on using the setting again in the future. / I och med att sociala medier under det senaste årtiondet mer eller mindre utvecklats till att spela en väsentlig roll i många människors liv, har diskussioner skapats kring vad en hälsosam skärmtid egentligen är. Framförallt bland unga användare har det funnits samband mellan ökad psykisk ohälsa och relativt hög användning av sociala medier. På ett mer generellt plan verkar många uppleva sin mobil som distraherande, och det är inte speciellt konstigt eftersom sociala medier medvetet är designade att hålla kvar användare så länge som möjligt. År 2018 tog diskussionerna fart ytterligare och satte press på de stora techbolagen att ta mer ansvar. Samma år, möjligen som ett svar på detta integrerades ett flertal funktioner i olika typer av smartphones såsom iPhone och Android telefoner. En av dessa funktioner är ett gråskaleläge som får de intresseväckande färgerna på skärmen att försvinna genom att göra den svartvit. Då det tidigare har utförts studier på ämnet, har den här studien specifikt avgränsats till universitetsstudenter som använder Instagram på en daglig basis, för att undersöka om gråskaleläget kan hjälpa dem att minska sin skärmtid på plattformen. 17 studenter inkluderades i denna undersökning. Först samlades data in kring deras nuvarande skärmtid på Instagram, tillsammans med kvalitativ information angående deltagarnas relation till plattformen. Studiedeltagarna instruerades sedan att använda gråskaleläget under 7 dagar varefter datan om skärmtiden för dessa 7 dagar samlades in. Efter detta fick studiedeltagarna återigen besvara ett antal kvalitativa frågor, och korta intervjuer hölls med 5 slumpmässigt utvalda deltagare för att få mer utvecklade svar på de frågor som ligger som underlag för studien. Resultaten till studien visar att studiedeltagarna spenderade mindre tid på Instagram under 7-dagars perioden med gråskaleläget på. Däremot upplevde en stor majoritet att inställningen försämrade andra aspekter av deras telefon, till den graden att de inte var positivt inställda till att använda sig av gråskaleläget igen.

Two of the most important challenges in network service assurance are * an overwhelming flow of low-quality alarms * to understand the structure and quality of the delivered services This thesis proposes solutions for alarm and service monitoring that addresses monitoring of large scale multi-service mobile networks. The work on alarms are based on statistical analysis of data collected from a real-world alarm flow and an associated trouble ticket database containing the network administrators expert knowledge. Using data from the trouble ticketing system as a reference, we examine the relationship between the original alarm severity and the human perception of the alarm priority. Using this knowledge, we suggest a neural network-based approach for alarm prioritization. Tests using live data show that our prototype assigns the same severity as a human expert in 50% of all cases, compared to 17% for a na¨ıve approach. In order to model and monitor the services this thesis propose a novel domain specific language called SALmon, which allows for efficient representation of service models, together with a computational engine for evaluation of service models. We show that the proposed system is a good match against real-world scenarios with special requirements around service modeling. / Två av de största problemen inom nätverksövervakning är:• överflöd av larm med dålig kvalitet• att förstå struktur och kvalitet på levererade tjänsterAvhandlingen föreslår lösningar inom larm- och tjänsteövervakning för storskaliga mobila nät med olika typer av tjänster. Arbetet med larm är baserat på en verklig larm- och ärendedatabas från en mobiloperatör. Vi har undersökt relationen mellan larmets ursprungliga prioritet och den prioritet som sätts av experter i ärendehanteringssystemet. Baserat på denna information föreslår vi en lösning för automatisk larmprioritering med hjälp av ett neuralt nätverk. Tester visar att vår prototyp sätter samma prioritet som en mänsklig expert i 50% av fallen jämfört med 17% för prioriteten som skickas i larmet.För att modellera och övervaka tjänster föreslår vi ett domänspecifikt språk, SALmon. SALmon gör det möjligt att uttrycka tjänstemodeller och även att beräkna status på dessa. Vi visar att lösningen matchar krav från mobila operatörer.

On-line social networking and communication services are increasingly popular methods to communicate with friends, family and communities. Statistics shows that users of services like Facebook and Twitter stretches across geographical locations, professions, age groups and habits. Smart mobile devices with Internet connectivity simplifies access to these services at anytime and from almost anywhere. However, the huge amount of user-generated content makes it difficult to identify useful information. A challenge is to create micro-communities where users may join in from heterogeneous social networks using proper user and identity management. The increasing number of social networks and communication services are also creating new challenges in social media content filtering, micro-community discovery, automatic group communication initialization.This licentiate thesis proposes to utilize social graphs for improving group communication. It therefore presents a framework that manages information harnessed from social-networking services and personal devices such as mobile phones and laptops. The framework can identify individual communication patterns and use these to calculate a social strength between users expressed as a weighted social graph.The central component of the framework is a social recommendation engine for social content filtering, group management and communication pattern discovery. The engine harness personalized social data (both content and contact) from the social-networking services and personal devices. The framework also contains methods for social strength calculation based on a unified interaction model that supports communication pattern discovery. A comparison study is presented together with the framework, which evaluates different social strength computation methods based on a simulated interaction dataset. The feasibility of social data collection from social networks and communication services are also discussed to illuminate potential benefits of the framework for the next generation of communication tools (such as mobile video conferencing).Evaluation of the framework is initially done by proof-of-concept prototypes that illustrate functional feasibility. Two prototypes are presented in this thesis, a presence information viewer that filters and prioritizes contacts and a real-time photo sharing application utilizing calendar data for initiation of group communication. In conclusion, improving group communication by offering services for micro-communities, based on our communication habits, personal interests and context (such as activity and location) is technically realistic and feasible.

The paradigm of pervasive computing aims to integrate the computing technologies in a graceful and transparent manner, and make computing solutions available anywhere and at any time. Different aspects of pervasive computing, like smart homes, smart offices, social networks, micromarketing applications, PDAs, etc. are becoming a part of everyday life. Context of pervasive computing system is any piece of information that can be of possible interest to the system. Context often includes location, time, activity, surroundings, etc. One of the core features of pervasive computing systems is context awareness – the ability to use context information to the benefit of the system. The thesis proposes a set of context prediction and situation prediction methods on top of enhanced situation awareness mechanisms. Being aware of the future context enables a pervasive computing system to choose the most efficient strategies to achieve its stated objectives and therefore a timely response to the upcoming situation can be provided. This thesis focuses on the challenges of context prediction, but in order to become really efficient and useful, context prediction approaches need to be gracefully integrated with different other aspects of reasoning about the context. This thesis proposes a novel integrated approach for proactively working with context information. In order to become efficient, context prediction should be complemented with proper acting on predicted context, i.e. proactive adaptation. The majority of current approaches to proactive adaptation solves context prediction and proactive adaptation problems in sequence. This thesis identifies the shortcomings of that approach, and proposes an alternative solution based on reinforcement learning techniques. The concept of situation provides useful generalization of context data and allows eliciting the most important information from the context. The thesis proposes, justifies and evaluates improved situation modeling methods that allow covering broader range of real-life situations of interest and efficiently reason about situation relationships. The context model defines the pervasive computing system’s understanding of its internal and external environments, and determines the input for context prediction solutions. This thesis proposes novel methods for formal verification of context and situation models that can help to build more reliable and dependable pervasive computing systems and avoid the inconsistent context awareness, situation awareness and context prediction results. The architecture of pervasive computing system integrates all the aspects of context reasoning and governs the interaction and collaboration between different context processing mechanisms. This thesis proposes, justifies and evaluates the architectural support for context prediction methods. The novel architectural solutions allow encapsulating various practical issues and challenges of pervasive computing systems and handling them on low levels of context processing, therefore, supporting the efforts for efficient context prediction and proactive adaptation.

Mobility support for users connecting to the Internet is an increasing trend. Different types of access networks like WiFi, CDMA, and UMTS are available, creating a heterogeneous access network environment. In the Internet today, there are a number of providers of various sizes supporting different technologies. Moving between such operators different types of authentication methods are often used interrupting ongoing services. This, in combination with lacking roaming agreements makes mobility among them with maintained connectivity and uninterrupted services difficult or even impossible.This thesis proposes an extended functionality to the Authentication, Authorization and Accounting, (AAA) protocol that enables a single AAA infrastructure to exist in a heterogeneous network environment and that enables interconnection between different operators in a tree-like structure of AAA servers. Mobile devices will maintain their IP address while connected to a network different from the home network independent of the network access technology. Furthermore, a scalability study is carried out in order to determine what is required from an AAA system in order for it to perform when dealing with larger numbers of users, service providers as well as supporting new technologies. A method for providing information for handover decisions for intra- and inter-operator mobility is also proposed. The proposed method selects the access network that according to a metric based on jitter and delay shows best performance.Evaluations show that authentication and IP address assignment can be supported in an efficient way in comparison with state of the art for both Ethernet and PPP based access networks using a common AAA infrastructure. CPU, memory and network link capacity in the home AAA server are identified as the primary bottlenecks when discussing scalability in RADIUS based AAA infrastructures and guidelines are proposed to address scalability issues during system design. The metric proposed to support in handover decisions shows that bandwidth can be estimated with more than 90% accuracy for WiFi, CDMA, and UMTS access networks.

This licentiate thesis discusses how lifelogging technologies can be used to build digital reminiscence systems. Lifelogging is a recent pervasive computing trend where different aspects of someone’s life are captured digitally. The aim of the proposed system is to create digital lifestories that can visualize the life of a person and provide a means for retrieving life experiences. The target users are people with mild dementia who have problems in navigating their daily life and in recalling previous events. The claim is that digital lifestories can be utilized for memory and reminiscence support as well as strengthen the bond between a person with mild dementia and his family. The main focus of the research study is about designing and developing digital reminiscence systems that can be used by people with mild dementia as aiding memory tools. Creating digital lifestories requires capturing of context data, such as places and people, and content data, such as sound and images, using pervasive lifelogging tools. The passive and continues capture of data results in the occurrence of false data and noise. For that, the system should reduce the collected data to not overload the user when reviewing the lifelogs. Another problem is that the life should be segmented in the form of activities that are searchable and accessible. Thus the collected lifelog data should be aggregated and structured into semantic activities and then represented as digital lifestories where context data can be retrieved together with related content. This licentiate thesis proposes solutions for filtering collected data to reduce the user’s efforts when reminiscing. The thesis also presents a method that uses prior knowledge of context data to improve the recognition of activities when creating the digital lifestories. In addition, locations where the user spends significant time can help in determining context parameters such as activities. This licentiate thesis proposes a novel approach that collects and clusters logged locations of the user to improve the activity recognition task. The presented approach defines possible places first, and it then identifies activities based on those places. Images, as content data, are then associated with the activities based on their timeframes so the user can review and adjust the data before saving it to his lifestory. The presented digital reminiscence system was evaluated through a field-test involving 10 people with mild dementia together with their caregivers. Healthcare professionals were also involved in the design and the evaluation of the system to improve the outcome of the study. The preliminary results indicate that the system indeed improves the quality of life for people with mild dementia, as their reminiscence processes are encouraged and that the communication with their surroundings increases in both volume and quality. The thesis shows that digital reminiscence systems, which describe life through activities, can increase the perceived quality of life for people with mild dementia. It also shows that activity recognition can be improved by using prior knowledge of context data and by automatic location clustering.