Jobbsökning är den process som syftar till att matcha arbetssökande med lämpliga jobb och jobbmöjligheter. Den vanligaste metoden för jobbsökning är användningen av Internet, som tillhandahåller diverse sociala nätverk. Dessa tjänster lockar miljontals besökare varje år, vilket bland annat resulterat i att Internet i nuläget spelar en genomgripande roll vad gäller jobbsökning och sysselsättning. Internet och dess informations- och kommunikationsteknologi kommer framöver fortsätta ta en mer betydelsefull roll i en del av arbets- och jobbsökningsprocessen, vilket efterlämnar dagens socialt missgynnade arbetssökande eftersom nätverken inte täcker dessa individers behov. Således utvärderades konceptet FindMeAJob vars idé handlar om att sammanknyta socialt missgynnade arbetssökande med olika typer av individer och verksamheter som är i behov av arbetskraft. Studien rekryterade socialt missgynnade arbetssökande, HR-specialister, rekryterare och Arbetsmarknadsförvaltningen i Helsingborg för att besvara frågeformulär baserat på Likertskala, fördjupningsfrågor och Storyboard. Resultaten var blandade; respondenter för gruppen socialt missgynnade arbetssökande ser i helhet positivt på FindMeAJob och anser att deras behov kan täckas någorlunda, medan sakkunniga respondenter har delade meningar. En djupare analys av resultatet resulterade i en slutsats om vad FindMeAJob torde innehålla för att möta de behov som kan ställas av de socialt missgynnade arbetssökande.

Modern vehicles nowadays are equipped with highly sensitive sensors which continuously log in the information when the vehicle is in motion. These vehicles also deal with some performance issues like more fuel consumption, breakdown, or failure, etc. The information logged in by the sensors can be useful to analyze and evaluate these performance issues.  As vehicles are there in the market and are used in multiple places. These vehicles can perform differently based on the way they are operated and driven and the usage of a vehicle varies from time to time. Moreover, the European Accident Research and Safety Report from Volvo Organization describes the factors responsible for road fatalities and accidents. It explains that 90\% of road fatalities are caused by the style of the vehicle being driven and 30\% is caused by the external weather and environmental factor. Therefore, in this work, vehicle usage modeling is done based on time to determine the different usage styles of a vehicle and how they can affect a vehicle's performance. The proposed framework is divided into four separate modules namely: Data pre\textendash processing, Data segmentation, Unsupervised machine learning, and Pattern Analysis. Mainly, ensemble clustering methods are used to extract the pattern of the vehicle usage style and vehicle performance in different seasons using truck logged vehicle data (LVD). From the results, we could build a strong correlation between the vehicle usage style and the vehicle performance that would require further investigation.

In recent years, the tech is moving towards a more automized and smarter software. To achieve smarter software the implementation of AI is a step towards that goal. The forest industry needs to become more automized and decrease the manual labor. Decreasing manual labor will both have a positive impact on both the cost and the environment. After doing a literature study the conclusion was to use Mask R-CNN to be able to make the AI learn about the pattern of the different stands. The different stands were extracted and masked for the Mask R-CNN. First there was a comparison between the usage of a computer versus Google Colab, and the results show that Google Colab did deliver the results a little faster than on the computer. Using a smaller area with fewer stands gave a better result and decreased the risk of the algorithm crashing. Using 42 areas with about 10 stands in each gave better results than using one big area with 3248 stands. Using 42 areas gave the result of an average IoU of 42%. Comparing this to 6 areas with about 10 stands each gave the result of 28% IoU. The result of increasing the data split to 70/30 did gave the best IoU with the value of 47%.

Natural disasters disrupt our daily life and cause many sufferings. Among the various natural disasters, flood is one of the most catastrophic. Assessing flood risk helps to take necessary precautions and can save human lives. The assessment of risk involves various factors which can not be measured with hundred percent certainty. Therefore, the present methods of flood risk assessment can not assess the risk of flooding accurately.  This research rigorously investigates various types of uncertainties associated with the flood risk factors. In addition, a comprehensive study of the present flood risk assessment approaches has been conducted. Belief Rule Base expert systems are widely used to handle various of types of uncertainties. Therefore, this research considers BRBES’s approach to develop an expert system to assess the risk of flooding. In addition, to facilitate the learning procedures of BRBES, an optimal learning algorithm has been proposed. The developed BRBES has been applied taking real world case study area, located at Cox’s Bazar, Bangladesh. The training data has been collected from the case study area to obtain the trained BRB and to develop the optimal learning model. The BRBES can generate different "What-If" scenarios which enables the analysis of flood risk of an area from various perspectives which makes the system robust and sustainable. This system is said to be intelligent as it has knowledge base, inference engine as well as the learning capability.

Research at Miun investigates the so called “Internet of Things”. The research has so far produced components for sharing sensor and actuator information on the Internet, most notably the Sensible Things platform. The Sensible Things platform can run on many different devices such as smart phones, raspberry Pi devices, and desktop computers. However, programming applications based on the Sensible Things platform and deploying it on the hardware require knowledge of the Java programming language and the API of the platform. Today, many novice and first time programmers learn to program using the Blockly programming method, which include connecting blocks together to form simple procedures. Therefore, we have applied the Blocky method to ease the development of simple applications on the Sensible Things platform. After the work was done, the general users are now able to create entry level Sensible Things Applications.

Background. The Internet of things (IoT) combines sensors and connectivity and can be applied to a wide variety of things. The security in these IoT devices is usually constrained by their limited hardware. When IoT handles sensitive data, security becomes an important challenge. Blockchain technologies enable safe transactions between two parties without involving any third party. The technology provides integrity to the two parties, ensuring the transaction is valid. Objectives. The objectives of this paper is to examine the applicability of a blockchain in an IoT system. The paper aims to find out the current threat map to IoT, Identify the security benefits of blockchain technologies and investigate an IoT home security system on the market. Methods. The threat map of IoT and the benefits of blockchain technologies was determined by studying existing literature. IoT vulnerabilities were assessed with respect to exploitation difficulty and severity. The security level in the IoT home security system was evaluated with a penetration test. Results. The most critical vulnerabilities of IoT today are weak guessable passwords, insecure updating mechanisms, insecure ecosystem interfaces, insecure data transfer and storage and use of insecure of outdated components. A big reason for the poor security is the lack of integrity in IoT systems. The blockchains can provide integrity, authenticity, transparency, decentralisation and more. The security level of the IoT home security system has security countermeasures implemented to protect from a basic level of threats. Conclusion. The majority of the current threats to IoT can be mitigated with integrity provided by a blockchain. From a security perspective blockchain technologies prove to be worth considering. / Bakgrund. Internet of Things (IoT) kombinerar sensorer med anslutning till internet och kan tillämpas på en mängd olika föremål. Säkerheten hos IoT är ofta hindrad av dess begränsade hårdvara. När IoT hanterar känslig information blir säkerhet en viktig utmaning. Blockkedje-teknologier möjliggör säkra transaktioner mellan två parter utan att involvera en tredje part. Teknologin garanterar integritet till de två parterna och försäkrar att deras transaktion är giltlig. Syfte. Syftet med denna uppsats är att utforska tillämpligheten av att integrera blockkedje-teknologier i IoT-system. Målet är att hitta en tidsenlig hotbild till IoT, identifiera säkerhetsfördelar med blockkedjor samt att utforska säkerhetsnivån i ett populärt larmsystem på marknaden. Metod. Hotbilden till IoT och fördelar med blockkedjor bedömdes genom att studera befintlig litteratur. Sårbarheter i IoT bedömdes med hänsyn till svårigheten att exploatera och allvarlighetsgraden. Säkerhetsnivån i IoT-produkten utvärderades med ett penetrationstest. Resultat. De mest kritiska sårbarheterna i IoT idag är svaga lösenord, osäkra uppdateringsmetoder, osäkra ekosystemsinterfacer, osäkra dataöverföringar och osäker datalagring och användning av osäkra och utdaterade komponenter. EN vanlig orsak till den otillräckliga säkerhetsnivån är bristande integritet. Blockkedje-teknologier kan bidra med integritet, autencitet, genomsynlighet, decentralisering och mer. Säkerhetsnivån på det undersökta larmsystemet hade säkerhetsåtgärder mot det grundliga penetrationstest som utfördes i denna fallstudie. Slutsatser. Majoriteten av de tidsenliga hot till IoT kan lindras med integriteten som en blockkedja förser. Från ett säkerhetsperspektiv visade det sig att blockedje-teknologin är värd att överväga.

The restrictions that have been introduced with the Covid-19 pandemic has led to stores having less customers and stores shutting down. There is a need to track people's occupancy in public places so there could be more people in those places but still following the restrictions. This study evaluates and compares three different heat sensors to see if they could be used for this. The three heat sensors that have been used in this study are both low-cost and low-resolution sensors. The choice of low-resolution heat sensors is important to not intrude on people's privacy. In this study, there were three different experiments performed on all the three sensors. The experiments were done to see how sunlight could affect the sensors, what advantages, and disadvantages there are by placing the sensors in different positions, and how good the sensors are at separating people. The collected results gave a good basis to answer the research questions and better understand the sensors chosen in this research. The results also showed that the sensors can be used as an aid against a pandemic when restrictions must be kept. In the discussion we talk about how the sensors performed in different experiments and how they performed against each other. Our chosen method is also discussed and what we think were good and what went wrong so others do not repeat our mistakes. The paper ends with our conclusions and suggestions for future work.

A measuring system Striton has been developed at the department of biomedicalengineering, University Hospital of Umeå, for motion analysis using motion sensors whichattaches to the patient’s lower body to assess the risk for potential neurological andmusculoskeletal damage. The measuring system is comprised of two motion sensor unitsand a remote control where data is gathered based on step height, step width, theorientation of the calves and step frequency. The motion sensor units which attach to thecalves are comprised of a MCU with a built in Wi-Fi module, a IMU and and time-of-flightsensors. Data is transferred through Wi-Fi and stored on a SD-card as CSV-files on theremote control which is comprised of a Raspberry Pi Zero WH running a Linux operatingsystem (Raspbian). The remote control also has the functions to initiate and complete ameasurement as well as mark an event. The extraction of data from the remote control toanother unit for analysis occurs through SSH and SFTP using third-party programs whichmay require technical knowledge. A unique software was designed specifically for Stritonfor the operating system Windows 10 using Visual Studio (2019) which provides thefunctions to connect to a predefined Wi-Fi access point as well as automatically reconnectto previously connected access point at shutdown, connect through SFTP, list the savedfiles on the remote control, perform file operations, synchronize the date and time on theremote control as well as change settings in the software which is saved locally in a settingsfile. The user interface is minimalistic with the intention to reduce complexity and timerequirement to extract the data from the measuring system Striton. / Ett mätsystem Striton har utvecklats av CMTS, Medicinsk Teknik – FoU på NorrlandsUniversitetssjukhus för rörelseanalys med hjälp av rörelsesensorer som fästs på patientensunderben för att bedöma en potentiell neurologisk samt muskuloskeletal skada.Mätsystemet består av två sensorenheter samt en fjärrkontroll där data samlas in baseratpå höjd av steg, stegbredd, underbenens orientering samt stegfrekvens. Sensorenheternasom fästs på underbenen består av en MCU med inbyggd Wi-Fi modul, IMU och time-offlight sensorer. Data förs över via Wi-Fi och lagras på ett SD-kort i form av CSV-filer påfjärrkontrollen beståendes av en Raspberry Pi Zero WH som driver ett Linuxoperativsystem (Raspbian). Fjärrkontrollen har även funktionerna att kunna starta ochstoppa en mätning samt markera en händelse. Extrahering av data från fjärrkontrollen tillen annan enhet för analys sker via SSH och SFTP med hjälp av tredjepartsprogram som kankräva teknisk kunskap. En unik programvara designades specifikt för Striton tilloperativsystemet Windows 10 i miljön Visual Studio (2019) och tillhandahålleregenskaperna att kunna ansluta till en bestämd Wi-Fi åtkomstpunkt samt automatisktåteransluta till föregående åtkomstpunkt vid avslut, ansluta via SFTP, visa sparade filer frånfjärrkontrollen, utföra filoperationer, synkronisera datum och tid på fjärrkontrollen samtändra inställningar i programvaran som sparas i en lokal inställningsfil. Gränssnittet ärminimalistiskt med syfte att reducera komplexiteten samt tidsåtgången för extrahering avdata från mätsystemet Striton.

Deepfakes are an emerging problem in social media and for celebrities and political profiles, it can be devastating to their reputation if the technology ends up in the wrong hands. Creating deepfakes is becoming increasingly easy. Attempts have been made at detecting whether a face in an image is real or not but training these machine learning models can be a very time-consuming process. This research proposes a solution to training deepfake detection models cooperatively on the edge. This is done in order to evaluate if the training process, among other things, can be made more efficient with this approach.  The feasibility of edge training is evaluated by training machine learning models on several different types of iPhone devices. The models are trained using the YOLOv2 object detection system.  To test if the YOLOv2 object detection system is able to distinguish between real and fake human faces in images, several models are trained on a computer. Each model is trained with either different number of iterations or different subsets of data, since these metrics have been identified as important to the performance of the models. The performance of the models is evaluated by measuring the accuracy in detecting deepfakes.  Additionally, the deepfake detection models trained on a computer are ensembled using the bagging ensemble method. This is done in order to evaluate the feasibility of cooperatively training a deepfake detection model by combining several models.  Results show that the proposed solution is not feasible due to the time the training process takes on each mobile device. Additionally, each trained model is about 200 MB, and the size of the ensemble model grows linearly by each model added to the ensemble. This can cause the ensemble model to grow to several hundred gigabytes in size.

Phoniro, ett företag som fokuserar på välfärdsteknologi har ett internt utvecklat program för att konfigurera olika larmenheter via Bluetooth. Phoniro behöver en mobilapplikation av detta program för att underlätta fältteknikernas arbete. Syftet med detta examensarbete är att beskriva teorin bakom apputveckling, trådlös kommunikation samt utvecklingen av en mobilapplikation som är körbar på operativsystem som IOS och Android med samma funktionalitet som Phoniros interna program. För att utveckla applikationen valdes arbetsmiljön Xamarin.Forms vilket är ett verktyg för att utveckla mobila applikationer till IOS och Android. Resultatet av detta arbete är en applikation där användaren kan söka efter Bluetooth-enheter samt ansluta och kommunicera med anslutna enheter. För att mäta användarvänligheten av applikationen används en heuristisk utvärdering. Den heuristiska utvärderingen påvisar att 40% fel är oupptäckta i systemet efter en undersökning med tre utvärderare utförts.