Development in the field of software architecture, from the early days in the mid-80’s, has been significant. From purely technical descriptions to decision based architectural knowledge, software architecture has seen fundamental changes to its methodologies and techniques. Architectural knowledge is a resource that is managed and stored by companies, this resource is valuable because it can be reused and analysed to improve future development. Companies today are interested in the reasoning behind the software architecture. This reasoning is mainly formulated through the architectural decisions made during development. For architectural decisions to be easier to analyse they need to be stored in a way that enables use of common analytical tools so that comparisons between decisions are consistent and relevant. Additionally, it is also important to have enough data, which leads us to the problem that, preferably, all the individual architectural knowledge cases must be structured and stored. To do this we present a tool that uses graph generation algorithms to generate architectural knowledge as graphs based on an architectural decision canvas called GRADE. This enables multiple decision cases to be encoded through graphs that can be used to analyse relationships and balances between different architectural knowledge elements represented through nodes and edges within a graph.

No description available.

High profile cyber attacks such as the one on DigiNotar in 2011, where a Certificate Authority (CA) was compromised, has shed light on the vulnerabilities of the internet. In order to make the internet safer in terms of exposing fraudulent certificates, CertificateTransparency (CT) was introduced. The main idea is to append all certificates to a publicly visible log, which anyone can monitor to check for suspicious activity. Although this is a great initiative for needing to rely less on CAs, the logs are still centralized and run by large companies. Therefore, in this thesis, in order to make the logs more available and scalable, we investigate the idea of a distributed client driven CT log via peer-to-peer (P2P) and WebRTC technology that runs in the background of the user’s browser. We show that such a system is indeed implementable, but with limited scalability. We also show that such a system would provide better availability while keeping the integrity of CT by implementing an append only feature, enforced by the Merkle Tree structure.

No description available.

Enterprise Resource Planning systems have been available since the 1990s and come with several business benefits for the users. One of the major advantages is improved decision making through current and accessible information about strategical, tactical and operational levels of the organization. Although several Enterprise Resource Planning system vendors provide several features for contract management, more decision support regarding the total profitability of service contracts is desired by the customers. Estimating the total profitability of service contracts is a challenging task for all service providers and implies a lot of manual data processing by the contract manager. This master’s thesis is conducted in collaboration with IFS World Operations AB and aims to investigate how functionality for budget and forecasting of the profitability of service contracts can be designed to be usable in terms of effectiveness. The implementation was performed iteratively and the resulting prototypes were evaluated and refined throughout the project. The final high-fidelity prototype for budgeting of service contracts was evaluated using the task success rate in conjunction with the System Usability Scale to assess how well the system conformed to the needs of the users. The study revealed that two of the key characteristics of financial follow-up information of service contracts is the support of creating a budget and graphical visualizations of both budgeted and actual values. The final usability evaluation indicated that the developed functionality was usable in terms of effectiveness and has an overall usability clearly above the average.

Denna rapport beskriver ett projektarbete som utfördes av sju studenter från civilingenjörsprogrammen inom datateknik och mjukvaruteknik vid Linköpings universitet.  Projektet utfördes i kursen TDDD96 Kandidatprojekt i mjukvaruutveckling under våren 2018. Syftet med projektet var att utveckla en webbapplikation till Sectra Communications AB som kan användas för att hantera och spåra tillgångar internt hos företaget. Under projektets utvecklingsfas följde projektgruppen en modifierad version av det agila systemutvecklingsramverket Scrum. Projektet resulterade i en fungerande webbapplikation som uppfyller de krav som togs fram tillsammans med Sectra Communications AB. Projektgruppen har utvecklat sina kunskaper inom webbutveckling, agila metoder och att arbeta i grupp. Alla projektmedlemmar har fördjupat sig inom ett varsitt ämne kopplat till projektet, dessa individuella bidrag kan läsas i slutet av rapporten.

We present and discuss algorithms and library software for solving the generalized non-symmetric eigenvalue problem (GNEP) on high performance computing (HPC) platforms with distributed memory. Such problems occur frequently in computational science and engineering, and our contributions make it possible to solve GNEPs fast and accurate in parallel using state-of-the-art HPC systems. A generalized eigenvalue problem corresponds to finding scalars y and vectors x such that Ax = yBx, where A and B are real square matrices. A nonzero x that satisfies the GNEP equation is called an eigenvector of the ordered pair (A,B), and the scalar y is the associated eigenvalue. Our contributions include parallel algorithms for transforming a matrix pair (A,B) to a generalized Schur form (S,T), where S is quasi upper triangular and T is upper triangular. The eigenvalues are revealed from the diagonals of S and T. Moreover, for a specified set of eigenvalues an associated pair of deflating subspaces can be computed, which typically is requested in various applications. In the first stage the matrix pair (A,B) is reduced to a Hessenberg-triangular form (H,T), where H is upper triangular with one nonzero subdiagonal and T is upper triangular, in a finite number of steps. The second stage reduces the matrix pair further to generalized Schur form (S,T) using an iterative QZ-based method. Outgoing from a one-stage method for the reduction from (A,B) to (H,T), a novel parallel algorithm is developed. In brief, a delayed update technique is applied to several partial steps, involving low level operations, before associated accumulated transformations are applied in a blocked fashion which together with a wave-front task scheduler makes the algorithm scale when running in a parallel setting. The potential presence of infinite eigenvalues makes a generalized eigenvalue problem ill-conditioned. Therefore the parallel algorithm for the second stage, reduction to (S,T) form, continuously scan for and robustly deflate infinite eigenvalues. This will reduce the impact so that they do not interfere with other real eigenvalues or are misinterpreted as real eigenvalues. In addition, our parallel iterative QZ-based algorithm makes use of multiple implicit shifts and an aggressive early deflation (AED) technique, which radically speeds up the convergence. The multi-shift strategy is based on independent chains of so called coupled bulges and computational windows which is an important source of making the algorithm scalable. The parallel algorithms have been implemented in state-of-the-art library software. The performance is demonstrated and evaluated using up to 1600 CPU cores for problems with matrices as large as 100000 x 100000. Our library software is described in a User Guide. The software is, optionally, tunable via a set of parameters for various thresholds and buffer sizes etc. These parameters are discussed, and recommended values are specified which should result in reasonable performance on HPC systems similar to the ones we have been running on.

The modern cars have been equipped with advanced technical features to help make driving faster, safer and comfortable. However, to enhance transport security i.e. to avoid unexpected traffic accidents it is necessary to consider a vehicle driver as a part of the environment and need to monitor driver’s health and mental state. Driving behavior-based and physiological parameters-based approaches are the two commonly used approaches to monitor driver’s health and mental state. Previously, physiological parameters-based approaches using sensors are often attached to the human body. Although these sensors attached with body provide excellent signals in lab conditions it can often be troublesome and inconvenient in driving situations.  So, physiological parameters extraction based on video images offers a new paradigm for driver’s health and mental state monitoring. This thesis report presents an intelligent non-contact-based approach to monitor driver’s cognitive load based on physiological parameters and vehicular parameters. Here, camera sensor has been used as a non-contact and pervasive methods for measuring physiological parameters. The contribution of this thesis is in three folds: 1) Implementation of a camera-based method to extract physiological parameters e.g., heart rate (HR), heart rate variability (HRV), inter-bit-interval (IBI), oxygen saturation (SpO2) and respiration rate (RR) considering several challenging conditions e.g. illumination, motion, vibration and movement. 2) Vehicular parameters e.g. lateral speed, steering wheel angle, steering wheel reversal rate, steering wheel torque, yaw rate, lanex, and lateral position extraction from a driving simulator. 3) Investigation of three machine learning algorithms i.e. Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) to classify driver’s cognitive load. Here, according to the results, considering the challenging conditions, the highest correlation coefficient achieved for both HR and SpO2 is 0.96. Again, the Bland Altman plots shows 95% agreement between camera and the reference sensor. For IBI, the quality index (QI) is achieved 97.5% considering 100 ms R-peak error. For cognitive load classification, two separate studies are conducted, study1 with 1-back task and study2 with 2-back task and both time domain and frequency domain features are extracted from the facial videos. Finally, the achieved average accuracy for the classification of cognitive load is 91% for study1 and 83% for study2. In future, the proposed approach should be evaluated in real-road driving environment considering other complex challenging situations such as high temperature, complete dark/bright environment, unusual movements, facial occlusion by hands, sunglasses, scarf, beard etc. / SafeDriver:

This thesis is a pilot study on sound as feedback to motion in active video games. The subject addressed herein has been approached from a multidisciplinary perspective drawing from several fields as audio, film and computer science. The study was set up as a user experience study. Participants where exposed to two videos of identical gameplay, one with feedback sound turned on and a second with feedback sound turned off. Each video was followed by a questionnaire and the study was ended by a semi-structured interview. The main aim was to answer whether users preferred gameplay with feedback sound turned on or off. Results of main question were slightly in favour of sound scenario with feedback sound off even thou not statistically significant. The study also highlights several issues with implemented feedback sound (a secondary goal of the study). Main finding here was problem with perceiving sounds intended function. Even thou results can be somewhat discouraging one should see this study in the light of it being a pilot study. The main value and contribution of this study can be found in lessons learned for future implementations.

Detta dokument beskriver ett kandidatexamensarbete som har genomförts av sju civilingenjörsstudenter från Tekniska högskolan vid Linköpings universitet. Syftet med arbetet var att utveckla en smart inpasseringslösning för företaget Zoezi AB och deras produkt Gymsystem. Systemet skulle dels bestå av en enkortsdator som styr ett lås och kommunicerar med Gymsystems servrar samt en mobilapplikation som kommunicerar med denna enkortsdator. Den första delen av detta dokument beskriver hur utvecklingen av detta system gick till, hur väl de krav som ställts har uppfyllts samt gruppens gemensamma erfarenheter från att utveckla mjukvara i ett större projekt. Rapporten beskriver hur en sådan lösning kan implementeras så att den skapar värde för kunden, vilka erfarenheter som kan vara intressanta för framtida projekt, vilket stöd en systemanatomi kan ge, hur Arduinoplattformen kan användas för hårdvaruprototyper, vilket stöd Extreme Programming kan ge, samt hur fördelningen av hållbarhetsdimensioner i ett sådant projekt kan se ut. Följande slutsatser kan dras utifrån projektet: Det implementerade systemet som beskrivs i rapporten skapar värde för kunden. Agila utvecklingsmetoder rekommenderas varmt inför framtida projekt. En systemanatomi ger bra stöd vid aktivitetsformulering. Arduinoplattformen är en bra plattform för hårdvaruprototyper men innebär vissa prestandabegränsningar. Extreme Programming ger bra stöd för en sådan projektgrupp som beskrivs i rapporten. Slutligen kan en skev fördelning av hållbarhetsdimensioner förväntas om dessa inte behandlas medvetet i projektets tidigare faser. Dokumentet senare del består av ett antal bilagor som innehåller individuella utredningar skrivna av gruppens medlemmar. Dessa bilagor utreder olika ämnen som i någon form har anknytning till antingen arbetet i detta projekt eller till mjukvaruprojekt i allmänhet.