A Multilayer Secured Messaging Protocol for REST-based Services

The lack of descriptive language and security guidelines poses a big challenge to implementing security in Representational State Transfer (REST) architecture. There is over reliance on Secure Socket Layer/Transport Layer Security (SSL/TLS), which in recent times has proven to be fallible. Some recent attacks against SSL/TLS include: POODLE, BREACH, CRIME, BEAST, FREAK etc. A secure messaging protocol is implemented in this work. The protocol is further compiled into a reusable library which can be called by other REST services. Using Feature Driven Development (FDD) software methodology, a two layer security protocol was developed. The first layer is a well hardened SSL/TLS configuration. The second layer is a well-designed end-to-end protocol that handles authentication, authorization, encryption and message integrity as well as timing and replay attack prevention. The end-to-end protocol uses HMAC-512 and a hybrid encryption system using the AES and RSA algorithms. The protocol was then compiled to a reusable library using C# language. Two different tests were carried out on this protocol: Penetration test and SSL/TLS configuration test. The Penetration Test was carried out using the Open Web Application Security Project Zed Attack Proxy (OWASP ZAP) application and Fiddler Web Debugger. The SSL/TLS test sought to test the SSL/TLS layer of the protocol for known vulnerabilities using a popular SSL/TLS test tool known as SSL Lab. The raw and scaled scores obtained from SSL Lab were 95% and 93% respectively. The results of Implementation test show that the protocol is implementable. The protocol is also resistant to such attacks as: Unauthorized, Timing and Replay attacks as shown by the result of the penetration test. The grade obtained from the SSL/TLS test is “A+”. The result also shows that the implementation is not vulnerable to currently known SSL attacks. The library can be reused by .NET applications and the implementation steps can also be followed by other REST services developers using other platforms

Encryption’s Importance to Economic and Infrastructure Security

Det övergripande syftet med den här avhandlingen var att utreda om network coopetition, samarbete mellan konkurrerande aktörer, kan öka värdeskapandet inom hälso- och sjukvården. Inom hälso- och sjukvården är network coopetition ett ämne som fått liten uppmärksamhet i tidigare studier. För att besvara syftet utvecklades en modell för network coopetition inom hälso- och sjukvården. Modellen applicerades sedan på en del av vårdkedjan för patienter i behov av neurokirurgisk vård. Resultaten från avhandlingen visar att: (1) Förutsättningarna för network coopetition i vårdkedjan för patienter i behov av neurokirurgisk vård är uppfyllda. (2) Det finns exempel på horisontell network coopetition i den studerade vårdkedjan. (3) Det existerar en diskrepans mellan hur aktörerna  ser  på  sitt  eget  och  de  andra  aktörernas  värdeskapande. (4)  Värdeskapandet bör utvärderas som ett gemensamt system där hänsyn tas till alla aktörer och utvärderas på process- nivå där hänsyn tas till alla intressenter. Dessa resultat leder fram till den övergripande slutsatsen är att network coopetition bör kunna öka värdeskapandet för högspecialiserade vårdkedjor med en stor andel inomlänspatienter.The overall purpose of this thesis was to investigate whether network coopetition, cooperation between competitive actors, can increase the value creation within the health care system. Within health care, network coopetition is a subject granted little attention in previous research. To fulfil the purpose a model for network coopetition within the health care system was developed. The model was the applied to one part of the chain of care for patients in need of neurosurgery. The results from this thesis show: (1) The conditions for network coopetition in the chain of care for patients in need of neurosurgery are fulfilled. (2) Examples of horizontal network coopetition have been found in the studied chain of care. (3) There is an existing discrepancy between how each actor recognizes its own and the other actors’ value creation. (4) The value creation ought to be evaluated as a common system where all actors are taken into account and at a process level where all stakeholders are considered. These results supports the final conclusion that network coopetition ought to be able to increase the value creation for highly specialized chain of cares with a large share of within-county patients

e-Social Science and Evidence-Based Policy Assessment : Challenges and Solutions

Peer reviewedPreprin

A Cloud Computing-based Dashboard for the Visualization of Motivational Interviewing Metrics

Motivational Interviewing (MI) is an evidence-based brief interventional technique that has been demonstrated to be effective in triggering behavior change in patients. To facilitate behavior change, healthcare practitioners adopt a nonconfrontational, empathetic dialogic style, a core component of MI. Despite its advantages, MI has been severely underutilized mainly due to the cognitive overload on the part of the MI dialogue evaluator, who has to assess MI dialogue in real-time and calculate MI characteristic metrics (number of open-ended questions, close-ended questions, reflection, and scale-based sentences) for immediate post-session evaluation both in MI training and clinical settings. To automate dialogue assessment and produce instantaneous feedback several technology-assisted MI (TAMI) tools like ReadMI based on Natural Language Processing (NLP) have been developed on mobile computing platforms like Android. These tools, however, are ill-equipped to support remote work and education settings, a consequence of the COVID-19 pandemic. Furthermore, these tools lack data visualization features to intuitively understand and track MI progress. In this thesis, to address the aforementioned shortcomings in the current landscape of TAMI, a web-based MI data visualization dashboard tool ReadMI.org has been designed and developed. The proposed dashboard leverages the highperformance computing capacity of cloud-based Amazon Web Service (AWS) to implement the NLP-based dialogue assessment functionality of ReadMI and a vibrant data visualization capability to intuitively understand and track MI progress. Additionally, through a simple Uniform Resource Locator (URL) address, ReadMI.org allows MI practitioners and trainers to access the proposed dashboard anywhere and anytime. Therefore, by leveraging the high-performance computing and distribution capability of cloud computing services, ReadMI.org has the potential to reach the growing population of MI practitioners and thereby provide a pathway for largescale MI adoption

Gendered Wilderness: Gendered Language in Wilderness Discourse

A theory of social nature has proliferated and is becoming widely accepted among social researchers, especially within critical geography. This states that our encounters with the non- human world are always mediated. Whether we engage the outdoors using the park system, television, outdoor outfitters, or political organizations, the rhetoric of race, gender, economics, and politics are always at work on how we interact with landscapes. Three goals for this research include: 1) To outline the discursive constructions of wilderness and gender in connection with the social, and political work they do modern society 2) To outline the lived gender experience among wilderness advocates, highlighting moments when this experience resonates with the dominant discourse as well as moments of dissonance. 3) To use the subsequent categories of experience to arrive at a theories of dominant and subversive wilderness discourse

Electronic document authenticity verification of diploma and transcript using smart contract on Ethereum blockchain

Ethereum is one of the oldest examples of blockchain technology provides a system that converts centralized storage to distributed and records transactions by way of decentralized and not by a centralized system and can be verified by each node, therefore it is suitable for storing fingerprints from official diploma documents and transcripts that are published. Smart contract is needed for making contract transactions to Ethereum with programming code, so contracts such as diplomas and transcripts uploaded on the Ethereum blockchain can distribute and produce diploma validation and the authenticity of transcripts with transaction hash, consensus, and comply with ERC-721 token standardization. The results showed that a sample of 5 electronic documents in pdf format with a transaction speed of 1 second on each file that were published and secured with Ethereum blockchain technology can be easily verified for authenticity, the system proposed and developed by us takes in consideration invalid and failure cases by giving the necessary feedback to the user

Machine Learning Models for Educational Platforms

Scaling up education online and onlife is presenting numerous key challenges, such as hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely. However, thanks to the wider availability of learning-related data and increasingly higher performance computing, Artificial Intelligence has the potential to turn such challenges into an unparalleled opportunity. One of its sub-fields, namely Machine Learning, is enabling machines to receive data and learn for themselves, without being programmed with rules. Bringing this intelligent support to education at large scale has a number of advantages, such as avoiding manual error-prone tasks and reducing the chance that learners do any misconduct. Planning, collecting, developing, and predicting become essential steps to make it concrete into real-world education. This thesis deals with the design, implementation, and evaluation of Machine Learning models in the context of online educational platforms deployed at large scale. Constructing and assessing the performance of intelligent models is a crucial step towards increasing reliability and convenience of such an educational medium. The contributions result in large data sets and high-performing models that capitalize on Natural Language Processing, Human Behavior Mining, and Machine Perception. The model decisions aim to support stakeholders over the instructional pipeline, specifically on content categorization, content recommendation, learners’ identity verification, and learners’ sentiment analysis. Past research in this field often relied on statistical processes hardly applicable at large scale. Through our studies, we explore opportunities and challenges introduced by Machine Learning for the above goals, a relevant and timely topic in literature. Supported by extensive experiments, our work reveals a clear opportunity in combining human and machine sensing for researchers interested in online education. Our findings illustrate the feasibility of designing and assessing Machine Learning models for categorization, recommendation, authentication, and sentiment prediction in this research area. Our results provide guidelines on model motivation, data collection, model design, and analysis techniques concerning the above applicative scenarios. Researchers can use our findings to improve data collection on educational platforms, to reduce bias in data and models, to increase model effectiveness, and to increase the reliability of their models, among others. We expect that this thesis can support the adoption of Machine Learning models in educational platforms even more, strengthening the role of data as a precious asset. The thesis outputs are publicly available at https://www.mirkomarras.com