DESIGN AND EXPLORATION OF NEW MODELS FOR SECURITY AND PRIVACY-SENSITIVE COLLABORATION SYSTEMS

Collaboration has been an area of interest in many domains including education, research, healthcare supply chain, Internet of things, and music etc. It enhances problem solving through expertise sharing, ideas sharing, learning and resource sharing, and improved decision making. To address the limitations in the existing literature, this dissertation presents a design science artifact and a conceptual model for collaborative environment. The first artifact is a blockchain based collaborative information exchange system that utilizes blockchain technology and semi-automated ontology mappings to enable secure and interoperable health information exchange among different health care institutions. The conceptual model proposed in this dissertation explores the factors that influences professionals continued use of video- conferencing applications. The conceptual model investigates the role the perceived risks and benefits play in influencing professionals’ attitude towards VC apps and consequently its active and automatic use

Advanced Threat Intelligence: Interpretation of Anomalous Behavior in Ubiquitous Kernel Processes

Targeted attacks on digital infrastructures are a rising threat against the confidentiality, integrity, and availability of both IT systems and sensitive data. With the emergence of advanced persistent threats (APTs), identifying and understanding such attacks has become an increasingly difficult task. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. This thesis presents a multi-stage system able to detect and classify anomalous behavior within a user session by observing and analyzing ubiquitous kernel processes. Application candidates suitable for monitoring are initially selected through an adapted sentiment mining process using a score based on the log likelihood ratio (LLR). For transparent anomaly detection within a corpus of associated events, the author utilizes star structures, a bipartite representation designed to approximate the edit distance between graphs. Templates describing nominal behavior are generated automatically and are used for the computation of both an anomaly score and a report containing all deviating events. The extracted anomalies are classified using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Ultimately, the newly labeled patterns are mapped to a dedicated APT attacker–defender model that considers objectives, actions, actors, as well as assets, thereby bridging the gap between attack indicators and detailed threat semantics. This enables both risk assessment and decision support for mitigating targeted attacks. Results show that the prototype system is capable of identifying 99.8% of all star structure anomalies as benign or malicious. In multi-class scenarios that seek to associate each anomaly with a distinct attack pattern belonging to a particular APT stage we achieve a solid accuracy of 95.7%. Furthermore, we demonstrate that 88.3% of observed attacks could be identified by analyzing and classifying a single ubiquitous Windows process for a mere 10 seconds, thereby eliminating the necessity to monitor each and every (unknown) application running on a system. With its semantic take on threat detection and classification, the proposed system offers a formal as well as technical solution to an information security challenge of great significance.The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, and the National Foundation for Research, Technology and Development is gratefully acknowledged

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing

The appraisal of Facebook online community: An exposition of mobile commerce in social media reviews

Peer reviewe

Low-cost methodologies and devices applied to measure, model and self-regulate emotions for Human-Computer Interaction

En aquesta tesi s'exploren les diferents metodologies d'anàlisi de l'experiència UX des d'una visió centrada en usuari. Aquestes metodologies clàssiques i fonamentades només permeten extreure dades cognitives, és a dir les dades que l'usuari és capaç de comunicar de manera conscient. L'objectiu de la tesi és proposar un model basat en l'extracció de dades biomètriques per complementar amb dades emotives (i formals) la informació cognitiva abans esmentada. Aquesta tesi no és només teòrica, ja que juntament amb el model proposat (i la seva evolució) es mostren les diferents proves, validacions i investigacions en què s'han aplicat, sovint en conjunt amb grups de recerca d'altres àrees amb èxit.En esta tesis se exploran las diferentes metodologías de análisis de la experiencia UX desde una visión centrada en usuario. Estas metodologías clásicas y fundamentadas solamente permiten extraer datos cognitivos, es decir los datos que el usuario es capaz de comunicar de manera consciente. El objetivo de la tesis es proponer un modelo basado en la extracción de datos biométricos para complementar con datos emotivos (y formales) la información cognitiva antes mencionada. Esta tesis no es solamente teórica, ya que junto con el modelo propuesto (y su evolución) se muestran las diferentes pruebas, validaciones e investigaciones en la que se han aplicado, a menudo en conjunto con grupos de investigación de otras áreas con éxito.In this thesis, the different methodologies for analyzing the UX experience are explored from a user-centered perspective. These classical and well-founded methodologies only allow the extraction of cognitive data, that is, the data that the user is capable of consciously communicating. The objective of this thesis is to propose a methodology that uses the extraction of biometric data to complement the aforementioned cognitive information with emotional (and formal) data. This thesis is not only theoretical, since the proposed model (and its evolution) is complemented with the different tests, validations and investigations in which they have been applied, often in conjunction with research groups from other areas with success

Skilling up for CRM: qualifications for CRM professionals in the Fourth Industrial Revolution

The 4th industrial revolution (4IR) describes a series of innovations in artificial intelligence, ubiquitous internet connectivity, and robotics, along with the subsequent disruption to the means of production. The impact of 4IR on industry reveals a construct called Industry 4.0. Higher education, too, is called to transform to respond to the disruption of 4IR, to meet the needs of industry, and to maximize human flourishing. Education 4.0 describes 4IR’s impact or predicted impact or intended impact on higher education, including prescriptions for HE’s transformation to realize these challenges. Industry 4.0 requires a highly skilled workforce, and a 4IR world raises questions about skills portability, durability, and lifespan. Every vertical within industry will be impacted by 4IR and such impact will manifest in needs for diverse employees possessing distinct competencies. Customer relationship management (CRM) describes the use of information systems to implement a customer-centric strategy and to practice relationship marketing (RM). Salesforce, a market leading CRM vendor, proposes its products alone will generate 9 million new jobs and $1.6 trillion in new revenues for Salesforce customers by 2024. Despite the strong market for CRM skills, a recent paper in a prominent IS journal claims higher education is not preparing students for CRM careers. In order to supply the CRM domain with skilled workers, it is imperative that higher education develop curricula oriented toward the CRM professional. Assessing skills needed for specific industry roles has long been an important task in IS pedagogy, but we did not find a paper in our literature review that explored the Salesforce administrator role. In this paper, we report the background, methodology, and results of a content analysis of Salesforce Administrator job postings retrieved from popular job sites. We further report the results of semi-structured interviews with industry experts, which served to validate, revise, and extend the content analysis framework. Our resulting skills framework serves as a foundation for CRM curriculum development and our resulting analysis incorporates elements of Education 4.0 to provide a roadmap for educating students to be successful with CRM in a 4IR world

Proceedings of EVA London 2024

The Electronic Visualisation and the Arts London 2024 Conference (EVA London 2024) is co-sponsored by the Computer Arts Society (CAS) and BCS, the Chartered Institute for IT, of which the CAS is a Specialist Group. As for 2022, the EVA London 2023 Conference is a physical and online “hybrid” conference. We continue with publishing the proceedings, both online, with open access via ScienceOpen, and also in our traditional printed form, in full colour. The main conference presentations run during 10–13 July 2023, with workshops and other activities, especially for students, on 14 July 2023

Personal Learning Environments for Inquiry-Based Learning

Personal Learning Environments have recently emerged as a novel approach to learning, putting learners in the spotlight and providing them with the tools for building their own learning environments according to their specific learning needs and aspirations. This approach enables learners to take complete control over their learning, thus becoming self-regulated and independent. This paper introduces a new European initiative for supporting and enhancing inquiry-based learning through Personal Learning Environments consisting of personal and social inquiry tools. This approach aims at supporting students in developing their self-regulated learning skills by conducting their scientific inquiries in collaboration with their peers

Gamification as a Service: Conceptualization of a Generic Enterprise Gamification Platform

Gamification is a novel method to improve engagement, motivation, or participation in non-game contexts using game mechanics. To a large extent, gamification is a psychological- and design-oriented discipline, i.e., a lot of effort has to be spent already in the design phase of a gamification project. Subsequently, the design is implemented in information systems such as portals or enterprise resource planning applications. These systems act as mediators to transport a gameful design to its users. However, the efforts for the subsequent development and integration process are often underestimated. In fact, most conceptual gamification designs are never implemented due to the high development costs that arise from building the gamification solution from scratch, imprecise design or technical requirements, and communication conflicts between different stakeholders in the project. This thesis addresses these problems by systematically defining the phases and stakeholders of the overall gamification process. Furthermore, the thesis rigorously defines the conceptual requirements of gamification based on a broad literature review. The identified conceptual requirements are mapped to a domain-specific language, called the Gamification Modeling Language. Moreover, this thesis analyzes 29 existing gamification solutions that aim to decrease the implementation efforts of gamification. However, using the different language elements, it is shown that none of the existing solutions suffices all requirements. Therefore, a generic and reusable platform as runtime environment for gamification is proposed which fulfills all presented functional and non-functional requirements. As another benefit, it is shown how the Gamification Modeling Language can be automatically compiled into code for the gamification runtime environment and, thus, further reduces development efforts. Based on the developed artifacts and five real gamified applications from industry, it is shown that the efforts for the implementation of the gamification can be significantly reduced from several months or weeks to a few days. Since the technology is designed as a reusable service, future projects benefit continuously with regards to time and efforts