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The problem of ``Dark Patterns" in user interface/user experience (UI/UX) design has proven a difficult issue to tackle. Malicious and explotitative design has expanded to multiple domains in the past 10 years and which has in turn led to multiple taxonomies attempting to describe them. While these taxonomies holds their own merit, and constitute unique contributions to the literature, their usefulness as separate entities is limited. We believe that in order to make meaningful progress in regulating malicious interface design, we must first form a globally harmonized system (GHS) for the classification and labeling of Dark Patterns. By leaning on network analysis tools and methods, this paper synthesizes existing taxonomies and their elements through as a directed graph. In doing so, the interconnectedness of Dark patterns can be more clearly revealed via community (cluster) detection. Ultimately, we hope that this work can serve as the inspiration for the creation of a glyph-based GHS for the classification of Dark Patterns.

In this paper, we propose a certificate sharing system based on blockchain that gives students authority and control over their academic certificates. Our strategy involves developing blockchain-based NFT certifications that can be shared with institutions or employers using blockchain addresses. Students may access the data created by each individual institute in a single platform, filter the view of the relevant courses according to their requirements, and mint their certificate metadata as NFTs. This method provides accountability of access, comprehensive records that are permanently maintained in IPFS, and verifiable provenance for creating, distributing, and accessing certificates. It also makes it possible to share certificates more safely and efficiently. By incorporating trust factors through data provenance, our system provides a countermeasure against issues such as fake and duplicate certificates. It addresses the challenge of the traditional certificate verification processes, which are lengthy manual process. With this system, students can manage and validate their academic credentials from multiple institutions in one location while ensuring authenticity and confidentiality using digital signatures and hashing for data protection against unauthorized access. Overall, our suggested system ensures data safety, accountability, and confidentiality while offering a novel approach to certificate distribution.

This research aims to develop machine learning models for students academic performance and study strategies prediction which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) essential for students learning process were used in building the models. Determining the broad effect of these attributes on students' academic performance and study strategy is the center of our interest. To investigate this, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Personalized gamification explores knowledge about the users to tailor gamification designs to improve one-size-fits-all gamification. The tailoring process should simultaneously consider user and contextual characteristics (e.g., activity to be done and geographic location), which leads to several occasions to tailor. Consequently, tools for automating gamification personalization are needed. The problems that emerge are that which of those characteristics are relevant and how to do such tailoring are open questions, and that the required automating tools are lacking. We tackled these problems in two steps. First, we conducted an exploratory study, collecting participants' opinions on the game elements they consider the most useful for different learning activity types (LAT) via survey. Then, we modeled opinions through conditional decision trees to address the aforementioned tailoring process. Second, as a product from the first step, we implemented a recommender system that suggests personalized gamification designs (which game elements to use), addressing the problem of automating gamification personalization. Our findings i) present empirical evidence that LAT, geographic locations, and other user characteristics affect users' preferences, ii) enable defining gamification designs tailored to user and contextual features simultaneously, and iii) provide technological aid for those interested in designing personalized gamification. The main implications are that demographics, game-related characteristics, geographic location, and LAT to be done, as well as the interaction between different kinds of information (user and contextual characteristics), should be considered in defining gamification designs and that personalizing gamification designs can be improved with aid from our recommender system., Comment: 14 pages, 2 figures, 8 tables. IEEE Transactions on Learning Technologies (2022)

We propose a new free eCommerce platform with blockchains that allows customers to connect to the seller directly, share personal data without losing control and ownership of it and apply it to the domain of shopping cart. Our new platform provides a solution to four important problems: private payment, ensuring privacy and user control, and incentives for sharing. It allows the trade to be open, transparent with immutable transactions that can be used for settling any disputes. The paper presents a case study of applying the framework for a shopping cart as one of the enterprise nodes of MultiChain which provides trading in ethers controlled by smart contracts and also collects user profile data and allows them to receive rewards for sharing their data with other business enterprises. It tracks who shared what, with whom, when, by what means and for what purposes in a verifiable fashion. The user data from the repository is converted into an open data format and shared via stream in the blockchain so that other nodes can efficiently process and use the data. The smart contract verifies and executes the agreed terms of use of the data and transfers digital tokens as a reward to the customer. The smart contract imposes double deposit collateral to ensure that all participants act honestly., Comment: To appear in IEEE International Conference on Blockchain and Cryptocurrency (IEEE ICBC)

Blockchain technology has evolved as a promising means to transform data management models in many domains including healthcare, agricultural research, tourism domains etc. In the research community, a usable blockchain-based system can allow users to create a proof of ownership and provenance of the research work, share research data without losing control and ownership of it, provide incentives for sharing and give users full transparency and control over who access their data, when and for what purpose. The initial adoption of such blockchain-based systems is necessary for continued use of the services, but their user acceptance behavioral model has not been well investigated in the literature. In this paper, we take the Technology Acceptance Model (TAM) as a foundation and extend the external constructs to uncover how the perceived ease of use, perceived usability, quality of the system and perceived enjoyment influence the intention to use the blockchain-based system. We based our study on user evaluation of a prototype of a blockchain-based research data sharing framework using a TAM validated questionnaire. Our results show that, overall, all the individual constructs of the behavior model significantly influence the intention to use the system while their collective effect is found to be insignificant. The quality of the system and the perceived enjoyment have stronger influence on the perceived usefulness. However, the effect of perceived ease of use on the perceived usefulness is not supported. Finally, we discuss the implications of our findings., Comment: To appear in IEEE TPS 2019

Currently, there is no universal method to track who shared what, with whom, when and for what purposes in a verifiable way to create an individual incentive for data owners. A platform that allows data owners to control, delete, and get rewards from sharing their data would be an important enabler of user data-sharing. We propose a usable blockchain- and smart contracts-based framework that allows users to store research data locally and share without losing control and ownership of it. We have created smart contracts for building automatic verification of the conditions for data access that also naturally supports building up a verifiable record of the provenance, incentives for users to share their data and accountability of access. The paper presents a review of the existing work of research data sharing, the proposed blockchain-based framework and an evaluation of the framework by measuring the transaction cost for smart contracts deployment. The results show that nodes responded quickly in all tested cases with a befitting transaction cost., Comment: 7 pages, 4 figure, 1 table, IEEE IEMCON 2019 conference

The tremendous technological advancement in the last few decades has brought many enterprises to collaborate in a better way while making intelligent decisions. The use of Information Technology tools in obtaining data of people's everyday life from various autonomous data sources allowing unrestricted access to user data has emerged as an important practical issue and has given rise to legal implications. Various innovative models for data sharing and management have privacy and centrality issues. To alleviate these limitations, we have incorporated blockchain in user modeling. In this paper, we constructed a decentralized data sharing architecture with MultiChain blockchain in the travel domain, which is also applicable to other similar domains including education, health, and sports. Businesses that operate in the tourism industries including travel and tour agencies, hotels and resorts, shopping malls are connected to the MultiChain and they share their user profile data via stream in the MultiChain. The paper presents the hotel booking service for an imaginary hotel as one of the enterprise nodes, which collects user profile data with proper validation and will allow users to decide which of their data to be shared thus ensuring user control over their data and the preservation of privacy. The data from the repository is converted into an open data format while sharing via stream in the blockchain so that other enterprise nodes, after receiving the data, can easily convert them and store into their own repositories. The paper presents an evaluation of the performance of the model by measuring the latency and memory consumption with three test scenarios that mostly affect the user experience. The node responded quickly in all of these cases., Comment: 10 pages, 11 figures, 4 tables, Future Technologies Conference (FTC) 2017, "for associated proceeding paper, see https://saiconference.com/Downloads/FTC2017/Proceedings/3_Paper_127-User-Controlled_Privacy-Preserving_User_Profile_Data_Sharing.pdf