User Full Control Research Articles

Self-Sovereign Identity-Based E-Portfolio Ecosystem

In Taiwan, traditional student assessments, covering academic and extracurricular achievements, have shifted from paper to electronic portfolios (e-portfolios). However, limited trust among institutions restricts students from freely sharing and using their educational data. This paper introduces a self-sovereign identity-based infrastructure aimed at enhancing personal data security within the e-portfolio ecosystem. The proposed system includes two core components: (1) a decentralized identity chain, aligning user identities across platforms and granting users full self-sovereign control; and (2) an e-portfolio application chain to manage user interactions and access permissions within the ecosystem. A trusted educational authority also audits data sources, ensuring data integrity and reliability. This infrastructure empowers users to control who can access their data, safeguarding their security, with the identity chain preventing unauthorized access and the application chain recording authorization statuses to restrict data visibility to approved parties only.

Development and evaluation of a novel user-operated slider-based audiometry method.

To evaluate a novel user-operated audiometry method allowing users full control in determining their pure-tone hearing thresholds. Comparative study. Participants were recruited from a hearing clinic after undergoing manual audiometry (six frequencies). They then performed test-retests of a new test (slider audiometry, eight frequencies) and completed the System Usability Scale questionnaire. 37 adult participants, including 30 hearing-impaired and seven normal-hearing individuals. Mean (SD) threshold differences for octave frequencies between 250 to 2000 Hz between manual and slider audiometry ranged from -7.8 (6.6) to -5.7 (6.5) dB and were significant. For 4000 and 8000 Hz mean differences were -0.3 (8.4) and 0.0 (9.7) dB and insignificant. Standard deviations ranged from 6.5 to 9.7 dB across six tested frequencies. Slider test-retest mean threshold differences ranged from -1.4 (4.7) to 0.3 (6.0) dB across eight tested frequencies, with standard deviations ranging from 4.1 to 8.5 dB. The average usability score for the slider audiometry was 88.3. When compared to manual audiometry, the slider audiometry demonstrated validity at 4000 and 8000 Hz but found significantly lower thresholds for octave frequencies between 250 to 2000 Hz. Test-retests of the new method revealed small mean differences and acceptable SDs.

Extending the code in the open-source saemix package to fit joint models of longitudinal and time-to-event data

Background and ObjectiveJoint modeling of longitudinal and time-to-event data has gained attention over recent years with extensive developments including nonlinear models for longitudinal outcomes and flexible time-to-event models for survival outcomes, possibly involving competing risks. However, in popular software such as R, the function used to describe the biomarker dynamic is mainly linear in the parameters, and the survival submodel relies on pre-implemented functions (exponential, Weibull, ...). The objective of this work is to extend the code from the saemix package (version 3.1 on CRAN) to fit parametric joint models where longitudinal submodels are not necessary linear in their parameters, with full user control over the model function. MethodsWe used the saemix package, designed to fit nonlinear mixed-effects models (NLMEM) through the Stochastic Approximation Expectation Maximization (SAEM) algorithm, and extended the main functions to joint model estimation. To compute standard errors (SE) of parameter estimates, we implemented a recently developed stochastic algorithm. A simulation study was proposed to assess (i) the performances of parameter estimation, (ii) the SE computation and (iii) the type I error when testing independence between the two submodels. Four joint models were considered in the simulation study, combining a linear or nonlinear mixed-effects model for the longitudinal submodel, with a single terminal event or a competing risk model. ResultsFor all simulation scenarios, parameters were precisely and accurately estimated with low bias and uncertainty. For complex joint models (with NLMEM), increasing the number of chains of the algorithm was necessary to reduce bias, but earlier censoring in the competing risk scenario still challenged the estimation. The empirical SE of parameters obtained over all simulations were very close to those computed with the stochastic algorithm. For more complex joint models (involving NLMEM), some estimates of random effects variances had higher uncertainty and their SE were moderately under-estimated. Finally, type I error was controlled for each joint model. Conclusionssaemix is a flexible open-source package and we adapted it to fit complex parametric joint models that may not be estimated using standard tools. Code and examples to help users get started are freely available on Github.

Analysis Of The 2nd Principle Of Pancasila On The Instagram Privacy Disclaimer

The article discusses the importance of user privacy on Instagram, a popular social media platform. In the digital era, millions of users share aspects of their personal lives, creating risks of data misuse and security threats. The right to privacy is recognized as a human right, including in the Indonesian Privacy Rights Law. Privacy Controls: Instagram gives users full control, allowing personal account settings. Data Policy: Instagram has a policy that explains the collection, use, and sharing of data, reflecting civilized principles. Data Security: Instagram claims to implement security measures and collaborate with third parties. Privacy Tools and Features: Instagram is constantly developing new privacy tools and features. In the context of Instagram user privacy, the principle of the 2nd Principle, "Just and Civilized Humanity," reflects fair treatment and respect for human rights. The rights to privacy, freedom of expression, data security, and control over personal information are the emphasis in implementing the 2nd Principle. Assessing the use of Instagram's privacy policy by the 2nd Principle and encouraging users to be wise in sharing information. A mixed method approach with descriptive qualitative methods and literature studies is used to understand the impact of Instagram user privacy. Privacy is a human right, and Instagram is expected to implement the principles of the 2nd Principle with policies that support the control, security, and fair treatment of users' data.

Design, control and evaluation of a treadmill-based Pelvic Exoskeleton (PeXo) with self-paced walking mode

Most gait rehabilitation exoskeletons focus only on assisting lower limb motions. However, the pelvis plays an essential role in overground ambulation. This calls for the need of gait training devices that allow full control and assistance of user’s pelvis to improve gait rehabilitation therapies of stroke survivors. This paper presents a new pelvic assistance treadmill-based exoskeleton (PeXo) for gait rehabilitation. The system includes a total of 5 actively controlled Degrees of Freedom (DOFs), plus a passive one, in order to provide all DOFs of the human pelvis. The paper describes the mechatronic design and haptic control of PeXo, and introduces a newly developed self-paced walking mode where the user can control walking speed at his/her will. The system was evaluated by means of a pilot study with a stroke survivor. Results showed PeXo can be used safely and reliably during walking activities, allowing for the natural motion of the pelvis with reduced interaction forces. However, the use of the platform reduced user’s Range of Motion at the pelvis and lower limb joints, whereas lower limb’s muscle activity increased to compensate the disturbances introduced by the platform. Nevertheless, the user reported a positive feedback when using the system, suggesting potential and promising advantages of PeXo that should be explored in a larger study in the future.

Examining the Use of Autonomous Systems for Home Health Support Using a Smart Mirror.

The home is becoming a key location for healthcare delivery, including the use of technology driven by autonomous systems (AS) to monitor and support healthcare plans. Using the example of a smart mirror, this paper describes the outcomes of focus groups with people with multiple sclerosis (MS; n = 6) and people who have had a stroke (n = 15) to understand their attitudes towards the use of AS for healthcare in the home. Qualitative data were analysed using a thematic analysis. The results indicate that the use of such technology depends on the level of adaptability and responsiveness to users' specific circumstances, including their relationships with the healthcare system. A smart mirror would need to support manual entry, responsive goal setting, the effective aggregation of data sources and integration with other technology, have a range of input methods, be supportive rather than prescriptive in messaging, and give the user full control of their data. The barriers to its adoption include a perceived lack of portability and practicality, a lack of accessibility and inclusivity, a sense of redundancy, feeling overwhelmed by multiple technological devices, and a lack of trust in data sharing. These results inform the development and deployment of future health technologies based on the lived experiences of people with health conditions who require ongoing care.

How to decentralize the internet: A focus on data consolidation and user privacy

Over the years, the Internet has become a field in which a small number of large Internet companies dominate most of the Internet services. As users get used to using their services, the users’ generated content and the data about their online behaviors are concentrated in such companies. This phenomenon, called “data consolidation”, has become a serious problem, which makes the Internet society seek to decentralize the current Internet. The decentralized Internet aims to (i) prevent the concentration of user data in a few giant companies like Google and Facebook, and (ii) give users full ownership and control of their data. Various technical solutions that address the data consolidation problem have been proposed; however, those solutions focus on somewhat different scopes of the problem often from their limited viewpoints. The main contributions in this paper are the following. First, we survey the solutions relevant to Internet decentralization based on the following criteria: data consolidation, data ownership, and the privacy of user data. Second, we suggest a holistic reference framework from a functional viewpoint, while the prior proposals in the literature handle a limited set of requirements. Last, we seek to identify remaining research issues, considering additional requirements that have not been addressed in the existing solutions.

Self-Sovereign Identity for Consented and Content-Based Access to Medical Records Using Blockchain

Electronic health records (EHRs) and medical data are classified as personal data in every privacy law, meaning that any related service that includes processing such data must come with full security, confidentiality, privacy, and accountability. Solutions for health data management, as in storing it, sharing and processing it, are emerging quickly and were significantly boosted by the COVID-19 pandemic that created a need to move things online. EHRs make a crucial part of digital identity data, and the same digital identity trends—as in self-sovereign identity powered by decentralized ledger technologies like blockchain, are being researched or implemented in contexts managing digital interactions between health facilities, patients, and health professionals. In this paper, we propose a blockchain-based solution enabling secure exchange of EHRs between different parties powered by a self-sovereign identity (SSI) wallet and decentralized identifiers. We also make use of a consortium IPFS network for off-chain storage and attribute-based encryption (ABE) to ensure data confidentiality and integrity. Through our solution, we grant users full control over their medical data and enable them to securely share it in total confidentiality over secure communication channels between user wallets using encryption. We also use DIDs for better user privacy and limit any possible correlations or identification by using pairwise DIDs. Overall, combining this set of technologies guarantees secure exchange of EHRs, secure storage, and management along with by-design features inherited from the technological stack.

KIF-Key Interactions Finder: A program to identify the key molecular interactions that regulate protein conformational changes.

Simulation datasets of proteins (e.g., those generated by molecular dynamics simulations) are filled with information about how a non-covalent interaction network within a protein regulates the conformation and, thus, function of the said protein. Most proteins contain thousands of non-covalent interactions, with most of these being largely irrelevant to any single conformational change. The ability to automatically process any protein simulation dataset to identify non-covalent interactions that are strongly associated with a single, defined conformational change would be a highly valuable tool for the community. Furthermore, the insights generated from this tool could be applied to basic research, in order to improve understanding of a mechanism of action, or for protein engineering, to identify candidate mutations to improve/alter the functionality of any given protein. The open-source Python package Key Interactions Finder (KIF) enables users to identify those non-covalent interactions that are strongly associated with any conformational change of interest for any protein simulated. KIF gives the user full control to define the conformational change of interest as either a continuous variable or categorical variable, and methods from statistics or machine learning can be applied to identify and rank the interactions and residues distributed throughout the protein, which are relevant to the conformational change. Finally, KIF has been applied to three diverse model systems (protein tyrosine phosphatase 1B, the PDZ3 domain, and the KE07 series of Kemp eliminases) in order to illustrate its power to identify key features that regulate functionally important conformational dynamics.

NegoSim: A Modular and Extendable Automated Negotiation Simulation Platform Considering EUBOA

In recent years, the research community has become increasingly interested in automated negotiation. It has been used in real-world systems such as autonomous vehicle transportation systems, smart grids, and e-commerce. Considering the broad range of applications, automated negotiation is of great use and interest to engineers, developers, and industry-focused researchers. Therefore, some researchers have developed platforms for automated negotiation. However, these systems do not provide complete control over the development of various protocols or analytic tools. This paper introduces NegoSim, a new negotiation simulator that introduces a new negotiation framework called EUBOA, though it also works on the well-known BOA framework. NegoSim is completely modular, with different APIs allowing users full control over developing and modifying automated negotiation agents, different assessments, protocols, and utility spaces, as well as the ability to create or modify different GUIs. NegoSim also includes predefined agents, protocols, utility space, and analytic tools, making it an ideal platform for researchers.

"fhircrackr": An R Package Unlocking Fast Healthcare Interoperability Resources for Statistical Analysis.

The growing interest in the secondary use of electronic health record (EHR) data has increased the number of new data integration and data sharing infrastructures. The present work has been developed in the context of the German Medical Informatics Initiative, where 29 university hospitals agreed to the usage of the Health Level Seven Fast Healthcare Interoperability Resources (FHIR) standard for their newly established data integration centers. This standard is optimized to describe and exchange medical data but less suitable for standard statistical analysis which mostly requires tabular data formats. The objective of this work is to establish a tool that makes FHIR data accessible for standard statistical analysis by providing means to retrieve and transform data from a FHIR server. The tool should be implemented in a programming environment known to most data analysts and offer functions with variable degrees of flexibility and automation catering to users with different levels of FHIR expertise. We propose the fhircrackr framework, which allows downloading and flattening FHIR resources for data analysis. The framework supports different download and authentication protocols and gives the user full control over the data that is extracted from the FHIR resources and transformed into tables. We implemented it using the programming language R [1] and published it under the GPL-3 open source license. The framework was successfully applied to both publicly available test data and real-world data from several ongoing studies. While the processing of larger real-world data sets puts a considerable burden on computation time and memory consumption, those challenges can be attenuated with a number of suitable measures like parallelization and temporary storage mechanisms. The fhircrackr R package provides an open source solution within an environment that is familiar to most data scientists and helps overcome the practical challenges that still hamper the usage of EHR data for research.

Decentralized Identity Authentication with Auditability and Privacy

Decentralized identity (DID) systems aim to give users full control over their identities by using completely decentralized technologies, such as blockchain or distributed ledgers, as identity providers. However, when user credentials are compromised, it is impossible in existing DID systems for the users to detect credential misuse. In this paper, we propose new DID authentication protocols with two properties: auditability and privacy. The former enables the detection of malicious authentication events, while the latter prevents an adversary from linking an authentication event to the corresponding user and service provider. We present two protocols that achieve auditability with varying privacy and performance guarantees. The first protocol has high performance, but it reveals information about the user. The second protocol achieves full privacy, but it incurs a higher performance overhead. We present a formal security analysis of our privacy-preserving protocols by using the Tamarin prover. We implemented them and evaluated their performance with a permissioned blockchain deployed over the Amazon AWS and a local cloud infrastructure. The results demonstrate that the first protocol is able to support realistic authentication workloads, while the second is nearly practical.

BioShare: An Open Framework for Trusted Biometric Authentication under User Control

Generally, biometric authentication is conducted either by mobile terminals in local-processing mode or by public servers in centralized-processing mode. In the former mode, each user has full control of his/her biometric data, but the authentication service is restricted to local mobile apps. In the latter mode, the authentication service can be opened up to network applications, but the owners have no control of their private data. It has become a difficult problem for biometric applications to provide open and trusted authentication services under user control. Existing approaches address these concerns in ad-hoc ways. In this work, we propose BioShare, a framework that provides trusted biometric authentication services to network applications while giving users full control of their biometric data. Our framework is designed around three key principles: each user has full control of his/her biometric data; biometric data is stored and processed in trusted environments to prevent privacy leaks; and the open biometric-authentication service is efficiently provided to network applications. We describe our current design and sample implementation, and illustrate how it provides an open face-recognition service with standard interfaces, combines terminal trusted environments with server enclaves, and enables each user to control his/her biometric data efficiently. Finally, we analyze the security of the framework and measure the performance of the implementation.

Employing Discrete Solid Phases to Represent C-S-H Solid Solutions in the Cemdata07 Thermodynamic Database to Model Cement Hydration Using the PHREEQC Geochemical Software

This paper presents a cement hydration model over time using the cemdata07 thermodynamic database and a series of derived discrete solid phases (DSPs) to represent calcium silicate hydrate (C-S-H) as a binary solid solution with two end-members. C-S-H in cement is amorphous and poorly crystalline with a range of molar Ca/Si ratios from 0.6 to 1.7. It displays strongly incongruent dissolution behaviour, where the release of calcium into solution is several orders of magnitude greater than silicon. It is, therefore, important that any cement hydration model provides a credible account of this behaviour. C-S-H has been described in the cemdata07 thermodynamic database as a number of solid solutions using different end-members with differing levels of complexity. While solid solutions can be included in most modern geochemical software programs, they often lead to a significant increase in computation time. This paper presents how an incongruent solid solution between two C-S-H end-members may be represented as a number of DSPs to model cement hydration over time using the PHREEQC geochemical software. By using DSPs rather than modelling C-S-H as a nonideal solid solution, this gives the user full control of the input for the model, reducing the computational demand and analysis time with no loss in accuracy in predicting stable-phase assemblages and their associated pore chemistry over time.

Arbitrary position 3D tomography for practical application in combustion diagnostics

This work aims to make three-dimensional (3D) tomographic techniques more flexible and accessible to in-situ measurements in practical apparatus by allowing arbitrary camera placements that benefit applications with more restrictive optical access. A highly customizable, in-house developed tomographic method is presented, applying smoothness priors through Laplacian matrices and hull constraints based on 3D space carving. The goal of this paper is to showcase a reconstruction method with full user control that can be adopted to various 3D field reconstructions. Simulations and experimental measurements of unsteady premixed CH4/air and ethanol (C2H5OH) diffusion pool flames were evaluated, comparing arbitrarily placed cameras around the probed domain to the more commonly used in-plane-half-circle camera arrangement. Reconstructions reproduced expected topological field features for both flame types. Results showed slight decrease in reconstruction quality for arbitrarily placed cameras compared to in-plane-half-circle arrangement. However, at lower numbers of camera views (N q ⩽ 6) arbitrary placement showed better results. The introduced methodology will be useful for optically limited setups in terms of handling a priori information, camera placement and 3D field evaluation.

User-empowered Privacy-preserving Authentication Protocol for Electric Vehicle Charging Based on Decentralized Identity and Verifiable Credential

The use of Electric Vehicles (EVs) has been gaining traction in recent years due to various reasons. While charging their EVs, users expose their identity and personal details, and an adversary being able to identify and track where users charge their EVs is a potential privacy threat. In this article, we propose a user-empowered privacy-preserving authentication protocol for EV charging based on Decentralized Identifier (DID) and Verifiable Credential (VC) to provide Zero-Knowledge Proof (ZKP)-security. The proposed method gives users full control over their identities and allows them to remain anonymous while charging from any station. Giving control over their identities empowers users. At the same time, by making use of the concept of VC, other parties can verify that a user is legitimate and authenticate the user before providing charging services. Hence, the proposed protocol makes the charging service available in a secure way, while empowering users and preserving their privacy.

Reimagining Prosthetic Control: A Novel Body-Powered Prosthetic System for Simultaneous Control and Actuation

Globally, the most popular upper-limb prostheses are powered by the human body. For body-powered (BP) upper-limb prostheses, control is provided by changing the tension of (Bowden) cables to open or close the terminal device. This technology has been around for centuries, and very few BP alternatives have been presented since. This paper introduces a new BP paradigm that can overcome certain limitations of the current cabled systems, such as a restricted operation space and user discomfort caused by the harness to which the cables are attached. A new breathing-powered system is introduced to give the user full control of the hand motion anywhere in space. Users can regulate their breathing, and this controllable airflow is then used to power a small Tesla turbine that can accurately control the prosthetic finger movements. The breathing-powered device provides a novel prosthetic option that can be used without limiting any of the user’s body movements. Here we prove that it is feasible to produce a functional breathing-powered prosthetic hand and show the models behind it along with a preliminary demonstration. This work creates a step-change in the potential BP options available to patients in the future.

Calibration by differentiation - Self-supervised calibration for X-ray microscopy using a differentiable cone-beam reconstruction operator.

High-resolution X-ray microscopy (XRM) is gaining interest for biological investigations of extremely small-scale structures. XRM imaging of bones in living mice could provide new insights into the emergence and treatment of osteoporosis by observing osteocyte lacunae, which are holes in the bone of few micrometres in size. Imaging living animals at that resolution, however, is extremely challenging and requires very sophisticated data processing converting the raw XRM detector output into reconstructed images. This paper presents an open-source, differentiable reconstruction pipeline for XRM data which analytically computes the final image from the raw measurements. In contrast to most proprietary reconstruction software, it offers the user full control over each processing step and, additionally, makes the entire pipeline deep learning compatible by ensuring differentiability. This allows fitting trainable modules both before and after the actual reconstruction step in a purely data-driven way using the gradient-based optimizers of common deep learning frameworks. The value of such differentiability is demonstrated by calibrating the parameters of a simple cupping correction module operating on the raw projection images using only a self-supervisory quality metric based on the reconstructed volume and no further calibration measurements. The retrospective calibration directly improves image quality as it avoids cupping artefacts and decreases the difference in grey values between outer and inner bone by 68-94%. Furthermore, it makes the reconstruction process entirely independent of the XRM manufacturer and paves the way to explore modern deep learning reconstruction methods for arbitrary XRM and, potentially, other flat-panel computed tomography systems. This exemplifies how differentiable reconstruction can be leveraged in the context of XRM and, hence, is an important step towards the goal of reducing the resolution limit of in vivo bone imaging to the single micrometredomain.

SecureVision: An Open-Source User-Customizable Image Encryption Program

Data security has become indispensable, with a view to keep sensitive information confidential. One important method is through image encryption, upon which features in an image would no longer be visible. The original image with its features could only be restored upon decryption using a set of keys. There are prestigious works in the literature regarding image encryption. However, there is a lack of easy-to-use, GUI-based, user-customizable computer programs for image encryption. In the present work, we developed a GUI-based image encryption and decryption program with server file transfer support, namely, SecureVision. A custom-made random number generator using the equation of an ellipse was developed to randomly shuffle the pixel positions. SecureVision was found to be robust, user-friendly and fast in both encryption and decryption. The program was highly sensitive to the supplied keys, which prevented brute-force attacks. SecureVision provided full user control, where users could modify the program modules to match their desired applications, which was particularly desirable for pedagogical purposes in that interested parties had the freedom to explore the concept of image encryption and decryption. SecureVision is distributed under a GPLv3 license, which would allow everyone to use, modify and distribute the program without any restriction.

SANS: Self-Sovereign Authentication for Network Slices

5G communications proposed significant improvements over 4G in terms of efficiency and security. Among these novelties, the 5G network slicing seems to have a prominent role: deploy multiple virtual network slices, each providing a different service with different needs and features. Like this, a Slice Operator (SO) ruling a specific slice may want to offer a service for users meeting some requirements. It is of paramount importance to provide a robust authentication protocol, able to ensure that users meet the requirements, providing at the same time a privacy-by-design architecture. This makes even more sense having a growing density of Internet of Things (IoT) devices exchanging private information over the network. In this paper, we improve the 5G network slicing authentication using a Self-Sovereign Identity (SSI) scheme: granting users full control over their data. We introduce an approach to allow a user to prove his right to access a specific service without leaking any information about him. Such an approach is SANS, a protocol that provides nonlinkable protection for any issued information, preventing an SO or an eavesdropper from tracking users’ activity and relating it to their real identities. Furthermore, our protocol is scalable and can be taken as a framework for improving related technologies in similar scenarios, like authentication in the 5G Radio Access Network (RAN) or other wireless networks and services. Such features can be achieved using cryptographic primitives called Zero-Knowledge Proofs (ZKPs). Upon implementing our solution using a state-of-the-art ZKP library and performing several experiments, we provide benchmarks demonstrating that our approach is affordable in speed and memory consumption.