Access Decisions Research Articles

Heuristic evaluations for accessibility decisions: a systematic literature review

There are more and more people who are using technology on a daily basis to communicate and access information, including the elderly and people with disabilities. Assistive technologies, also called technical aids, are accessibility and inclusion resources that provide a greater degree of autonomy and assistance for people who have some degree of difficulty in using resources in their contact with real and virtual environments. The aim of this study is to investigate the perceptions about the heuristic assessment of accessibility in software by undertaking a systematic review of the literature that covers the last 5 years. This started off with a total of 8548 published papers that are indexed in the Scopus and Web of Science databases. Twelve relevant research questions were drawn up that were answered in accordance with the results found in the final set of articles. Two groups of keywords were created to assist in the search for papers and a filtering process was applied to the results of the searches. The research shows, by means of figures and tables, evidence about the growth of scientific papers in the area. The lead authors and journals that publish on evaluating the accessibility of software are listed, as are answers to the questions about which evaluative models, heuristics, metrics, limitations, assistive technologies and target audiences are most cited in published papers. This study also analyzes the co-citation of the references and couples bibliographic sources. The research showed evidence that there is no general consensus on the use of a single model to assess accessibility, but that the W3C WCAG guidelines are the sources most used to reference heuristics and metrics in studies of this type. It was also noticed that most studies do not focus on assistive technologies, but are widely used to support decisions on websites.

Access Control Design Practice and Solutions in Cloud-Native Architecture: A Systematic Mapping Study.

Protecting the resources of a cloud-native application is essential to meet an organization's security goals. Cloud-native applications manage thousands of user requests, and an organization must employ a proper access control mechanism. However, unfortunately, developers sometimes grumble when designing and enforcing access decisions for a gigantic scalable application. It is sometimes complicated to choose the potential access control model for the system. Cloud-native software architecture has become an integral part of the industry to manage and maintain customer needs. A microservice is a combination of small independent services that might have hundreds of parts, where the developers must protect the individual services. An efficient access control model can defend the respective services and consistency. This study intends to comprehensively analyze the current access control mechanism and techniques utilized in cloud-native architecture. For this, we present a systematic mapping study that extracts current approaches, categorizes access control patterns, and provides developers guidance to meet security principles. In addition, we have gathered 234 essential articles, of which 29 have been chosen as primary studies. Our comprehensive analysis will guide practitioners to identify proper access control mechanisms applicable to ensuring security goals in cloud-native architectures.

Effective Mechanical Thrombectomy for Posterior Circulation Ischemia Using Magnetic Resonance Imaging-based Arterial Structures.

To improve the success of mechanical thrombectomy, three-dimensional turbo spin-echo (3D-TSE) sequences on T2WI can be employed to estimate the vascular structure of the posterior circulation. In addition to the short imaging time of 3D-TSE T2WI (33 sec), it can visualize the outer diameter of the main cerebral artery, including the occluded vessels. However, to date, the efficacy of mechanical thrombectomy in the posterior circulation remains unclear, and safer and more efficient mechanical thrombectomy procedures are required. Assessment of the anatomical variations in the posterior circulation using 3D-TSE T2WI is valuable for access decisions, device selection, and safe device guidance and retrieval techniques to the target vessel. Herein, we present representative cases of basilar artery and posterior cerebral artery occlusions in our institute and describe the utility of preoperative 3D-TSE T2WI in these patients.

Learning Based Multi-Channel Access in Vehicular Networks With Illegal Operators and Users

This paper investigates the multi-channel access (MCA) problem for vehicular networks in presence of illegal operators and users. Due to the limited spectrum resource, vehicle-to-vehicle (V2V) links need to reuse the frequency spectrum allocated for vehicle-to-infrastructure (V2I) links in networks. For high-mobility environments of vehicular networks, the fast-varying channel conditions will inevitably lead to the significant uncertainty of the acquired channel state information (CSI). Hence, the traditional centralized MCA methods, which rely on the CSI, could not be performed in a timely manner. We formulate the MCA of the vehicle users as a distributed optimization problem, by maximizing the sum capacity of V2I links while guaranteeing the reliability of V2V links as well as reducing the cost due to frequency hopping. A Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework is introduced to tackle this problem, where each vehicle user, connecting with either a V2I or V2V link, acts as a learning agent to make spectrum access decisions rapidly and locally to meet the latency requirement. In order to adapt to the dynamic environment, we further propose a delayed interaction aided MADDPG algorithm, which enables online training for the distributed MCA problem. Moreover, to guarantee the proposed algorithm to converge stably, we optimize the selecting weights of experience traces and propose a Batch Prioritized Experience Replay (BPER) strategy to ensure that the high priority traces can be learned timely. Compared with baselines, simulation results showcase remarkable performance gain of our proposed algorithm.

Influence of Financial Literacy on Micro-Credit Accessibility Among Rural Households in Tanzania

Purpose: Microcredits will remain crucial to rural households over a long period as they provide small loans to the needy, making it an essential aspect of rural finance. Due to the rural credit market's structure and financial inclusion problem, rural households often have trouble accessing microcredit. This paper analyzes the influence of financial literacy (FL) on microcredit accessibility in rural households in Tanzania. Methodology: Using a large dataset of Finscope households survey in Tanzania, a binary logit estimation technique was utilized to determine the influence of financial literacy on credit accessing decisions of rural households. In addition, a descriptive analysis was done from the selected sample. Findings: Generally, the analysis showed that financial literacy had a negative and significant effect on the micro-credit accessibility decision of rural households in Tanzania. Education level has a significant positive influence on micro-credit attaining decisions. Practical implication: The outcome of this research is helpful to policymakers and micro-credit institutions as they provide an insight into the influence of financial literacy on microcredit accessibility. The study recommends financial literacy programmes be established in rural areas to enlighten the citizens about the benefit and proper uses of borrowed money. Originality/Value: This paper used actual surveyed data from Tanzanians in all regions. The results are robust to different categories of households and control variables that can also affect micro-credit accessibility decisions.

Service Convenience and Customer Loyalty among Non-Rated Hotels in Murang’a County, Kenya

Service environment is characterised by intense competition as organisations rival each other for customers. This underlines the significance of customer loyalty, as it is vital for a firm’s survival and growth. Building a loyal customer base has not only become a major marketing goal, but it is also an important basis for developing a sustainable competitive advantage. Keeping customers has been a challenge among hotels in the hospitality sector. Customers will continue to defect from one hotel to another, which has cost implications for the customer and the hotel itself. Therefore, this study aims to examine the effects of service convenience on customer loyalty in hotels in Murangá County, Kenya. Specifically, the study sought to establish the effects of SERVCON scale dimensions (access convenience, decision convenience, transaction convenience, benefit, and post-benefit conveniences) on customer loyalty. The study targeted establishments that are hotels in the County. A descriptive cross-sectional survey design was used. Data was collected from customers dining in the hotels through self-reported questionnaires. Moreover, 384 respondents were conveniently selected. Variables were measured using adapted and modified constructs from previous research. Questionnaires were pretested in one hotel in Kiambu County to assess their usability for the study. Content validity was used to assess the validity of the questionnaire. The reliability of the constructs was examined using Cronbach’s Alpha coefficient technique. Descriptive statistics (means, percentages, and standard deviations) and inferential statistics (multiple linear regression analysis) were used to analyse quantitative data. The study revealed that service convenience dimensions have a significant positive influence on customer loyalty. The study results provide strong evidence that service convenience influences customer loyalty positively. This finding has great implications for managers of hotels in Murang’a County. Foremost, there is a need to ensure that information regarding the hotel is accessible via different platforms to ease customers’ decision-making. This can be done through an investment into available social media platforms and hiring sales representatives to keep the platforms active and up to date. Secondly, managers of hotels in Murang’a County need to ensure that it is easy to locate and access the hotels. To this end, there is a need to use crystal clear sign boards and lobby Murang’a County to ensure that access roads are updated to tarmac standards and maintained.

Correspondence: Debating China's Use of Overseas Ports

Correspondence: Debating China's Use of Overseas Ports

Public Ownership, Public Rights: Recreational Stream Access Decisions in the Mountain West

Public Ownership, Public Rights: Recreational Stream Access Decisions in the Mountain West

Enabling Attribute-based Access Control in NoSQL Databases.

NoSQL databases are being increasingly used for efficient management of high volumes of unstructured data in applications like information retrieval, natural language processing, social computing, etc. However, unlike traditional databases, data protection measures such as access control for these databases are still in their infancy, which could lead to significant vulnerabilities and security/privacy issues as their adoption increases. Attribute-based Access Control (ABAC), which provides a flexible and dynamic solution to access control, can be effective for mediating accesses in typical usage scenarios for NoSQL databases. In this paper, we propose a novel methodology for enabling ABAC in NoSQL databases. Specifically we consider MongoDB, which is one of the most popular NoSQL databases in use today. We present an approach to both specify ABAC access control policies and to enforce them when an actual access request has been made. MongoDB Wire Protocol is used for extracting and processing appropriate information from the requests. We also present a method for supporting dynamic access decisions using environmental attributes and handling of ad-hoc access requests through digitally signed user attributes. Results from an extensive set of experiments on the Enron corpus as well as on synthetically generated data demonstrate the scalability of our approach. Finally, we provide details of our implementation on MongoDB and share a Github repository so that any organization can download and deploy the same for enabling ABAC in their own MongoDB installations.

Accessible decision support for sustainable energy systems in developing countries

With rising electricity demand through digitization and innovation, the urgency of climate change mitigation, and the recent geopolitical crisis, stakeholders in developing countries face the complex task to build reliable, affordable, and low-emission energy systems. Information inaccessibility, data unavailability, and scarce local expertise are major challenges for planning and transitioning to decentralized solutions. Motivated by the calls for more solution-oriented research regarding sustainability, we design, develop, and evaluate the web-based decision support system NESSI4Dweb+ that is tailored to the needs and capabilities of various stakeholders in developing countries. NESSI4Dweb+ is open access and considers location-specific circumstances to facilitate multi-energy planning. Its applicability is demonstrated with a case study of a representative rural village in southern Madagascar and evaluated through seven interviews with experts and stakeholders. We show that NESSI4Dweb+ can support the achievement of the United Nations Sustainable Development Goals and enable the very prerequisite of digitization: reliable electrification.

Hemodialysis Arteriovenous Access Cosmesis Scale (AVACS): A new measure for vascular access.

This study aimed to develop a cosmesis scale to evaluate the cosmetic appearance of hemodialysis (HD) arteriovenous (AV) accesses from the perspective of the patient and clinician, which could be incorporated into clinical trials. Using a modified Delphi process, two AV access cosmesis scale (AVACS) components were developed in a four-round Delphi panel consisting of two surveys and two consensus meetings with two rounds of patient consultation. The Delphi panel consisted of 15 voting members including five interventional or general nephrologists, five vascular surgeons, three interventional radiologists, and two vascular access nurse coordinators. Four patients experienced with vascular access were involved in patient question development. For a component to be included in the AVACS, it had to meet the prespecified panel consensus agreement of ⩾70%. The clinician component of the AVACS includes nine questions on the following AV access features: scarring, skin discoloration, aneurysm/pseudoaneurysms and megafistula appearance. The patient component includes six questions about future vascular access decisions, interference with work or leisure activities, clothing choices, self-consciousness or attractiveness, emotional impact, and overall appearance. Delphi panel methods are subjective by design, but with expert clinical opinion are used to develop classification systems and outcome measures. The developed scale requires further validation testing but is available for clinical trial use. While safety and efficacy are the primary concerns when evaluating AV access for HD, cosmesis is an important component of the ESKD patient experience. The AVACS has been designed to assess this important domain; it can be used to facilitate patient care and education about vascular access choice and maintenance. AVACS can also be used to inform future research on developing new techniques for AV access creation and maintenance, particularly as relates to AV access cosmesis.

An Accessible Clinical Decision Support System to Curtail Anesthetic Greenhouse Gases in a Large Health Network: Implementation Study.

Inhaled anesthetics in the operating room are potent greenhouse gases and are a key contributor to carbon emissions from health care facilities. Real-time clinical decision support (CDS) systems lower anesthetic gas waste by prompting anesthesia professionals to reduce fresh gas flow (FGF) when a set threshold is exceeded. However, previous CDS systems have relied on proprietary or highly customized anesthesia information management systems, significantly reducing other institutions' accessibility to the technology and thus limiting overall environmental benefit. In 2018, a CDS system that lowers anesthetic gas waste using methods that can be easily adopted by other institutions was developed at the University of California San Francisco (UCSF). This study aims to facilitate wider uptake of our CDS system and further reduce gas waste by describing the implementation of the FGF CDS toolkit at UCSF and the subsequent implementation at other medical campuses within the University of California Health network. We developed a noninterruptive active CDS system to alert anesthesia professionals when FGF rates exceeded 0.7 L per minute for common volatile anesthetics. The implementation process at UCSF was documented and assembled into an informational toolkit to aid in the integration of the CDS system at other health care institutions. Before implementation, presentation-based education initiatives were used to disseminate information regarding the safety of low FGF use and its relationship to environmental sustainability. Our FGF CDS toolkit consisted of 4 main components for implementation: sustainability-focused education of anesthesia professionals, hardware integration of the CDS technology, software build of the CDS system, and data reporting of measured outcomes. The FGF CDS system was successfully deployed at 5 University of California Health network campuses. Four of the institutions are independent from the institution that created the CDS system. The CDS system was deployed at each facility using the FGF CDS toolkit, which describes the main components of the technology and implementation. Each campus made modifications to the CDS tool to best suit their institution, emphasizing the versatility and adoptability of the technology and implementation framework. It has previously been shown that the FGF CDS system reduces anesthetic gas waste, leading to environmental and fiscal benefits. Here, we demonstrate that the CDS system can be transferred to other medical facilities using our toolkit for implementation, making the technology and associated benefits globally accessible to advance mitigation of health care-related emissions.

Person-based co-design of a decision aid template for people with a genetic predisposition to cancer.

People with genetic predispositions to cancer are faced with complex health decisions about managing their risk. Decision aids can support informed, values-based decisions, alongside shared decision-making with a clinician. Whilst diagnoses of genetic predispositions to cancer are increasing, there is no scalable decision aid to support these people. This paper presents an accessible, relevant decision aid template which can be adapted for different predispositions to cancer. The decision aid template was co-developed with 12 patients affected by cancer and informed by empirical and theoretical literature. In addition, consultations were conducted with a further 19 people with Lynch syndrome; a specific genetic predisposition to cancer. Clinical stakeholders were consulted regularly. Coulter's framework for decision aid development guided the process, and these activities were complemented by the International Patient Decision Aid Standards, and the latest evidence on communicating risk in decision aids. Programme theory was developed to hypothesise how the decision aid would support decision-making and contextual factors which could influence the process. Guiding principles co-developed with the patient panel described how the decision aid could effectively engage people. The in-depth co-design process led to the identification of five core components of an accessible decision aid template for people with a genetic predisposition to cancer: defining the decision; option grid showing implications of each option; optional further details such as icon arrays to show tailored risk and personal narratives; values clarification activity; and a summary to facilitate discussion with a clinician. Specific guidance was produced describing how to develop each component. The guiding principles identified that the decision aid template needed to promote trust, reduce distress, and be comprehensive, personally relevant and accessible in order to engage people. Adopting a co-design process helped ensure that the decision aid components were relevant and accessible to the target population. The template could have widespread application through being adapted for different genetic predispositions. The exact content should be co-designed with people from diverse backgrounds with lived experience of the specific predisposition to ensure it is as useful, engaging and relevant as possible.

Using automated methods to detect safety problems with health information technology: a scoping review.

To summarize the research literature evaluating automated methods for early detection of safety problems with health information technology (HIT). We searched bibliographic databases including MEDLINE, ACM Digital, Embase, CINAHL Complete, PsycINFO, and Web of Science from January 2010 to June 2021 for studies evaluating the performance of automated methods to detect HIT problems. HIT problems were reviewed using an existing classification for safety concerns. Automated methods were categorized into rule-based, statistical, and machine learning methods, and their performance in detecting HIT problems was assessed. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses extension for Scoping Reviews statement. Of the 45 studies identified, the majority (n = 27, 60%) focused on detecting use errors involving electronic health records and order entry systems. Machine learning (n = 22) and statistical modeling (n = 17) were the most common methods. Unsupervised learning was used to detect use errors in laboratory test results, prescriptions, and patient records while supervised learning was used to detect technical errors arising from hardware or software issues. Statistical modeling was used to detect use errors, unauthorized access, and clinical decision support system malfunctions while rule-based methods primarily focused on use errors. A wide variety of rule-based, statistical, and machine learning methods have been applied to automate the detection of safety problems with HIT. Many opportunities remain to systematically study their application and effectiveness in real-world settings.

Semantic Attribute-Based Encryption: A framework for combining ABE schemes with semantic technologies

Attribute-Based Encryption (ABE) is a cryptographic solution to protect resources in a fine-grained manner based on a set of public attributes. This is similar to attribute-based access control schemes in the sense that both rely on public attributes and access control policies to grant access to resources. However, ABE schemes do not consider the semantics of attributes provided by users or required by access structures. Such semantics not only improve the functionality by making proper access decisions but also enable cross-domain interoperability by making users from one domain able to access and use resources of other domains. This paper proposes a Semantic ABE (SABE) framework by augmenting a classical Ciphertext-Policy ABE (CP-ABE) scheme with semantic technologies using a generic procedure by which any CP-ABE scheme can be extended to an SABE. The proposed SABE framework is implemented in Java and the source code is publicly available. The experiment results confirm that the performance of the proposed framework is promising.

Development and evaluation of cloud-based virtual reality for design evaluation: multicriteria comparative analysis

PurposeThe study aims to introduce a cloud-based virtual reality (VR) approach and investigate its applicability and performance in aiding the remote design evaluation process by assessing the clients' convenience perception toward cloud-based VR-aided design evaluation (Cloud-based VR Approach) compared to 3D model-aided design evaluation (3D Model Approach) and rendering images-aided design evaluation (Image Approach).Design/methodology/approachA multicriteria comparative study was conducted with 26 university students using the “analytic hierarchy process” technique to compare the three approaches applied to home finishing material selection tasks based on the five “service convenience” dimensions, consisting of access convenience, decision convenience, transaction convenience, benefit convenience and post-benefit convenience.FindingsThe results indicated that the “Cloud-based VR Approach” was perceived to be more convenient than the “3D Model Approach” and the “Image Approach” based on the aspects of “decision convenience”, “transaction convenience”, “benefit convenience” and “post-benefit convenience”. The only aspect that the Cloud-based VR Approach was comparatively less convenient than the 3D Model Approach and Image Approach for the user was “access convenience”. Overall, the findings showed that the developed Cloud-based VR Approach had more potential than the conventional approaches in aiding the design evaluation process under ongoing social distancing measures requiring designers and clients to work remotely.Originality/valueThe disastrous impacts of the COVID-19 pandemic on logistical systems have resulted in massive disruptions to the construction industry worldwide. Various construction activities have been halted and most meetings moved online. Design evaluation conducted between clients and designers is one of the important activities affected by such an impact. Thus, this study presents the Cloud-based VR Approach as an innovative means to maintain essential ongoing activities and meeting of the current design evaluation approach with respect to the social distancing measures.

In-Depth Analysis and Systematic Literature Review on Risk Based Access Control in Cloud

Security in Cloud is one of the most foremost and critical feature, which can ensure the confidence of the Scientific community on Cloud environment. With the dynamic and ever changing nature of the Cloud computing environment, static access control models become obsolete. Hence, dynamic access control models are required, which is still an emergent and underdeveloped domain in Cloud security. These models utilize not only access policies but also contextual and real-time information to determine the access decision. Out of these dynamic models the Risk-based Access control model, estimates the security risk value related to the access request dynamically to determine the access decision. The exclusive working pattern of this access control model makes it an excellent choice for dynamically changing environment that rules the cloud’s environment. This paper provides a systematic literature appraisal and evaluation of risk-based access control models to provide a detailed understanding of the topic. The contributions of selected articles have been summarized. The security risks in cloud environment have been reviewed, taking in the account of both Cloud Service Provider and Cloud Customer perspectives. Additionally, risk factors used to build the risk-based access control model were extracted and analyzed. Finally, the risk estimation techniques used to evaluate the risks of access control operations have also been identified.

ANFIS for risk estimation in risk-based access control model for smart homes

The risk-based access control model is one of the dynamic models that use the security risk as a criterion to decide the access decision for each access request. This model permits or denies access requests dynamically based on the estimated risk value. The essential stage of implementing this model is the risk estimation process. This process is based on estimating the possibility of information leakage and the value of that information. Several researchers utilized different methods for risk estimation but most of these methods were based on qualitative measures, which cannot suit the access control context that needs numeric and precise risk values to decide either granting or denying access. Therefore, this paper presents a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) model for risk estimation in the risk-based access control model for the Internet of Things (IoT). The proposed ANFIS model was implemented and evaluated against access control scenarios of smart homes. The results demonstrated that the proposed ANFIS model provides an efficient and accurate risk estimation technique that can adapt to the changing conditions of the IoT environment. To validate the applicability and effectiveness of the proposed ANFIS model in smart homes, ten IoT security experts were interviewed. The results of the interviews illustrated that all experts confirmed that the proposed ANFIS model provides accurate and realistic results with a 0.713 in Cronbach’s alpha coefficient which indicates that the results are consistent and reliable. Compared to existing work, the proposed ANFIS model provides an efficient processing time as it reduces the processing time from 57.385 to 10.875 Sec per 1000 access requests, which demonstrates that the proposed model provides effective and accurate risk evaluation in a timely manner.

Etiology of Macular Edema Defined by Deep Learning in Optical Coherence Tomography Scans.

PurposeTo develop an automated method based on deep learning (DL) to classify macular edema (ME) from the evaluation of optical coherence tomography (OCT) scans.MethodsA total of 4230 images were obtained from data repositories of patients attended in an ophthalmology clinic in Colombia and two free open-access databases. They were annotated with four biomarkers (BMs) as intraretinal fluid, subretinal fluid, hyperreflective foci/tissue, and drusen. Then the scans were labeled as control or ocular disease among diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), and retinal vein occlusion (RVO) by two expert ophthalmologists. Our method was developed by following four consecutive phases: segmentation of BMs, the combination of BMs, feature extraction with convolutional neural networks to achieve binary classification for each disease, and, finally, multiclass classification of diseases and control images.ResultsThe accuracy of our model for nAMD was 97%, and for DME, RVO, and control were 94%, 93%, and 93%, respectively. Area under curve values were 0.99, 0.98, 0.96, and 0.97, respectively. The mean Cohen's kappa coefficient for the multiclass classification task was 0.84.ConclusionsThe proposed DL model may identify OCT scans as normal and ME. In addition, it may classify its cause among three major exudative retinal diseases with high accuracy and reliability.Translational RelevanceOur DL approach can optimize the efficiency and timeliness of appropriate etiological diagnosis of ME, thus improving patient access and clinical decision making. It could be useful in places with a shortage of specialists and for readers that evaluate OCT scans remotely.

Evaluation of short-term probabilistic eruption forecasting at Whakaari, New Zealand

Phreatic explosions at volcanoes are difficult to forecast but can be locally devastating, as illustrated by the deadly 2019 Whakaari (New Zealand) eruption. Quantifying eruption likelihood is essential for risk calculations that underpin volcano access decisions and disaster response. But estimating eruption probabilities is notoriously difficult for sudden onset eruptions. Here, we describe two retrospectively developed models for short-term (48 h) probabilistic forecasting of phreatic eruptions at Whakaari. The models are based on a pseudo-prospective analysis of seven Whakaari eruptions whose precursors were identified by time series feature engineering of continuous seismic data. The first model, an optimized warning system, could anticipate six out of seven eruptions at the cost of 14 warning days each year. While a warning is in effect, the probability of eruption is about 8% in 48 h, which is about 126 times higher than outside the warning. The second model used isotonic calibration to translate the output of the forecast model onto a probability scale. When applied pseudo-prospectively in the 48 h prior to the December 2019 eruption, it indicated an eruption probability up to 400 times higher than the background. Finally, we quantified the accuracy of these seismic data-driven forecasts, alongside an observatory expert elicitation that used multiple data sources. To do this, we used a forecast skill score that was benchmarked against the average rate of eruptions at Whakaari between 2011 and 2019. This exercise highlights the conditions under which the three different forecasting approaches perform well and where potential improvements could be made.