Trustworthiness Measurement Research Articles

Facial Anthropomorphic Trustworthiness Scale for Social Robots: A Hybrid Approach.

Social robots serve as autonomous systems for performing social behaviors and assuming social roles. However, there is a lack of research focusing on the specific measurement of facial trustworthiness toward anthropomorphic robots, particularly during initial interactions. To address this research gap, a hybrid deep convolution approach was employed in this study, involving a crowdsourcing platform for data collection and deep convolution and factor analysis for data processing. The goal was to develop a scale, called Facial Anthropomorphic Trustworthiness towards Social Robots (FATSR-17), to measure the trustworthiness of a robot's facial appearance. The final measurement scale comprised four dimensions, "ethics concern", "capability", "positive affect", and "anthropomorphism", consisting of 17 items. An iterative examination and a refinement process were conducted to ensure the scale's reliability and validity. The study contributes to the field of robot design by providing designers with a structured toolkit to create robots that appear trustworthy to users.

Trustworthy Medical Image Segmentation with improved performance for in-distribution samples

Despite the enormous achievements of Deep Learning (DL) based models, their non-transparent nature led to restricted applicability and distrusted predictions. Such predictions emerge from erroneous In-Distribution (ID) and Out-Of-Distribution (OOD) samples, which results in disastrous effects in the medical domain, specifically in Medical Image Segmentation (MIS). To mitigate such effects, several existing works accomplish OOD sample detection; however, the trustworthiness issues from ID samples still require thorough investigation. To this end, a novel method TrustMIS (Trustworthy Medical Image Segmentation) is proposed in this paper, which provides the trustworthiness and improved performance of ID samples for DL-based MIS models. TrustMIS works in three folds: IT (Investigating Trustworthiness), INT (Improving Non-Trustworthy prediction) and CSO (Classifier Switching Operation). Initially, the IT method investigates the trustworthiness of MIS by leveraging similar characteristics and consistency analysis of input and its variants. Subsequently, the INT method employs the IT method to improve the performance of the MIS model. It leverages the observation that an input providing erroneous segmentation can provide correct segmentation with rotated input. Eventually, the CSO method employs the INT method to scrutinise several MIS models and selects the model that delivers the most trustworthy prediction. The experiments conducted on publicly available datasets using well-known MIS models reveal that TrustMIS has successfully provided a trustworthiness measure, outperformed the existing methods, and improved the performance of state-of-the-art MIS models. Our implementation is available at https://github.com/SnehaShukla937/TrustMIS.

Overcoming language barriers: An exploration of the police perceptions of the digitisation of witness statements in South Africa

The research identified instances were the English version of sworn statements was an inaccurate translation of the indigenous statement provided by a witness or complainant. The inaccurate translation of indigenous witness statements is a global phenomenon that manifests against the backdrop of the fourth industrial revolution (4IR). This study was conducted to explore police experiences of taking indigenous witness statements, and to establish their perceptions of the digitisation of witness statements. Purposive sampling was used for sampling. Interview schedules were administered to eighteen (18) South African Police Service (SAPS) members from nine Community Service Centres (CSC) in Soweto, a township in the Gauteng province. Ethical consideration, measures of trustworthiness and the Theory of Performance (ToP) were applied. The ToP provides that a person’s level of performance is dependent on the interaction of various contextual factors. Thematic content analysis was conducted using Atlas ti resulting in five dominant themes or impact factors namely “skills”, “collaboration”, “resources”, “duration” and “language”. Results showed that a majority of the SAPS members perceived that witness statements should be digitised to address language challenges experienced and improve their level of performance. Furthermore, they perceived the usefulness of technology in modernising outdated resources in the SAPS.

A validity and reliability study of the Turkish computational thinking scale

The purpose of this study was to adapt the computational thinking scale developed by Tsai, Liang and Hsu (2021) into Turkish in order to determine the computational thinking skill levels of secondary school students according to such basic elements defined by Selby and Woollard (2013) as abstraction, decomposition, algorithmic thinking, evaluation and generalization and to do the related validity and reliability study. A total of 454 high school students (9th – 12th grade) determined with the convenient sampling method constituted the sample of the study. The original scale was made up of 19 5-point Likert-type items. Confirmatory Factor Analysis (CFA) was performed to examine the conformity of the data collected via the adapted scale to the five-factor structure of the original scale. As a result of CFA, it was seen that the factor structure of the original scale was preserved. The reliability of the scale was checked with the internal consistency coefficient for the whole scale and its factors. The Cronbach Alpha coefficients obtained were .84 and McDonald's omega coefficients obtained were .86. The scale's Turkish adaptation was found to be a valid and trustworthy measurement tool for establishing the computational thinking proficiency levels of students in high school.

Validation of Himawari-8 Sea Surface Temperature Retrievals Using Infrared SST Autonomous Radiometer Measurements

This study has evaluated five years (2016–2020) of Himawari-8 (H8) Sea Surface Temperature (SST) Level 2 Pre-processed (L2P) data produced by the Australian Bureau of Meteorology (Bureau) against shipborne radiometer SST measurements obtained from the Infrared SST Autonomous Radiometer (ISAR) onboard research vessel RV Investigator. Before being used, all data sets employed in this study have gone through careful quality control, and only the most trustworthy measurements are retained. With a large matchup database (31,871 collocations in total, including 16,418 during daytime and 15,453 during night-time), it is found that the Bureau H8 SST product is of good quality, with a mean bias ± standard deviation (SD) of −0.12 °C ± 0.47 °C for the daytime and −0.04 °C ± 0.37 °C for the night-time. The performance of the H8 data under different environmental conditions, determined by the observations obtained concurrently from RV Investigator, is examined. Daytime and night-time satellite data behave slightly differently. During the daytime, a cold bias can be seen under almost all environmental conditions, including for most values of wind speed, SST, and relative humidity. On the other hand, the performance of the night-time H8 SST product is consistently more stable under most meteorological conditions with the mean bias usually close to zero.

The Impact of Trustworthiness on the Association of Corporate Social Responsibility and Irresponsibility on Legitimacy

AbstractResearch on corporate social responsibility (CSR) and corporate social irresponsibility (CSIR) has long argued both affect firms' legitimacy, for good or bad. Such research has ignored how ex‐ante corporate trustworthiness and associated attributions affect the association of CSR and CSIR on corporate normative legitimacy. Focusing on two unique aspects of corporate normative legitimacy evaluations – affect and misconduct – we argue that ex‐ante perceptions of firm trustworthiness moderate the associations among CSR and CSIR and different aspects of normative legitimacy. Utilizing comprehensive panel‐data analytical approaches for S&P 500 firms from 2000 to 2015, MSCI‐KLD data, mass‐media‐based measures of firm trustworthiness, including normative affect‐based legitimacy, and normative misconduct‐related‐legitimacy, our proposition is mostly supported, with surprising caveats. A post‐hoc analysis shows these associations vary based on public versus investors' affective‐legitimacy views. The findings of this study critically challenge extant scholarship and call for a nuanced view of the impact of CSR and CSIR on firm legitimacy.

What are you assessing when you measure "trust" in scientists with a direct measure?

This article analyzes three publicly available datasets focused on trust in science and scientists. It specifically seeks to understand what direct measures of trust (i.e. questions that directly ask respondents how much they trust scientists) assess in terms of discrete measures of trustworthiness (i.e. perceptions of scientists' ability, integrity, and benevolence). Underlying the analyses is a concern that direct measures of trust are a poor substitute for differentiating between discrete trustworthiness perceptions and behavioral trust in the form of a specific willingness to make oneself vulnerable. The research concludes that it is unclear what direct trust measures are capturing in any given context and suggests that researchers should better use trust-related theory when designing surveys and trust-focused campaigns. The secondary data used come from the General Social Survey, Gallup, and the Pew Research Center.

Non-adherence to growth monitoring and promotion sessions amongst caregivers of children under 5 years in Polokwane Municipality, Limpopo province.

Child growth is crucial for nutritional and health status; poor growth may result in stunting. South Africa experiences a high prevalence of stunting, micronutrient deficiencies and late identification of growth faltering. Non-adherence to growth monitoring and promotion (GMP) sessions remains a challenge and caregivers contribute to non-adherence. Therefore, this study explores factors affecting the non-adherence of GMP services. Qualitative approach and phenomenological exploratory study design were used. One-on-one interviews were conducted with 23 participants conveniently sampled. Sampling size was dependent on data saturation. Voice recorders were used to capture data. Tesch's eight steps, inductive, descriptive and open coding techniques were used to analyse data. Measures of trustworthiness were ensured through credibility, transferability, dependability and confirmability. Participants indicated non-adherence to GMP sessions because of a lack of knowledge of the importance of adherence and poor service by healthcare workers, which includes long waiting hours. Inconsistent availability of GMP services at healthcare facilities and growth of the firstborn children with non-adherence to GMP sessions are factors influencing participants' adherence. A lack of transportation and lunch money also contributed to non-adherence to sessions. A lack of knowledge of the importance of adherence to GMP sessions, long waiting hours and inconsistent availability of GMP services at facilities contributed enormously to non-adherence. Therefore, the Department of Health must ensure consistent availability of GMP services to demonstrate the importance and enable adherence. Healthcare facilities should reduce waiting hours to minimise the need for lunch money, and service delivery audits should be conducted to identify other factors contributing to non-adherence to address these.Contribution:Primary health care providers should conduct service delivery audits and internal surveys to identify factors that contribute to non-adherence in order to introduce measures to address them.

Trustworthy and Reliable Deep Learning-based Cyberattack Detection in Industrial IoT.

A fundamental expectation of the stakeholders from the Industrial Internet of Things (IIoT) is its trustworthiness and sustainability to avoid the loss of human lives in performing a critical task. A trustworthy IIoT-enabled network encompasses fundamental security characteristics such as trust, privacy, security, reliability, resilience and safety. The traditional security mechanisms and procedures are insufficient to protect these networks owing to protocol differences, limited update options, and older adaptations of the security mechanisms. As a result, these networks require novel approaches to increase trust-level and enhance security and privacy mechanisms. Therefore, in this paper, we propose a novel approach to improve the trustworthiness of IIoT-enabled networks. We propose an accurate and reliable supervisory control and data acquisition (SCADA) network-based cyberattack detection in these networks. The proposed scheme combines the deep learning-based Pyramidal Recurrent Units (PRU) and Decision Tree (DT) with SCADA-based IIoT networks. We also use an ensemble-learning method to detect cyberattacks in SCADA-based IIoT networks. The non-linear learning ability of PRU and the ensemble DT address the sensitivity of irrelevant features, allowing high detection rates. The proposed scheme is evaluated on fifteen datasets generated from SCADA-based networks. The experimental results show that the proposed scheme outperforms traditional methods and machine learning-based detection approaches. The proposed scheme improves the security and associated measure of trustworthiness in IIoT-enabled networks.

The Trustworthiness Measurement Model of Component Based on Defects

In modern software engineering, the component-based development approach has become one of the important trends in software development technology. The trustworthiness of components plays a vital role in developing component-based trustworthy software. If there exist defects in components, then the trustworthiness of the component will be reduced, and the trustworthiness of the software system will be influenced. In this case, it is necessary to measure the trustworthiness of the component in terms of the defect. In this paper, a trustworthiness measurement model of components will be proposed based on defects. Firstly, the defect types are formalized according to the component specification. Secondly, the weight allocation method of defect types is designed based on the correlation between defect types and experts’ evaluation. The value of the trustworthiness attribute is estimated by using the risk value of the defect and the weight of the defect type. Furthermore, the trustworthiness measurement model of the component is proposed, the corresponding algorithm is designed, and some algebra properties are proved. Finally, a case study is used to illustrate the application of the model.

An Experimental Investigation of Trusting Behaviour

In this paper, we present the results of an experiment conducted in Italy on trusting behaviour. Our subjects participated in a trust game and filled in a questionnaire on trust and trustworthiness based on the attitudinal questions reported in the European Value Survey. The aims of the research are twofold. Firstly, using the experimental dataset, we construct two measures of trustworthiness among all recipients in the experiment, one based on the questionnaires’ answers and another based on the strategy method. We then compare the ex-ante behavioural decision to trust (before participants are allocated to a group) with the ex-post decision to trust (after participants are allocated to a group and trustors are informed of the level of trustworthiness of all the recipients who have been randomly allocated to each group). Our main finding is that trust strongly varies once the information is disclosed to trustors. The effect on trust is greater when the strategy method is used. Secondly, we compare the behavioural measure of trust with the attitudinal measures of trust and trustworthiness and find that there is only a weak correlation between the two measures, thus confirming, for the Italian case, similar findings in country-based studies on trust.

A Novel Software Trustworthiness Evaluation Strategy via Relationships between Criteria

Software trustworthiness evaluation is regarded as a multi-criteria decision-making problem. However, most current software trustworthiness evaluation methods do not consider the relationships between criteria. In this paper, we present a software trustworthiness evaluation strategy via the relationships between criteria. Because the trustworthy attribute degree is evaluated by a criterion, a trustworthy attribute measurement method based on fuzzy theory is first proposed, and the relationships between criteria are described by cooperative and conflicting degrees between criteria. Then, a measure formula for the symmetric substitutivity between criteria is proposed, and the cooperative degree between criteria is taken as the approximation of the symmetric substitutivity between criteria. With the help of the symmetric substitutivity between criteria, the software trustworthiness measurement model obtained by axiomatic approaches is applied to aggregate the degree to which each optional software product meets each objective. Finally, the candidate software products are sorted according to the trustworthiness aggregation results, and the optimal product is obtained from the alternative software products on the basis of the sorting results. The theoretical rationality of the measurement model is validated by proving that it satisfies the desirable properties of software trustworthiness measures, and its effectiveness is demonstrated through a case study.

Technical Committee on Fault Tolerant Measurement Systems

The goal of Technical Committee on Fault Tolerant Measurement Systems (TC-32) is to promote the research, training and outreach activities that cover all aspects of theory and practices of ensuring trustworthy measurement results under every operating condition. This is especially important in critical infrastructures where the cost of failure is very high. In recent years, TC-32 promotes and supports technical activities in reliable computation, propagation of uncertainty in signal processing systems and architectures/infrastructures/workflows to ensure fault tolerant instrumentation and measurements.

SaaS Service Combinatorial Trustworthiness Measurement Method Based on Markov Theory and Cosine Similarity

With the rapid progress of information technology, cloud computing and cloud services are widely accepted and applied to all aspects of social life. In the cloud computing environment, SaaS (Software-as-a-Service) services have become the main form of software services. For SaaS services, evolutionary and iterative development methods have become the main methods of software system construction. For systems with high trustworthiness, the independent trustworthiness of each SaaS service has a great impact on the overall status. However, SaaS services with high independent trustworthiness do not always build highly trusted software systems. The combinatorial trustworthiness between SaaS services is as important as the independent trustworthiness of each SaaS service. This paper takes combinatorial trustworthiness between SaaS services as the research object. Combinatorial trustworthiness measurement method based on Markov and cosine similarity theory is proposed. The feasibility and effectiveness of the proposed method are verified through simulation experiments. Applicable scenarios, advantages, and disadvantages of the proposed method are shown through the comparison of different measurement methods. The proposed method provides theoretical and technical support for users to select SaaS services suitable for their application scenarios, build cloud service systems, and monitor the operation status of cloud service systems.

Tracking Student Outcomes Through Instructional Choices in Physical Education

This study used self-determination theory to provide a better understanding of learner motivation in a physical education program. Students participated in a sports unit with choice in activities, equipment, partners, and competition as opposed to a traditional teacher-led activity unit. The objective was to determine differences in motivation and perceptions of physical education in grade school children. An alternating treatment design was used in which students experienced both a teacher-led unit and a student choice–driven sports unit. Fifty fifth-grade students from a suburban school in the Western United States participated in this study. Students were assessed through a number of parameters: physical activity enjoyment, situational motivation, fourth- and fifth-grade student attitudes toward physical education, pedometer readings, and interviews. This study used t tests and repeated-measures analyses of variance to explore differences based on condition (i.e., choices and/or teacher led) and gender, as well as common comparisons (i.e., trustworthiness measures) for the interview data. Students displayed significantly higher enjoyment ratings when they had the choice condition second (from the alternating treatment design), immediately before posttests, and boys rated enjoyment of physical education significantly higher than did girls. In another measure, the group with choices of the hockey unit was predominantly more active in the sport than those participating in the teacher-led activity. The interview themes elicited the following responses: (a) students expressed a desire for instructional choices, but not in team selections; (b) students loved sports, but they also wanted more fitness; and (c) students identified and valued instructional choices. The findings suggest that competence plays a critical role in physical education, and when students can choose the level of competition and play in small group games, they feel more competent, play more, and have higher enjoyment ratings within the physical education program.

Science, Medicine, and the Anesthesiologist

Science, Medicine, and the Anesthesiologist

Trusted Cloud Service System Based on Block Chain Technology

With the development and popularization of cloud computing technology, more and more users choose cloud services to build their application systems. With the improvement of users’ understanding of software systems, in addition to functionality, trustworthiness has become another key issue concerned by users. Based on the existing research on cloud service trustworthiness measurement and evaluation, this paper proposes a cloud service-trusted delivery model based on asymmetric encryption and hash function and a trusted runtime model of cloud service system based on block chain technology. The proposed models will effectively solve the untrusted problems such as denial and tampering in the process of cloud service acquisition, as well as the system anomaly at runtime. Finally, through targeted experiments to verify the effectiveness and feasibility of the proposed models, and through experimental analysis, this paper expounds on the principle and mechanism of model operation and trustworthiness guarantee.

Reputation-Based Consensus for Blockchain Technology in Smart Grid

Accurate and thorough measurement of all nodes' trustworthiness should create a safer network environment. We designed a blockchain-based, completely self-operated reputation system, R360, to enhance network security in decentralized network. R360 is a multi-factor measurement on the reputation of all nodes in the network. Specifically, a node's function, defense capability, quality of service provided, availability, malicious behavior, and resources are evaluated, providing a more accurate picture about a node's trustworthiness, which in turn should enhance the security of blockchain operations in a non-trust environment. Our design has been implemented on a single-machine, simulated setting, which demonstrates that, comparing to systems with few dimensions, R360's consensus took less time while at the same time providing better security to the system. Further security analysis shows that our design can defend common security attacks on reputation systems. Such a blockchain-based, self-operated reputation system could be used to manage smat grids for more efficient energy production and delivery.

A Novel Trustworthiness Measurement Model Based on Weight and User Feedback

Software trustworthiness is an essential criterion for evaluating software quality. In component-based software, different components play different roles and different users give different grades of trustworthiness after using the software. The two elements will both affect the trustworthiness of software. When the software quality is evaluated comprehensively, it is necessary to consider the weight of component and user feedback. According to different construction of components, the different trustworthiness measurement models are established based on the weight of components and user feedback. Algorithms of these trustworthiness measurement models are designed in order to obtain the corresponding trustworthiness measurement value automatically. The feasibility of these trustworthiness measurement models is demonstrated by a train ticket purchase system.

Securing Smart City Services in Cyber-Physical Systems Using the Computation Annealed Selection Process

Cyber-physical systems in a smart city environment offer secure computations in addition to robust resources and secure information exchanges. From the security aspect, the selection of legitimate computing resources is considered the most trustworthy measure for preserving user and data privacy. However, the outlier information results in false computations and time-consuming privacy measures for smart city users. This article introduces the Computation Annealed Selection Process (CASP) for combating outlier information in cyber-physical system networks. This process is assimilated with different infrastructure units in the smart city in an adaptable fashion. The adaptive measure administers information and user privacy through mutual time-key-based authentication. Information privacy is endured until the smart city provider provides a valid service interval. W2The service interval is evaluated for its consistency in maintaining the privacy and is validated using decision-tree learning. The decision tree performs interval classification and key assignment recommendations. Based on the decisions, the privacy is either prolonged or withdrawn for the information exchanged between the users and service providers. This process is repeated until the end of the service interval, identifying the outlier information in legitimate intervals. The proposed process improves detection, legitimacy rate, interval time, and interval rate by 7.57%, 10.4%, 13.15%, and 7.59%, respectively.