AIBench: Towards trustworthy evaluation under the 45°law

Trustworthiness of software and services is a key concern for their use and adoption by organizations and endusers. Trustworthiness evaluation is an important task to support both providers and consumers in making informed decisions, i.e., for selecting components from a software marketplace. Most of the literature evaluates trustworthiness by focusing on a single dimension (e.g., from the security perspective) while there are limited contributions towards multifaceted and end-to-end trustworthiness evaluation. Our analysis reveals that there is a lack of a comprehensive framework for comparative, multifaceted end-to-end trustworthiness evaluation, which takes into account different layers of abstractions of both the system topology and its trustworthiness. In this paper, we provide a framework for end-to-end trustworthiness evaluation using computational approaches, which is based on aggregating certified trustworthiness values for individual components. The resulting output supports in defining trustworthiness requirements for a software component to be developed and eventually integrated within a system, as well as obtaining trustworthiness evidences for a composite system before the actual deployment. Thereby it supports the designer in analyzing the end-to-end trustworthiness. An application example illustrates the application of the framework.

This study investigates if there is an age-related positivity effect on how older adults in eastern cultures perform trustworthiness evaluations based on facial cues. Furthermore, it explores the potential method information processing affected by the positivity effect during trustworthiness evaluations. The results show that the level of trustworthiness older adults in China assign to faces is significantly higher than that of younger adults; older adults’ trustworthiness evaluations based on facial cues in the valid cueing condition is significantly higher than in the invalid cueing condition; and for younger adults, trustworthiness evaluation of untrustworthy faces in the valid cueing condition was significantly lower than in the invalid cueing condition. Overall, these results show that positive information processing in older adults is regulated by attention capacity, while negative information processing by young adults is regulated by attention capacity, which indicates that the age-related positivity effect in older adults is due to increased positive information processing.

A novel evidential reasoning based method for software trustworthiness evaluation under the uncertain and unreliable environment

Incompletely predictable vehicular ad hoc networks (IPNs) are presented for vehicular networks with vehicles moving in particular ranges. A group of vehicles in the same region are required to encrypt and sign the uploaded region information in IPNs. So far, research has not been able to provide an appropriate trustworthiness evaluation system ensuring security and fairness in IPNs. In this paper, we first establish an evaluation system for vehicles in the network for secure and fair trustworthiness evaluation. Various vehicle attributes are utilized to evaluate the trustworthiness level of a vehicle. The novel trustworthiness evaluation can provide a more comprehensive and real-time update of vehicle status. Second, based on the system, we propose a cloud-aided trustworthiness evaluation (CATE) scheme. According to the evaluation results provided by the system, the CATE scheme also guarantees lightweight trustworthiness level confirmation with the help of session key generation. Besides, the information upload security is ensured with group signature and encryption. The security proof indicates that the proposed CATE scheme is secure for vehicles in IPNs to interact with trustworthiness. According to quantitative comparative analysis and experimental simulation based on paring-based cryptography (PBC) library, the designed scheme has been proved to be efficient.

The influence of visual context on the evaluation of facial trustworthiness

Cloud computing is the buzz word where the likes of servers, storage, applications etc., are provided as service to organizations and individual users. The on-demand delivery of service, on a pay-as-you-go model, showcases the flexibility of this service benefiting users to cut down on the capital expenditure. Resource management, governed by optimal resource allocation, becomes an essential ingredient for on-demand resourcing. In an era still marred by skepticism in cloud adoption, the focus on `Trustworthiness of cloud' gains importance for resource management. While current approaches provide trustworthiness based on brokers and consumers, trustworthiness - for the ability to complete submitted task with optimal resource allocation is critical. Hence, we propose technique of resource and reputation monitoring for `trustworthiness evaluation', by using self-assessment based scheme. This scheme goes a step further in ensuring optimal resource allocation and workload balancing, by maintaining in the warm spot. The blend of trustworthiness and optimal resource allocation provides the advantage of best service at reduced cost ensuring high success rate for resource providers, thereby benefiting users and resource providers. These statements have been validated through experimental methods for efficient trustworthy resource allocation.

Managing Trustworthiness in Component-based Embedded Systems

The use of Open Source Components (OSCs) is rapidly increasing in software systems. One of the biggest obstacles for the use of OSCs is the fact that the software integrators can not be sure of the quality of the components. Thus, it is the responsibility of the integrators to perform the quality evaluation. When building products using OSCs, the integrators should have a clear method and tools that facilitate and automate the evaluation work. Our contribution is a method and tooling for trustworthiness evaluation and testing of OSCs. The method provides clear guidelines to assist the integrators to perform the evaluation in their own software development environment. The tooling assists in the technical trustworthiness evaluation, containing tools for reliability analysis and testing. The tools are independent, co-operating in the open source context.

Accurate assessment of trustworthiness is fundamental to successful and adaptive social behavior. Initially, people assess trustworthiness from facial appearance alone. These assessments then inform critical approach or avoid decisions. Individuals with Williams syndrome (WS) exhibit a heightened social drive, especially toward strangers. This study investigated the temporal dynamics of facial trustworthiness evaluation in neurotypic adults (TD) and individuals with WS. We examined whether differences in neural activity during trustworthiness evaluation may explain increased approach motivation in WS compared to TD individuals. Event-related potentials were recorded while participants appraised faces previously rated as trustworthy or untrustworthy. TD participants showed increased sensitivity to untrustworthy faces within the first 65–90 ms, indexed by the negative-going rise of the P1 onset (oP1). The amplitude of the oP1 difference to untrustworthy minus trustworthy faces was correlated with lower approachability scores. In contrast, participants with WS showed increased N170 amplitudes to trustworthy faces. The N170 difference to low–high-trust faces was correlated with low approachability in TD and high approachability in WS. The findings suggest that hypersociability associated with WS may arise from abnormalities in the timing and organization of early visual brain activity during trustworthiness evaluation. More generally, the study provides support for the hypothesis that impairments in low-level perceptual processes can have a cascading effect on social cognition.

People evaluate a stranger's trustworthiness from their facial features in a fraction of a second, despite common advice "not to judge a book by its cover." Evaluations of trustworthiness have critical and widespread social impact, predicting financial lending, mate selection, and even criminal justice outcomes. Consequently, understanding how people perceive trustworthiness from faces has been a major focus of scientific inquiry, and detailed models explain how consensus impressions of trustworthiness are driven by facial attributes. However, facial impression models do not consider variation between observers. Here, we develop a sensitive test of trustworthiness evaluation and use it to document substantial, stable individual differences in trustworthiness impressions. Via a twin study, we show that these individual differences are largely shaped by variation in personal experience, rather than genes or shared environments. Finally, using multivariate twin modeling, we show that variation in trustworthiness evaluation is specific, dissociating from other key facial evaluations of dominance and attractiveness. Our finding that variation in facial trustworthiness evaluation is driven mostly by personal experience represents a rare example of a core social perceptual capacity being predominantly shaped by a person's unique environment. Notably, it stands in sharp contrast to variation in facial recognition ability, which is driven mostly by genes. Our study provides insights into the development of the social brain, offers a different perspective on disagreement in trust in wider society, and motivates new research into the origins and potential malleability of face evaluation, a critical aspect of human social cognition.

With the advanced development of the intelligent transportation system, vehicular ad hoc networks have been observed as an excellent technology for the development of intelligent traffic management in smart cities. Recently, researchers and industries have paid great attention to the smart road-tolling system. However, it is still a challenging task to ensure geographical location privacy of vehicles and prevent improper behavior of drivers at the same time. In this paper, a reliable road-tolling system with trustworthiness evaluation is proposed, which guarantees that vehicle location privacy is secure and prevents malicious vehicles from tolling violations at the same time. Vehicle route privacy information is encrypted and uploaded to nearby roadside units, which then forward it to the traffic control center for tolling. The traffic control center can compare data collected by roadside units and video surveillance cameras to analyze whether malicious vehicles have behaved incorrectly. Moreover, a trustworthiness evaluation is applied to comprehensively evaluate the multiple attributes of the vehicle to prevent improper behavior. Finally, security analysis and experimental simulation results show that the proposed scheme has better robustness compared with existing approaches.

Trustworthiness evaluation and retrieval-based revision method for case-based reasoning classifiers

<p indent="0mm">It is common for within-dimensional assimilation effect in the social evaluation of faces under social context. Recent studies have also found a cross-dimensional assimilation effect in the facial trustworthiness evaluation with the socioeconomic status (SES) background information as social context. However, whether the SES of evaluators moderates this cross-dimensional assimilation effect remains unclear, since the relationship between evaluators and evaluatees has largely been ignored in studies of the assimilation effect. Therefore, the present study aimed to investigate whether the SES of evaluators moderates the impact of the SES background information for evaluated faces on facial trustworthiness evaluation, and thereby to explore the broader theoretical issue on the interaction between evaluator and evaluatee on assimilation effect. There were two phases in the experiment. In the first phase, participants completed a status-color association training task, to learn how to associate colored backgrounds (purple/blue) with different levels of SES (high-SES/low-SES). This was to enable the participants to accurately perceive the SES information of the evaluated faces based on the background color in the subsequent evaluative task. In the second phase, they completed a facial trustworthiness rating task that used a 100-point scale to evaluate the trustworthiness of the faces displayed on colored backgrounds indicating high- or low-SES (<italic>T</italic>_high-SES/<italic>T</italic>_low-SES). After that, the facial trustworthiness baseline (<italic>T</italic>₀) was also measured by presenting participants the isolated faces without the colored backgrounds. The cross-dimensional assimilation effect of SES information of evaluated faces on facial trustworthiness (<italic>T</italic>_change) was calculated as follows: <italic>T</italic>_change=<italic>T</italic>_high-SES–<italic>T</italic>₀ or <italic>T</italic>₀–<italic>T</italic>_low-SES. One-sample <italic>t</italic>-test analysis revealed that the presence of high/low SES background information for the evaluated faces led to the evaluators’ trustworthiness evaluation being closer to the faces’ SES background information. That is, both the high and low SES information for the evaluated faces led to a significant assimilation effect on facial trustworthiness evaluation. But the assimilation of low SES information among low-SES participants is non-significant. Furthermore, a 2 (SES background information for evaluated faces: High vs. low) × 2 (SES of evaluators: High vs. low) repeated-measures ANOVA of the <italic>T</italic>_change showed a significant interaction of evaluators and evaluatees’ SES. Simple effect analysis revealed that the assimilation of low-SES information on facial trustworthiness evaluation among low-SES participants is significantly smaller than that among high-SES participants, which was also supported by the item-based ANOVA analysis. In summary, the results of the experiment suggested: (1) Both high-SES and low-SES information for evaluated faces exerted an assimilation effect on facial trustworthiness evaluation; (2) the assimilation effect is moderated by the SES of evaluators with an asymmetry of the in-group favoritism among high-SES evaluators and low-SES evaluators. The present study also contributed to the literature on the assimilation effect, by showing that: (1) Assimilation effect can occur not only between context and evaluative targets in the within dimension but also in cross-dimensions; (2) the social relationship between evaluator and evaluatee should be taken into account when evaluating the assimilation effect of social context.

Social Internet of Things (SIoT) integrates the social concept into the Internet of Things (IoT) for enhancing the efficiency of services. Due to the increasing scale of the network and the intellectualization of IoT objects, the development of SIoT has also been accompanied by the issue of trust management which is crucial to the security and reliability of the system. The definition and evaluation of the trustworthiness of objects are vital to build cooperation among objects. In this paper, we employ the blockchain technology to address the trust management issue in SIoT. Blockchain will be adopted as an underlying support technology for trust-related data storage and retrieval. Moreover, we present a flexible and adaptive trust computation method to enhance the reliability of objects’ trustworthiness evaluation. The simulated experiment results demonstrate that the proposed blockchain-based method enhances the effectiveness and security of trust management in SIoT.

Combining QoS prediction and customer satisfaction estimation to solve cloud service trustworthiness evaluation problems