In the Internet of Things vision, every physical object has a virtual component that can produce and consume services. Such extreme interconnection will bring unprecedented convenience and economy, but it will also require novel approaches to ensure its safe and ethical use. In the Internet of Things (IoT), everything real becomes virtual, which means that each person and thing has a locatable, addressable, and readable counterpart on the Internet. These virtual entities can produce and consume services and collaborate toward a common goal. The user's phone knows about his physical and mental state through a network of devices that surround his body, so it can act on his behalf. The embedded system in a swimming pool can share its state with other virtual entities. With these characteristics, the IoT promises to extend "anywhere, anyhow, anytime" computing to "anything, anyone, any service." Several significant obstacles remain to fulfill the IoT vision, among them security. The Internet and its users are already under continual attack, and a growing economy-replete with business models that undermine the Internet's ethical use-is fully focused on exploiting the current version's foundational weaknesses. This does not bode well for the IoT, which incorporates many constrained devices. Indeed, realizing the IoT vision is likely to spark novel and ingenious malicious models. The challenge is to prevent the growth of such models or at least to mitigate and limit their impact. Meeting this challenge requires understanding the characteristics of things and the technologies that empower the IoT. Mobile applications are already intensifying users' interaction with the environment, and researchers have made considerable progress in developing sensory devices to provide myriad dimensions of information to enrich the user experience. However, without strong security foundations, attacks and malfunctions in the IoT will outweigh any of its benefits. Traditional protection mechanisms-lightweight cryptography, secure protocols, and privacy assurance-are not enough. Rather, researchers must discover the full extent of specific obstacles. They must analyze current security protocols and mechanisms and decide if such approaches are worth integrating into the IoT as is or if adaptations or entirely new designs will better accomplish security goals. The proper legal and technical framework is essential. To establish it, analysts must thoroughly understand the risks associated with various IoT scenarios, such as air travel, which has many interrelated elements, including safety, privacy, and economy [1]. Only then is it possible to justify the cost of developing security and privacy mechanisms. All these requirements underline some critical first steps in implementing IoT security measures successfully: understand the IoT conceptually, evaluate Internet security's current state, and explore how to move from solutions that meet current requirements and constraints to those that can reasonably assure a secure IoT.
a b s t r a c t RFID technology meets identification and tracking requirements in healthcare environments with potential to speed up and increase reliability of involved processes. Due to this, high expectations for this integration have emerged, but hospital and medical centers interested in adoption of RFID technology require prior knowledge on how to squeeze RFID capabilities, real expectations and current challenges. In this paper, we show our lab tested solutions in two specific healthcare scenarios. On the one hand, we analyze the case of a medical equipment tracking system for healthcare facilities enabling both real-time location and theft prevention. Worth-noting aspects such as possible EMI interferences, technology selection and management of RFID data from hospital information system are analyzed. Lab testing of system reliability based on passive UHF RFID is provided for this case. On the other hand, we analyze and provide a solution for care and control of patients in a hospital based on passive HF RFID with the result of a fully functional demonstrator. Our prototype squeezes RFID features in order to provide a backup data source from patient's wristband. It also provides an offline working mode aiming to increase application reliability under network fail down and therefore, improving patient's safety. Considerations regarding lessons learned and challenges faced are exposed.
Cognitive diagnosis models (CDMs) are latent class multidimensional statistical models that help classify people accurately by using a set of discrete latent variables, commonly referred to as attributes. These models require a Q-matrix that indicates the attributes involved in each item. A potential problem is that the Q-matrix construction process, typically performed by domain experts, is subjective in nature. This might lead to the existence of Q-matrix misspecifications that can lead to inaccurate classifications. For this reason, several empirical Q-matrix validation methods have been developed in the recent years. de la Torre and Chiu proposed one of the most popular methods, based on a discrimination index. However, some questions related to the usefulness of the method with empirical data remained open due the restricted number of conditions examined, and the use of a unique cutoff point ( EPS) regardless of the data conditions. This article includes two simulation studies to test this validation method under a wider range of conditions, with the purpose of providing it with a higher generalization, and to empirically determine the most suitable EPS considering the data conditions. Results show a good overall performance of the method, the relevance of the different studied factors, and that using a single indiscriminate EPS is not acceptable. Specific guidelines for selecting an appropriate EPS are provided in the discussion.
Cognitive diagnosis models (CDMs) allow classifying respondents into a set of discrete attribute profiles. The internal structure of the test is determined in a Q-matrix, whose correct specification is necessary to achieve an accurate attribute profile classification. Several empirical Q-matrix estimation and validation methods have been proposed with the aim of providing well-specified Q-matrices. However, these methods require the number of attributes to be set in advance. No systematic studies about CDMs dimensionality assessment have been conducted, which contrasts with the vast existing literature for the factor analysis framework. To address this gap, the present study evaluates the performance of several dimensionality assessment methods from the factor analysis literature in determining the number of attributes in the context of CDMs. The explored methods were parallel analysis, minimum average partial, very simple structure, DETECT, empirical Kaiser criterion, exploratory graph analysis, and a machine learning factor forest model. Additionally, a model comparison approach was considered, which consists in comparing the model-fit of empirically estimated Q-matrices. The performance of these methods was assessed by means of a comprehensive simulation study that included different generating number of attributes, item qualities, sample sizes, ratios of the number of items to attribute, correlations among the attributes, attributes thresholds, and generating CDM. Results showed that parallel analysis (with Pearson correlations and mean eigenvalue criterion), factor forest model, and model comparison (with AIC) are suitable alternatives to determine the number of attributes in CDM applications, with an overall percentage of correct estimates above 76% of the conditions. The accuracy increased to 97% when these three methods agreed on the number of attributes. In short, the present study supports the use of three methods in assessing the dimensionality of CDMs. This will allow to test the assumption of correct dimensionality present in the Q-matrix estimation and validation methods, as well as to gather evidence of validity to support the use of the scores obtained with these models. The findings of this study are illustrated using real data from an intelligence test to provide guidelines for assessing the dimensionality of CDM data in applied settings.
Decisions on how to calibrate an item bank might have major implications in the subsequent performance of the adaptive algorithms. One of these decisions is model selection, which can become problematic in the context of cognitive diagnosis computerized adaptive testing, given the wide range of models available. This article aims to determine whether model selection indices can be used to improve the performance of adaptive tests. Three factors were considered in a simulation study, that is, calibration sample size, Q-matrix complexity, and item bank length. Results based on the true item parameters, and general and single reduced model estimates were compared to those of the combination of appropriate models. The results indicate that fitting a single reduced model or a general model will not generally provide optimal results. Results based on the combination of models selected by the fit index were always closer to those obtained with the true item parameters. The implications for practical settings include an improvement in terms of classification accuracy and, consequently, testing time, and a more balanced use of the item bank. An R package was developed, named cdcatR, to facilitate adaptive applications in this context.
Past research suggests that the connection between values and people's behaviour may not be as straightforward and robust as has been claimed. We propose that a more holistic and discriminative view that acknowledges the influence of a specific combination of values on specific kinds of behaviour is needed. In the current project, we test two hypotheses regarding the transcendental‐change profile (TCP). First, that TCP is characterized by a combination of the readiness to engage in those challenges (instrumental) that can make the world a better place (terminal). Second, the centrality of the TCP facilitates performance of those prosocial actions that are perceived as stimulating and global. The results of five studies support the reliability and validity of this conceptualization of TCP (Studies 1 and 2), and show that when the prosocial initiative is perceived as either global (Study 3) or stimulating (Studies 4 and 5), the TCP is the strongest predictor of the willingness and commitment to engage in such prosocial action.
“Sexting” is generally defined as the exchange of sexual media content via the internet. However, research on this topic has underscored the need to seek greater consensus when considering different conceptual elements that make up this definition. The aim of this study was to develop and validate an instrument for measuring sexting among adolescents, in order to cover a gap identified in the previous literature. The Adolescent Sexting Scale (A-SextS for short) was developed for validation on a sample of 579 Spanish secondary school pupils between the ages of 11 and 18. Evidence for face, content, concurrent, and criterion validity were assessed. A comprehensive set of 64 items, covering six defining characteristics of sexting (e.g., actions, recipient, media format, degree of sexual explicitness), was constructed after conducting an extensive literature review, two discussion groups, and a pilot study. Sexting prevalence rates measured by A-SextS were mostly concurrent with those found in previous studies. A-SextS subscales produced statistically significant positive associations with pornography consumption and physical sexual intercourse. The study shows that A-SextS can be an integrating instrument that facilitates a rigorous and comprehensive assessment of adolescent sexting experiences, as well as the formulation of an operationalized definition of the practice of sexting.
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