Psychometrics in Practice at RCEC

A broad range of topics is dealt with in this volume: from combining the psychometric generalizability and item response theories to the ideas for an integrated formative use of data-driven decision making, assessment for learning and diagnostic testing. A number of chapters pay attention to computerized (adaptive) and classification testing. Other chapters treat the quality of testing in a general sense, but for topics like maintaining standards or the testing of writing ability, the quality of testing is dealt with more specifically.\ud All authors are connected to RCEC as researchers. They present one of their current research topics and provide some insight into the focus of RCEC. The selection of the topics and the editing intends that the book should be of special interest to educational researchers, psychometricians and practitioners in educational assessment

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Primate Amygdala Neurons Simulate Decision Processes of Social Partners.

By observing their social partners, primates learn about reward values of objects. Here, we show that monkeys' amygdala neurons derive object values from observation and use these values to simulate a partner monkey's decision process. While monkeys alternated making reward-based choices, amygdala neurons encoded object-specific values learned from observation. Dynamic activities converted these values to representations of the recorded monkey's own choices. Surprisingly, the same activity patterns unfolded spontaneously before partner's choices in separate neurons, as if these neurons simulated the partner's decision-making. These "simulation neurons" encoded signatures of mutual-inhibitory decision computation, including value comparisons and value-to-choice conversions, resulting in accurate predictions of partner's choices. Population decoding identified differential contributions of amygdala subnuclei. Biophysical modeling of amygdala circuits showed that simulation neurons emerge naturally from convergence between object-value neurons and self-other neurons. By simulating decision computations during observation, these neurons could allow primates to reconstruct their social partners' mental states

Ensemble classification and signal image processing for genus Gyrodactylus (Monogenea)

This thesis presents an investigation into Gyrodactylus species recognition, making use of machine learning classification and feature selection techniques, and explores image feature extraction to demonstrate proof of concept for an envisaged rapid, consistent and secure initial identification of pathogens by field workers and non-expert users. The design of the proposed cognitively inspired framework is able to provide confident discrimination recognition from its non-pathogenic congeners, which is sought in order to assist diagnostics during periods of a suspected outbreak. Accurate identification of pathogens is a key to their control in an aquaculture context and the monogenean worm genus Gyrodactylus provides an ideal test-bed for the selected techniques. In the proposed algorithm, the concept of classification using a single model is extended to include more than one model. In classifying multiple species of Gyrodactylus, experiments using 557 specimens of nine different species, two classifiers and three feature sets were performed. To combine these models, an ensemble based majority voting approach has been adopted. Experimental results with a database of Gyrodactylus species show the superior performance of the ensemble system. Comparison with single classification approaches indicates that the proposed framework produces a marked improvement in classification performance. The second contribution of this thesis is the exploration of image processing techniques. Active Shape Model (ASM) and Complex Network methods are applied to images of the attachment hooks of several species of Gyrodactylus to classify each species according to their true species type. ASM is used to provide landmark points to segment the contour of the image, while the Complex Network model is used to extract the information from the contour of an image. The current system aims to confidently classify species, which is notifiable pathogen of Atlantic salmon, to their true class with high degree of accuracy. Finally, some concluding remarks are made along with proposal for future work

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

Enhancing the use of Haptic Devices in Education and Entertainment

This research was part of the two-years Horizon 2020 European Project "weDRAW". The aim of the project was that "specific sensory systems have specific roles to learn specific concepts". This work explores the use of the haptic modality, stimulated by the means of force-feedback devices, to convey abstract concepts inside virtual reality. After a review of the current use of haptic devices in education, available haptic software and game engines, we focus on the implementation of an haptic plugin for game engines (HPGE, based on state of the art rendering library CHAI3D) and its evaluation in human perception experiments and multisensory integration

Perceptual binding of static and dynamic signals: a psychophysical and electrophysiological study on contour integration.

The present work investigates the mechanisms underpinning the integration of local signals (either local orientations, positions or directions) into whole configurations. The investigation is composed of three studies that try to disentangle the issue using a contour integration paradigm. Each of them focuses on a specific aspect of the problem. Study one compares two integration models: the first is the well known “association field model”, based on local lateral interactions between adjacent receptive fields tuned to similar orientation (in primary visual area V1). The second is a second-stage filter that follows rectification of first-order filters. Study two tests, instead, the idea that a local cooperative system is responsible for the integration of directional signals. In addition it investigates whether such a mechanism could explain the “motion facilitation effect” usually found with dynamic (compared to static contours). Finally, study three extends findings from study two recording Visual Evoked Potentials (VEPs) elicited by static and dynamic contours. My findings provide support to the idea that a mechanism based on local lateral interactions in V1 could account for the integration of static contours, whereas a local cooperative mechanism could account for the integration of static contours. Moreover, these two mechanisms interact, in a way that the cooperative motion system facilitates or impairs the input feeding the static association field

Expressive language development in minimally verbal autistic children: exploring the role of speech production

Trajectories of expressive language development are highly heterogeneous in autism. I examine the hypothesis that co-morbid speech production difficulties may be a contributing factor for some minimally verbal autistic individuals. Chapters 1 and 2 provide an overview of language variation within autism, and existing intervention approaches for minimally verbal autistic children. These chapters situate this thesis within the existing literature. Chapter 3 describes a longitudinal study of expressive language in minimally verbal 3-5 year olds (n=27), with four assessment points over 12 months. Contrary to expectations, initial communicative intent, parent responsiveness and response to joint attention did not predict expressive language growth or outcome. Speech skills were significant predictors. Chapter 4 describes the design, development and feasibility testing of the BabbleBooster app, a novel, parent-meditated speech skills intervention, in which 19 families participated for 16 weeks. Acceptability feedback was positive but adherence was variable. I discuss how this could be improved in future iterations of the app and intervention protocol. Chapter 5 details how BabbleBooster’s efficacy was evaluated. For interventions with complex or rare populations, a randomized case series design is a useful alternative to an under-powered group trial. There was no evidence that BabbleBooster improved speech production scores, likely due to limited dosage. Future research using this study design could determine optimal treatment intensity and duration with an improved version of the app. Taken together, these studies underscore the contribution of speech production abilities to expressive language development in minimally verbal autistic individuals. I argue that this reflects an additional condition, and is not a consequence of core autism features. The intervention piloted here represents a first step towards developing a scalable tool for parents to support speech development in minimally verbal children, and illustrates the utility of randomized single case series for testing treatment effects in small, heterogeneous cohorts

The Effect of Jyoti Meditation on Student Counselor Emotional Intelligence, Stress, and Daily Spiritual Experiences

Previous research has found meditation to be effective in reducing practitioner stress, improving emotional functioning, and increasing pro-social emotions, such as empathy and compassion. In addition, research examining the effects of meditation on student counselors has shown that it increases counselor self-efficacy, reduces distress, and increases cognitive empathy. Therefore, it behooves counselor educators to discover methods of integrating meditation into counselor training. The meditation practice investigated in the current study is new to the counseling and psychology literature. The majority of the current research has examined transcendental and mindfulness-based practices. However, recent research has shown that spirituality has the ability to potentiate meditation. Jyoti mediation (JM), the practice used in this study, is a spiritually based practice used for spiritual and personal growth for over 500 years. This study examined whether student counselors, after participating in a JM group, would have a significantly different level of emotional intelligence, stress and daily spiritual experiences than a comparison group who received a psycho-educational curriculum. Moreover, I investigated if the frequency of meditation related to the treatment outcomes. I conducted a six week randomized controlled trial where participants (n = 60) completed self-report assessments on the first, third and sixth week of the intervention. In addition, the participants in the meditation condition were asked to complete a daily journal reporting their experiences with the meditation treatment and their frequency of practice. Participants were required to meditate once a week in the group, and requested to meditate at least ten additional minutes each day. In order to analyze the data, I conducted a repeated measures multivariate analysis of variance (RM-MANOVA). The RM-MANOVA revealed no significant difference between the two groups. However, because the range of time spent meditating was so wide, I conducted a second RM-MANOVA using only participants that meditated in group and an additional 60 minutes over the six weeks. The second RM-MANOVA approached significance in the main effects (p = .06); and revealed a significant univariate between group effect for stress. Likewise, I conducted two Pearson moment correlations to investigate the relationship between the study outcomes and meditation frequency. The first correlation revealed no significant relationship between meditation frequency and any of the independent. However, the second correlational analysis revealed a significant relationship between stress and meditation frequency. Also, both correlational analyses revealed a significant relationship between stress and emotional intelligence. In order to gain a better understanding of how the independent variables effected stress over time, I conducted a growth curve analysis (GCA). I used PROC Mixed in SAS and nested the measurement points into each individual. The GCA revealed significant non-trivial variance between individuals at initial status. In addition, the GCA revealed that emotional intelligence accounted for 27% of that variance, and when controlling for emotional intelligence there is a significant interaction between time and group. The implications and limitations of these findings are discussed