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This article discusses the need for artificial intelligence (AI) practitioners to shift their focus from a purely technical mindset toward a more human-centered approach. Technical experts are trained to consider the technical aspects of their work, which can cause them to overlook important socio–technical considerations and implications, resulting in a socio–technical gap in AI-based systems. Unhelpful practitioner cultures can lead to them “rejecting practices or downplaying the importance of values or the possible threats of ignoring them”. While efforts are being made to create ethical and more human-centered AI systems, there is a need for corresponding changes in the attitudes and perceptions of AI practitioners. Practitioners need to move away from a sole focus on compliance with responsible AI guidelines and regulations toward active reflection and empathy based on a true understanding of the profound effects their decisions can have on different stakeholders. However, one problematic barrier to beginning work on interventions that target practitioners’ mindsets and attitudes is the lack of a standardized method for evaluating or measuring the effectiveness of design interventions on their attitudes and perceptions. This article suggests the need for clearer metrics within the human–computer interaction (HCI) community for looking at practitioners’ attitudes toward socio–technical factors in AI design. [ABSTRACT FROM AUTHOR]

Digital technology influences behaviours, moods and wellbeing. The relationships are complex, but users are increasingly interested in finding how to balance a digital life with psychological wellbeing. We present an approach for investigating the relationship between lifestyle aspects and digital technology usage patterns that combines MindGauge, a mobile app enabling users collect and analyse their moods and behaviours, with a productivity tool (RescueTime). We then report a 16-month study in which we collected computer and smartphone usage and self-reports from 72 participants. We present methods for analysing the relationship between productivity, task-switching, mood and lifestyle, and more specifically how digital technology usage associates with productivity and task-switching. Our study also investigates how lifestyle aspects (sleep quality, physical activity, workload, social interaction and alcoholic drink consumption) relate to mood, task-switching and productivity. Results show that more frequent task-switching is associated with negative moods. A few lifestyle aspects, such as sleep quality and physical activity, had a significant relationship with positive moods. We also contribute a mood detection model that utilise both digital footprints and lifestyle contexts, yielding an accuracy of 87 percent. The study provides evidence that such methods can be used to understand the impact of technology on wellbeing. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic framework for the optimization of microgrids that has the functionality of providing flexibility services to System Operators (SOs) considering uncertainties in the energy forecast. The methodology is developed with the aim of being applied to complex microgrids composed of different distributed energy resources and hybrid energy storage systems (ESS). The associated optimization problem is operated in two stages: the first one performs a stochastic optimization of the microgrid in order to reserve an up/down regulation capacity with which to deal with the energy forecast uncertainties of the microgrid. The different microgrid devices are optimized by considering their operational costs in order to achieve their optimal operation in the Day-Ahead Market (DM). The second stage is used to re-schedule the initial planning according to the signal request and an economic offer from the SO. The control problem is developed using Stochastic Model Predictive Control (SMPC) techniques and Mixed-Integer Quadratic Programming (MIQP), owing to the presence of logic, integer, mixed and probabilistic variables. The simulation results show that the proposed methodology reduces the risk of undergoing up/down-penalty deviations in the Regulation Service Market (RM), also being able to provide flexibility services to the SOs, despite being subject to uncertainties in the energy forecast carried out for the microgrid. [ABSTRACT FROM AUTHOR]

Dimensional affect recognition is a challenging topic and current techniques do not yet provide the accuracy necessary for HCI applications. In this work we propose two new methods. The first is a novel self-organizing model that learns from similarity between features and affects. This method produces a graphical representation of the multidimensional data which may assist the expert analysis. The second method uses extreme learning machines, an emerging artificial neural network model. Aiming for minimum intrusiveness, we use only the heart rate variability, which can be recorded using a small set of sensors. The methods were validated with two datasets. The first is composed of 16 sessions with different participants and was used to evaluate the models in a classification task. The second one was the publicly available Remote Collaborative and Affective Interaction (RECOLA) dataset, which was used for dimensional affect estimation. The performance evaluation used the kappa score, unweighted average recall and the concordance correlation coefficient. The concordance coefficient on the RECOLA test partition was 0.421 in arousal and 0.321 in valence. Results show that our models outperform state-of-the-art models on the same data and provides new ways to analyze affective states. [ABSTRACT FROM AUTHOR]

We explored how computer vision techniques can be used to detect engagement while students (N = 22) completed a structured writing activity (draft-feedback-review) similar to activities encountered in educational settings. Students provided engagement annotations both concurrently during the writing activity and retrospectively from videos of their faces after the activity. We used computer vision techniques to extract three sets of features from videos, heart rate, Animation Units (from Microsoft Kinect Face Tracker), and local binary patterns in three orthogonal planes (LBP-TOP). These features were used in supervised learning for detection of concurrent and retrospective self-reported engagement. Area under the ROC Curve (AUC) was used to evaluate classifier accuracy using leave-several-students-out cross validation. We achieved an AUC = .758 for concurrent annotations and AUC = .733 for retrospective annotations. The Kinect Face Tracker features produced the best results among the individual channels, but the overall best results were found using a fusion of channels. [ABSTRACT FROM PUBLISHER]

The electricity sector is undergoing major changes from the point of view of management and market. One of the keys that are accelerating this change is the increasing penetration of Distributed Energy Resources (DERs). The complexity of the scenario envisaged in the near future requires that the equipment in the electrical network must have the capacity to interact in a much more dynamic way than in the current system, and where the connection interface should be provided with the necessary intelligence and communication capability to manage the whole system effectively. This paper describes the development of an Intelligent Electronic Device (IED) that can integrate into the current and future grid equipment. Specifications and considerations for their design as well as its multifunctional architecture are discussed. The developed prototype was subjected to a testing protocol Class A, according to IEC 61000-4-30 to check the monitoring parameters. Various tests for estimating the delays involved in the protection algorithms are presented also. Finally, a real test scenario is discussed, in which the prototype has been integrated into an inverter providing it with the capabilities required by a future Smart Grid context. [ABSTRACT FROM PUBLISHER]

Engagement is critical to the success of learning activities such as writing, and can be promoted with appropriate feedback. Current engagement measures rely mostly on data collected by observers or self-reported by the participants. In this paper, we describe a learning analytic system called Tracer, which derives behavioral engagement measures and creates visualizations of behavioral patterns of students writing on a cloud-based application. The tool records the intermediate stages of document development and uses this data to measure learners’ behavioral engagement and derive three visualizations. Writers ($N = 23$ University students) participated in a controlled one-hour writing session in which they post-facto self-reported their level of behavioral engagement. Results show that the level of behavioral engagement automatically estimated by the system correlates with the level reported by the participants. Additionally, users stated that the visualizations were coherent with their writing activity and were useful to help them reflect on the writing process. [ABSTRACT FROM PUBLISHER]

Unobtrusive, contactless recordings of physiological signals are very important for many health and human–computer interaction applications. Most current systems require sensors which intrusively touch the user's skin. Recent advances in contact-free physiological signals open the door to many new types of applications. This technology promises to measure heart rate (HR) and respiration using video only. The effectiveness of this technology, its limitations, and ways of overcoming them deserves particular attention. In this paper, we evaluate this technique for measuring HR in a controlled situation, in a naturalistic computer interaction session, and in an exercise situation. For comparison, HR was measured simultaneously using an electrocardiography device during all sessions. The results replicated the published results in controlled situations, but show that they cannot yet be considered as a valid measure of HR in naturalistic human–computer interaction. We propose a machine learning approach to improve the accuracy of HR detection in naturalistic measurements. The results demonstrate that the root mean squared error is reduced from 43.76 to 3.64 beats/min using the proposed method. [ABSTRACT FROM PUBLISHER]

Present distribution networks have not been designed to accept extensive Distributed Generation (DG). The present framework may change in the near future impelled by a worldwide increase in DG penetration in distribution grids. The mutual dependency of energy distribution and communications on the same grid requires new means for effectively monitoring and controlling both systems in parallel. Relaying and control IEDs can also serve as measurement units for power quality. Synchronized Measurement Technology (SMT) has the potential of becoming the backbone of this paradigm. By using wide-area information systems, the data can be made available to all involved partners. This paper provides different outlooks about the possibilities of Embedded Systems to integrate the protection, control and monitoring functions in Distribution Automation Systems. To justify the choice of synchronization technology, we have scheduled a complete sequence of synchronism tests and measurement tests based on standard specifications. [ABSTRACT FROM PUBLISHER]

Signals from peripheral physiology (e.g., ECG, EMG, and GSR) in conjunction with machine learning techniques can be used for the automatic detection of affective states. The affect detector can be user-independent, where it is expected to generalize to novel users, or user-dependent, where it is tailored to a specific user. Previous studies have reported some success in detecting affect from physiological signals, but much of the work has focused on induced affect or acted expressions instead of contextually constrained spontaneous expressions of affect. This study addresses these issues by developing and evaluating user-independent and user-dependent physiology-based detectors of nonbasic affective states (e.g., boredom, confusion, curiosity) that were trained and validated on naturalistic data collected during interactions between 27 students and AutoTutor, an intelligent tutoring system with conversational dialogues. There is also no consensus on which techniques (i.e., feature selection or classification methods) work best for this type of data. Therefore, this study also evaluates the efficacy of affect detection using a host of feature selection and classification techniques on three physiological signals (ECG, EMG, and GSR) and their combinations. Two feature selection methods and nine classifiers were applied to the problem of recognizing eight affective states (boredom, confusion, curiosity, delight, flow/-engagement, surprise, and neutral). The results indicated that the user-independent modeling approach was not feasible; however, a mean kappa score of 0.25 was obtained for user-dependent models that discriminated among the most frequent emotions. The results also indicated that k-nearest neighbor and Linear Bayes Normal Classifier (LBNC) classifiers yielded the best affect detection rates. Single channel ECG, EMG, and GSR and three-channel multimodal models were generally more diagnostic than two--channel models. [ABSTRACT FROM PUBLISHER]

Affect-aware technologies are moving the frontiers of how we understand, support, and optimize student learning. The authors explore five areas that exemplify cutting-edge research in the burgeoning field. These include intelligent tutoring systems that detect and respond to students' affective states and sometimes synthesize affect; the strategic induction of confusion as a means to stimulate deep learning; techniques to increase student engagement and reflection; systems that support the development of prosocial behaviors, resilience, and other aspects that contribute to students' well-being; and sample projects that highlight how these new ideas can be taken from laboratories into real-world classrooms. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach for semiautomatic question generation to support academic writing. Our system first extracts key phrases from students' literature review papers. Each key phrase is matched with a Wikipedia article and classified into one of five abstract concept categories: Research Field, Technology, System, Term, and Other. Using the content of the matched Wikipedia article, the system then constructs a conceptual graph structure representation for each key phrase and the questions are then generated based the structure. To evaluate the quality of the computer generated questions, we conducted a version of the Bystander Turing test, which involved 20 research students who had written literature reviews for an IT methods course. The pedagogical values of generated questions were evaluated using a semiautomated process. The results indicate that the students had difficulty distinguishing between computer-generated and supervisor-generated questions. Computer-generated questions were also rated as being as pedagogically useful as supervisor-generated questions, and more useful than generic questions. The findings also suggest that the computer-generated questions were more useful for the first-year students than for second or third-year students. [ABSTRACT FROM PUBLISHER]