Miracle-dz/UniFAR-FTUs huggingface dataset:
Promoting Healthy and Effective Relationships Among School-Aged Children and Youth

R esearchers, practitioners, policymakers, and the nation at large are united in concern for preparing children and adolescents to be positive, functioning members of society. Although academic and technical skills have received much attention as prerequisites for success in the adult world, the importance of social and emotional competence as related to success across the lifespan and multiple life contexts is receiving increased attention. A range of social and emotional competencies are associated with positive academic, school, and work outcomes and are directly related to the development of healthy and effective relationships. Whereas healthy and effective relationships contribute to adaptive outcomes, unhealthy relationships undermine psychological well-being and academic competence (Kenny & WalshBlair, 2012). Incidents of bullying and school violence have heightened public awareness of the negative effects of poor social and emotional competence. From a strengths-based perspective, the development of social and emotional competencies among youth is a foundation for healthy social, family, work, community, and civic relationships across the lifespan. Integral to the attainment of healthy and effective relationships with peers, teachers, coworkers, and family members is the ability to process and engage in social interactions in a skilled and effective way; this is often referred to as social competence. A significant body of research has been conducted on social relationships and social skills, with the definition of these constructs varying based on the research questions and the theoretical orientation of the researchers. Information processing theorists (e.g., Dodge, Pettit, McClaskey, Brown, & Gottman, 1986) focus on the cognitive mechanisms that underlie competent behaviors. The individual receives some form of social information, interprets the information, and subsequently produces a response. Competence results when the individual’s interpersonal goals are met C H A P T E R 4
Study of Clinical Profile of Asthma among Pediatric Patients Attending Department of Pediatrics of a Tertiary Care Hospital , Kakinada

1&2 Professor, Department of Pediatrics, Government General Hospital, Rangaraya Medical College, Kakinada, Andhra Pradesh, India 3 Assistant Professor, Department of Pediatrics, Government General Hospital, Rangaraya Medical College, Kakinada, Andhra Pradesh, India 4 Professor and Head , Department of Pediatrics, Government General Hospital , Rangaraya Medical college, Kakinada, Andhra Pradesh, India 5 Junior Resident, Department of Pediatrics, Government General Hospital, Rangaraya Medical College, Kakinada, Andhra Pradesh, India
“We Make a Great Team!”: Adults with Low Prior Domain Knowledge Learn more from a Peer Robot than a Tutor Robot

In peer tutoring, the learner is taught by a colleague rather than by a traditional tutor. This strategy has been shown to be effective in human tutoring, where students have higher learning gains when taught by a peer instead of a traditional tutor. Similar results have been shown in child-robot interactions studies, where a peer robot was more effective than a tutor robot at teaching children. In this work, we compare skill increase and perception of a peer robot to a tutor robot when teaching adults. We designed a system in which a robot provides personalized help to adults in electronic circuit construction. We compare the number of learned skills and preferences of a peer robot to a tutor robot. Participants in both conditions improved their circuit skills after interacting with the robot. There were no significant differences in number of skills learned between conditions. However, participants with low prior domain knowledge learned significantly more with a peer robot than a tutor robot. Furthermore, the peer robot was perceived as friendlier, more social, smarter, and more respectful than the tutor robot, regardless of initial skill level.
Application Of Homotopy Perturbation Transform Method To Linear And Non-linear Space-time Fractional Reaction-diffusion Equations

In this paper, we obtain the analytical solutions of linear and non-linear space-time fractional reaction-diffusion equations on a finite domain by the application of homotopy perturbation transform method (HPTM). The HPTM is a combined form of the Laplace transform method with the homotopy perturbation method. Some examples are also given. Numerical results show that the HPTM is easy to implement and accurate when applied to linear and non-linear space-time fractional reaction-diffusion equations.
User-to-User Matching Services through Prediction of Mutual Satisfaction Based on Deep Neural Network

With the development of the sharing economy, existing recommender services are changing from user–item recommendations to user–user recommendations. The most important consideration is that all users should have the best possible satisfaction. To achieve this outcome, the matching service adds information between users and items necessary for the existing recommender service and information between users, so higher-level data mining is required. To this end, this paper proposes a user-to-user matching service (UTU-MS) employing the prediction of mutual satisfaction based on learning. Users were divided into consumers and suppliers, and the properties considered for recommendations were set by filtering and weighting. Based on this process, we implemented a convolutional neural network (CNN)–deep neural network (DNN)-based model that can predict each supplier’s satisfaction from the consumer perspective and each consumer’s satisfaction from the supplier perspective. After deriving the final mutual satisfaction using the predicted satisfaction, a top recommendation list is recommended to all users. The proposed model was applied to match guests with hosts using Airbnb data, which is a representative sharing economy platform. The proposed model is meaningful in that it has been optimized for the sharing economy and recommendations that reflect user-specific priorities.
Practical, Ethical, and Overlooked: Teleoperated Socially Assistive Robots in the Quest for Autonomy

Socially Assistive Robots (SARs) show significant promise in a number of domains: providing support for the elderly, assisting in education, and aiding in therapy. Perhaps unsurprisingly, SAR research has traditionally focused on providing evidence for this potential. In this paper, we argue that this focus has led to a lack of critical reflection on the appropriate level of autonomy (LoA) for SARs, which has in turn led to blind spots in the research literature. Through an analysis of the past five years of HRI literature, we demonstrate that SAR researchers are overwhelmingly developing and envisioning autonomous robots. Critically, researchers do not include a rationale for their choice in LoA, making it difficult to determine their motivation for fully autonomous robots. We argue that defaulting to research fully autonomous robots is potentially short-sighted, as applying LoA selection guidelines to many SAR domains would seem to warrant levels of autonomy that are closer to teleoperation. We moreover argue that this is an especially critical oversight as teleoperated robots warrant different evaluation metrics than do autonomous robots since teleoperated robots introduce an additional user, the teleoperator. Taken together, this suggests a mismatch between LoA selection guidelines and the vision of SAR autonomy found in the literature. Based on this mismatch, we argue that the next five years of SAR research should be characterized by a shift in focus towards teleoperation and teleoperators.
Personalized Meta-Learning for Domain Agnostic Learning from Demonstration

For robots to perform novel tasks in the real-world, they must be capable of learning from heterogeneous, non-expert human teachers across various domains. Yet, novice human teachers often provide suboptimal demonstrations, making it difficult for robots to successfully learn. Therefore, to effectively learn from humans, we must develop learning methods that can account for teacher suboptimality and can do so across various robotic platforms. To this end, we introduce Mutual Information Driven Meta-Learning from Demonstration (MIND MELD) [12], [13], a personalized meta-learning framework which meta-learns a mapping from suboptimal human feedback to feedback closer to optimal, conditioned on a learned personalized embedding. In a human subjects study, we demonstrate MIND MELD's ability to improve upon suboptimal demonstrations and learn meaningful, personalized embeddings. We then propose Domain Agnostic MIND MELD, which learns to transfer the personalized embedding learned in one domain to a novel domain, thereby allowing robots to learn from suboptimal humans across disparate platforms (e.g., self-driving car or in-home robot).
Exploring Discourse Corpora Using Process Mining Techniques

In our paper we will introduce and discuss Process Mining (PM) as a means for conducting conversational analytic, linguistic and rhetorical investigations into discourse processes. PM is a technique for automatically deriving and further analyzing process models from event data. It is mainly applied for the analysis of Business Processes. We will argue that conversations and other kinds of discourse can be treated as processes too, and that PM enables us to systematically investigate large quantities of discourse transcripts. It would be possible to examine many different linguistic research questions, such as examining the conditions of turn-taking in dialogue or the pragmasemantics of discourse particles like English “well” or German “halt”, among others. 1
Service Recommendations with Deep Learning: A Study on Neural Collaborative Engines

Background: The present paper aims to investigate the adoption of Neural Networks for recommendation systems and to propose Deep Learning architectures as advanced frameworks for designing Collaborative Filtering engines. Recommendation systems are data-driven infrastructures which are widely adopted to create effective and cutting-edge smart services, allowing to personalize the value proposition and adapt it to changes and variations in customers’ preferences. Method: Our research represents an exploratory investigation on the adoption of Neural Networks for Recommendation Systems, inspired by the findings of a recent study on service science that highlighted the suitability of those models for designing cutting-edge recommenders capable of overcoming stable traditional benchmarks like the Singular Value Decomposition and the k-Nearest Neighbors algorithms. Following this study, we designed a more “complex” Feed-Forward Neural Network, trained on the “Movielens 100K” dataset using the Mean-Squared Error function to approximate the model loss generated and the Adaptive Moment Estimation algorithm (Adam) for the parameters optimization. Results: The results of this study demonstrate the primary role of Feed-Forward Neural Networks for designing advanced Collaborative recommenders, consolidating and even improving the outcomes of the work that inspired our research. Conclusion: Given these assumptions, we confirm the suitability of Feed-Forward Neural Networks as effective recommendation algorithms, laying the foundations for further studies in neural-based recommendation science.
Two-way Human-Robot Interaction in 5G Tele-operation

The advancement in 5G technology has emerged as an enabler across a wide range of industries advancement in recent years, especially for those requiring low tolerance in latency for high-speed data transmission. With a primary focus on Human-Robot Interaction (HRI), this paper proposes an intuitive and user-friendly design on mobile robot tele-operation system to exploit 5G's advantages. Unlike traditional remote control with limited signal range and single-direction command control, our design demonstrates an interactive approach for 5G tele-operation, empowering an active and instant two-way interaction between pilots and robots with the aids of control, visual, audible, and haptic cues, to minimize incident rate.
Interdisciplinary Explorations of Processes of Mutual Understanding in Interaction with Assistive Shopping Robots

The main goal of this workshop is to establish awareness of the emerging field of socially assistive shopping robots in human-robot interaction (HRI) and, simultaneously, to foster interdisciplinary approaches that combine the development of social robots with a sequential and embodied perspective regarding mutual processes of understanding in shopping interactions with assistive robots.
On Human-Aware Information Seeking

Large parts of scientific work relies on seeking for information in very large datasets and respective metadata (e.g., document repositories on the web, databases, local image collections). Based on a search string or even sample data as a query, information retrieval systems (IR systems) return lists of ranked items that match the query, together with a short preview of the item. Using search strings or example data, it is not easy to express certain information needs, however. In this extended abstract we discuss in what way the interaction of a user with an information retrieval (IR) system can optimized with human-aware collaborative planning strategies.
DESIGN AND EVALUATION OF VISUAL INTERPENETRATION CUES IN VIRTUAL GRASPING and appeared slightly preferred over discrete change

We present design and impact studies of visual feedback for virtual grasping. The studies suggest new or updated guidelines for feedback. Recent grasping techniques incorporate visual cues to help resolve undesirable visual or performance artifacts encountered after real fingers enter a virtual object. Prior guidelines about such visuals are based largely on other interaction types and provide inconsistent and potentially-misleading information when applied to grasping. We address this with a two-stage study. In the first stage, users adjusted parameters of various feedback types, including some novel aspects, to identify promising settings and to give insight into preferences regarding the parameters. In the next stage, the tuned feedback techniques were evaluated in terms of objective performance (finger penetration, release time, and precision) and subjective rankings (visual quality, perceived behavior impact, and overall preference). Additionally, subjects commented on the techniques while reviewing them in a final session. Performancewise, the most promising techniques directly reveal penetrating hand configuration in some way. Subjectively, subjects appreciated visual cues about interpenetration or grasp force, and color changes are most promising. The results enable selection of the best cues based on understanding the relevant tradeoffs and reasonable parameter values. The results also provide a needed basis for more focused studies of specific visual cues and for choosing conditions in comparisons to other feedback modes, such as haptic, audio, or multimodal. Considering results, we propose that 3D interaction guidelines must be updated to capture the importance of interpenetration cues, possible performance benefits of direct representations, and tradeoffs involved in cue selection.
Themselves Must Strike the Blow: The Socialist Argument for Strikes and Self-Emancipation

ABSTRACT:Socialists know that they ought to defend strikes, but why? The best argument is that strikes are acts of self-emancipation. The ideal of self-emancipation lies at the heart of socialist political theory. It is up to workers to emancipate themselves, not just because it takes class power to overthrow capitalism, but because there is an intrinsic connection between class struggle and socialist freedom. Workers can only possess and exercise the freedoms they are denied, but ought to enjoy, if they demand that freedom for themselves, through their own, collective activity. Strikes are an essential way of both winning and exercising those denied freedoms. They are therefore a path to, and partial realization of, the ideal of self-emancipation to which socialists are, or ought to be, committed.
Environmental Efficiency of Photovoltaic Power Plants in China—A Comparative Study of Different Economic Zones and Plant Types

In this paper we study and compare the environmental efficiency of 118 photovoltaic (PV) plants in China. Drawing on the nonparametric data envelopment analysis (DEA) method, our study takes the initiative to take the insolation, annual sunshine duration, and covering area as input variables into account, as well as the installed capacity, annual electricity generation, CO2 emission reduction, and coal saving as output variables, to provide a unified measure of environmental efficiency of PV plants in China. We find widespread inefficiencies in roughly 95% of the PV plants, and the performance of different economic zones and types of PV plants are quite different. Specifically, those PV plants in eastern China are the least satisfying performers among three different economic zones. The surprising result indicates that eastern China has room for improvement by overcoming the inefficiencies caused by serious aerosol pollution and the high urbanization rate. We also find rooftop PV plants have the highest efficiencies among the four types of PV plants due to very little power loss. However, complementary PV plants have the lowest efficiencies most likely because of high operating temperatures during the process of power generation.
Is anything wrong with higher education in Latvia?

This paper argues that the answer is yes. It builds its case on the basis of two findings. First, using a Survey of Innovative Businesses in Latvia (SIBiL), it finds that higher education received after the 1990 is not as effective as Soviet education in promoting innovativeness, as measured by both firm-level product innovations and patent applications. Second, it shows that, as measured by the number of English language articles in ISI Web of Science database, Latvian scientists seriously underperform, as compared to their colleagues in Sweden, Finland, Estonia, and Lithuania, especially in the field of social sciences. Based on these findings, this paper argues for sweeping reforms to the system of higher education in Latvia, focusing on subjecting the faculty of Latvian universities to the test of international peer review process. This requires measures to strengthen the role of English language and a reform of public financing of the system of higher education in Latvia.
Responsible investment in Germany, Austria and Switzerland

Sustainable and Responsible Investments (SRI) are booming in the US and Europe. In German-speaking countries, Switzerland is a leading force for SRI with an overall 2010 market volume of about US$45 billion, Germany’s SRI market covers middle ground with US$21 billion, while Austria’s is relatively small with US$3 billion. In this paper, we give a timely review on German-speaking countries’ attributes, responsible investment, and its legislation, whilst analyzing the financial performance and characteristics of socially responsible investments in the respective country’s stock markets. We focus on passive equity investments to obtain a most undistorted view on the performance and style of SRI strategies. The paper investigates two socially responsible investment strategies which are present in German-speaking countries: SRI in general and green investing in particular. First, both strategies do not have a performance which is different from conventional benchmarks controlling for well known characteristics. This inference is robust to five alternative asset pricing models using unconditional and conditional calendar-time factor regressions. Second, both socially responsible investment strategies have a higher systematic risk than their benchmark. Third, characteristics even beyond a size tilt are important in explaining responsible investment indices’ performance attribution like a momentum or an investment anomaly (both with negative coefficients).
Near-field studies of surface plasmon generation: optical and terahertz studies

We study the dielectric constant dependent diffraction phenomena of single slit apertures, both theoretically and experimentally. We experimentally simulate perfect metal and real metal cases by investigating subwavelength diffraction by a single slit, both in nano-optical and in terahertz regimes, keeping the slit-width/wavelength ratio approximately the same for both of frequency regimes. The wave-front in optical regime separates itself into forward propagating beam and surface-bound 90-degree diffracted wave, i.e., surface plasmon polaritons; while the separation of modes is not observed in terahertz regime.
Visualization of Pursuit Differential Game on a Plane

This paper is dedicated to differential pursuit games. In the theory of dynamic games, a number of methods have been developed that provide a guaranteed result. The scheme of the Method of Resolving Functions is applied in the work. Sufficient conditions to end of the game have been found. For the task of simple pursuit, the visualization of the trajectory of the movements of the group of pursuers and the fugitive on the plane is realized. To do this, a software product was created in the Python programming language. This software product is a prototype of a "pursuer-evader" simulation system that can be used to select controls in pursuit tasks. Two cases of fugitive control selection were considered in the development. In the first one, the control of the fugitive is based on the following algorithm: the fugitive determines the nearest pursuer in terms of the Euclidean norm; the fugitive builds his control on the beam, which comes from the position of the nearest pursuer and crosses the position of the fugitive, in the direction opposite to the pursuer with maximum speed. In the second case, the control of the fugitive is set by the user. The visualization of the trajectory of one pursuer and one evader on the plane was realized for the Pontryagin’s checking example of a game problem with simple motions. The results have a graphical presentation. The result showed the coincidence of the estimated time and the actual end time of the game.
A mandibular bone density index for prediction of jaw bone osteoporosis in men

Reduction in mandibular bone mineral density can be measure using panoramic radiography; however, this method has limited applicability in remote areas in Indonesia. Therefore, an urgent need exists for the development of a simple screening tool for predicting mandibular bone density. The objective of this study was to develop an index, consisting of osteoporosis risk factors, for predicting mandibular bone density in men. This study employed a case-control design, and included 89 elderly men (age>60 years) with mandibular bone osteoporosis as cases and 87 elderly men with normal mandibular bone as controls. Several risk factors for osteoporosis were analyzed. In this study, we used panoramic radiography to measure the decrease in mandibular density. The primary risk factors for decreased mandibular bone density in men were testosterone level(p=0.00),calcium intake (p=0.00),age(p=0.01), physical activity (p=0.0),and multivitamin intake (p=0.01). The index for predicting mandibular bone osteoporosis comprised these significant risk factors as well as some risk factors that were important (p < 0.25) but not statistically significant (p>0.05), such as history of fractured bones and food texture. In this study, we developed an index for predicting mandibular bone density in elderly men that can be used as a simple and easy screening tool for predicting osteoporosis. Clinical article (J Int Dent Med Res 2017; 10(Special Issue: pp. 612-618)