Miracle-dz/UniFAR-FTUs huggingface dataset:
Embodied information behavior, mixed reality and big data

A renaissance in the development of virtual (VR), augmented (AR), and mixed reality (MR) technologies with a focus on consumer and industrial applications is underway. As data becomes ubiquitous in our lives, a need arises to revisit the role of our bodies, explicitly in relation to data or information. Our observation is that VR/AR/MR technology development is a vision of the future framed in terms of promissory narratives. These narratives develop alongside the underlying enabling technologies and create new use contexts for virtual experiences. It is a vision rooted in the combination of responsive, interactive, dynamic, sharable data streams, and augmentation of the physical senses for capabilities beyond those normally humanly possible. In parallel to the varied definitions of information and approaches to elucidating information behavior, a myriad of definitions and methods of measuring and understanding presence in virtual experiences exist. These and other ideas will be tested by designers, developers and technology adopters as the broader ecology of head-worn devices for virtual experiences evolves in order to reap the full potential and benefits of these emerging technologies.
Quantitative Assessment Of Socio-affective Dynamics In Autism Using Interpersonal Physiology

Autism Spectrum Disorder (ASD) is estimated to affect 1 in 68 individuals in the United States (U.S. Department of Health and Human Services, 2014) and is characterized by qualitative impairments in social reciprocity and communication, along with restricted and repetitive behavior, that collectively manifest early in childhood (American Psychiatric Association, 2013). Individuals with ASD also often have great difficulty interpreting and using nonverbal communication, understanding and navigating social relationships, and making sense of their own and others’ emotions (APA, 2013). Converging evidence in the social and affective neuroscience literature suggests that the same brain regions activated while experiencing affect are also recruited when observing or inferring affective experiences in another person, offering a potential means by which inferring another’s affective state automatically influences one’s own (Decety & Jackson, 2006). Understanding how this underlying process of interpersonal interoceptive inference may be impacted in individuals with social reciprocity impairment is an important step towards developing an etiology of socio-affective impairment in ASD, and could have important implications for intervention development. In support of this aim, we have developed a timevarying dynamical systems model (TVDSM) of dyadic peripheral physiology to provide a feasible, sensitive, interpretable, and statistically valid approach to quantifying socio-affective dynamics and socio-affective functioning in a variety of settings. We assess the reliability and validity of our model using interpersonal physiology data from both controlled and naturalistic studies. Using the TVDSM, we then compare interpersonal physiological measures of socio-affective dynamics with existing behavioral and informant-report measures of social reciprocity in a sample of young children with ASD and a typically developing comparison undergoing both naturalistic and standardized interactions with a parent and researcher in a lab setting.
COLLABORATION IN SCIENTIFIC DIGITAL ECOSYSTEMS: A SOCIO-TECHNICAL NETWORK ANALYSIS

Munyua, Philip Mutuma. Ph.D., Purdue University, May 2016. Collaboration in Scientific Digital Ecosystems: A Socio-Technical Network Analysis. Major Professor: Sabine Brunswicker. This dissertation seeks to understand the formation, operation, organizational (collaboration) and the effect of scientific digital ecosystems that connect several online community networks in a single platform. The formation, mechanism and processes of online networks that influence members output is limited and contradictory. The dissertation is comprised of three papers that are guided by the following research questions: How does online community member’s productivity (or success) depend upon their ‘position’ in the digital networks? What are the network formation mechanism, structures and characteristics of an online community? How do scientific innovations traverse (diffuse) amongst users in online communities? A combination of exploratory, inductive and deductive research designs is applied sequentially but in a non-linear manner to address research question. The dissertation contributes to the literature on scientific collaboration, digital communities of creation, social network modelling and diffusion of innovation. The first paper applies network theory and spatial probit autocorrelative modelling technique to evaluate how member developer’s positioning in digital community correlate with his/her productivity. The second paper looks at the dynamics
Young Learners’ Motivation for Reading in English and Their Reading Achievement

Academic and professional success has never been so dependent on literacy skills in English as a foreign or second language (L2) as it is at the present time. Reading has become a crucial skill in terms of learning mainly due to the fast technological development and a vast body of information available in English on the internet. This situation is placing great demands for reading skill development even to young English language learners. since young learners’ confidence and motivation are found to contribute to their linguistic outcomes, the paper aims to study how young learners’ motivation contributes to their reading skill development and reading achievement. A survey of reading skills and attitudes was conducted with a sample of 502 young English language learners (aged 11, primary grade five) from six state primary schools located in five distant towns in serbia. Motivation and reading skills were investigated both quantitatively and qualitatively. correlational analysis of these results showed that there was a statistically significant difference in reading outcomes in relation to motivation for L2 learning and to attitudes to different aspects of reading, while the interviews with a sub-sample of L2 learners indicated a crucial role of motivation for extensive reading and fluency development. The findings point to the key role of motivation for young learners’ L2 literacy development and have significant pedagogical implications.
The acquisition of A- and A’-bound pronouns in Brazilian Portuguese

Previous studies on the acquisition of pronouns have concentrated either on A-bound or on A’-bound (resumptive) pronouns. In both cases, children performed at chance-level. In this work, I argue that the poor performance is due to a single underlying cause. This conclusion receives support from results of tests I conducted with Brazilian Portuguese-speaking children, in which the same children performed poorly with both A-bound and resumptive pronouns. I follow Grodzinsky and Reinhart’s (1993) claim that children’s behavior is due to their limited working memory, but diverge from them in arguing that the problem resides in comparing syntactic computations, not semantic interpretations. The two analyses make different predictions for resumptive pronouns, where the interpretations for a derivation with a gap and a pronoun are the same and children still have problems.
Measurement of prompt D$^0$, D$^+$, D$^{*+}$, and D$^+_s$ production in p$-$Pb collisions at $\mathbf{\sqrt{{\textit s}_{\rm NN}}~=~5.02~TeV}$

The measurement of the production of prompt D$^0$, D$^+$, D$^{*+}$, and D$^+_s$ mesons in proton$-$lead (p$-$Pb) collisions at the centre-of-mass energy per nucleon pair of $\sqrt{s_{\rm NN}}$ = 5.02 TeV, with an integrated luminosity of $292\pm 11$ $\mu$b$^{-1}$, are reported. Differential production cross sections are measured at mid-rapidity ($-0.96<y_{\rm cms}<0.04$) as a function of transverse momentum ($p_{\rm T}$) in the intervals $0< p_{\rm T} < 36$ GeV/$c$ for D$^0$, $1< p_{\rm T} <36$ GeV/$c$ for D$^+$ and D$^{*+}$, and $2< p_{\rm T} <24$ GeV/$c$ for D$^+_s$ mesons. For each species, the nuclear modification factor $R_{\rm pPb}$ is calculated as a function of $p_{\rm T}$ using a proton-proton (pp) reference measured at the same collision energy. The results are compatible with unity in the whole $p_{\rm T}$ range. The average of the non-strange D mesons $R_{\rm pPb}$ is compared with theoretical model predictions that include initial-state effects and parton transport model predictions. The $p_{\rm T}$ dependence of the D$^0$, D$^+$, and D$^{*+}$ nuclear modification factors is also reported in the interval $1< p_{\rm T} < 36$ GeV/$c$ as a function of the collision centrality, and the central-to-peripheral ratios are computed from the D-meson yields measured in different centrality classes. The results are further compared with charged-particle measurements and a similar trend is observed in all the centrality classes. The ratios of the $p_{\rm T}$-differential cross sections of D$^0$, D$^+$, D$^{*+}$, and D$^+_s$ mesons are also reported. The D$^+_s$ and D$^+$ yields are compared as a function of the charged-particle multiplicity for several $p_{\rm T}$ intervals. No modification in the relative abundances of the four species is observed with respect to pp collisions within the statistical and systematic uncertainties.
Investigation of local opening-sliding relationship in the vicinity of deformed bar in concrete by using DIC technique

One of the important factors affecting the behaviour of a reinforced concrete (RC) is the bond between concrete and the reinforcement bars. To model the bond between concrete and the reinforcement in the numerical simulation of a RC, accurately modelling for the reinforcing bar with its ribs in 3D shape is required. So to simulate the bond behaviour between concrete and steel rebar, the contact surface between them is represented by normal and shear behaviours. The characteristics of these springs are modified based on numerical simulations to get best-matched results to the experimental results. Therefore, in this study, the opening-sliding relationships between a deformed bar and surrounding matrix are investigated experimentally by using digital images correlation (DIC) technique. A standard axial tension test was applied to a deformed steel bar that was embedded in concrete and cement paste specimen. The specimen had a trapezoidal “window,” which allows full continues observation and measurements of concrete or cement paste displacements by recording high-resolution digital video that was later analysed by DIC software. The half of the steel bar’s cross-section was removed to see directly the ribs of the rebar and thereby capture the local deformations between these ribs. For that purpose 15 points were chosen, at the tips of the ribs and also between the ribs of the deformed bar, to investigate the relative displacements between the deformed bar and surrounding matrix in X-direction and Y-direction. Based on the results of the experiments, some relationships between opening and sliding in case of cement paste or concrete were established. It was found that the opening between the concrete and the rebar at the tip of the rib is higher than between the ribs. Also, the sliding between the concrete and the rebar at the tip of the rib is higher than between the ribs. A. Okeil, R. Upadhyay, K. Matsumoto and K. Nagai
Unsteady Combined Entropy and Output-based Adjoint Approach for Mesh Refinement and Error Estimation

This paper presents a strategy for estimating temporal and spatial numerical errors in scalar outputs of unsteady fluid dynamics simulations using a combination of two error indicators: one based on a user-specified engineering output, and the other based on entropy variables. This work is an extension of a combined error indicator approach for steady-state simulations introduced in our previous work. Using an entropy-based approach to calculate error estimates is computationally advantageous as it does not require the solution of an auxiliary adjoint equation. This advantage is significant for unsteady problems, where the reverse adjoint solve is eliminated, as the entropy variables are computed directly from the state. Unfortunately, unlike the output-based approach that specifically targets regions of the spatial domain that are critical to an output, the entropy-based spatial error indicator is prone to target all regions of the domainwhere spurious entropy is generated. Through a combined approach, in which both spatial and temporal errors are combined, the limitations of the entropy-based approach are mitigated, leading tomuch better output error estimates that inmany instances are comparable to those obtained using only the output-based approach. The main approach for combining the indicators investigated in this work is one that uses a coarser output-based adjoint that, while less accurate by itself, is far less computationally expensive. In addition, the use of masks to help improve the spatial error estimates using the entropy variables is also considered. This work focuses predominantly on ways of combining the error indicators for a simulation that uses the compressible Navier-Stokes equations with mesh optimization via error sampling and synthesis (MOESS).
Environmental Considerations in Engineering: Students’ Goals and Journeys

This qualitative study explored the journeys of students with environmental goals who started college majoring in engineering, including students’ motivations to enter college majoring in engineering, their transitions through college, and how they viewed environmental issues as part of their future engineering careers and among the social responsibilities of engineers. Twelve engineering students with initially strong environmental interests were interviewed at the end of their first year of college; nine were initially majoring in environmental engineering (EnvE), two in civil engineering, and one in mechanical engineering. These students spanned three institutions and continued to participate in semi-structured interviews in subsequent years. In addition to interests in the environment, ten of the twelve students described family or friends as influential to choosing to major in engineering and seven discussed liking math/science in high school. Among the twelve students, one graduated with a degree in EnvE, five graduated from Civil Engineering, one graduated from Chemical Engineering, three transferred out of engineering to other majors, and two left college. Students who transferred out of EnvE to other engineering majors cited difficulty/disliking chemistry and broader career options among their reasons. Students who transferred out of engineering cited academic struggles, disliking the courses/curriculum in engineering, and disliking an internship experience. Another student was disappointed that engineering was not creative enough and lacked appropriate attention to broader issues. Among the students who left engineering, two transferred into environmental studies and one into environmental science, indicating their persistent commitment to environmental issues. The seven students who initially had environmental goals and persisted to earn engineering degrees discussed their career goals and responsibilities with respect to the environment in different ways. Five of the seven remained strong in their environmental convictions, and two seemed to lose their passion for helping the environment through engineering. The paper concludes with a discussion of how EnvE might better retain students with a broad range of interests and motivations.
New Technology in Survey Research: Computer-Assisted Personal Interviewing (CAPI)

This paper summarizes what is currently known about computer-assisted personal interviewing (CAPI). It gives an overview of research on CAPI within U.S. Government agencies, at a number of European statistical agencies, and at university-based research organizations in the United States. The paper focuses on four areas of concern among CAPI developers that have slowed deployment of this technology on a broader scale: respondent acceptance, interviewer acceptance, impact on data quality, and costs. It argues that experience to date relative to all of these issues has been encouraging, although we are more certain at this stage about acceptance by respondents and interviewers than we are about the precise impact on data quality or the likely costs of CAPI.
Machine Learning Methods for Medical and Biological Image Computing

MACHINE LEARNING METHODS FOR MEDICAL AND BIOLOGICAL IMAGE COMPUTING Rongjian Li Old Dominion University, 2016 Director: Dr. Shuiwang Ji Medical and biological imaging technologies provide valuable visualization information of structure and function for an organ from the level of individual molecules to the whole object. Brain is the most complex organ in body, and it increasingly attracts intense research attentions with the rapid development of medical and biological imaging technologies. A massive amount of high-dimensional brain imaging data being generated makes the design of computational methods for efficient analysis on those images highly demanded. The current study of computational methods using hand-crafted features does not scale with the increasing number of brain images, hindering the pace of scientific discoveries in neuroscience. In this thesis, I propose computational methods using high-level features for automated analysis of brain images at different levels. At the brain function level, I develop a deep learning based framework for completing and integrating multi-modality neuroimaging data, which increases the diagnosis accuracy for Alzheimer’s disease. At the cellular level, I propose to use three dimensional convolutional neural networks (CNNs) for segmenting the volumetric neuronal images, which improves the performance of digital reconstruction of neuron structures. I design a novel CNN architecture such that the model training and testing image prediction can be implemented in an endto-end manner. At the molecular level, I build a voxel CNN classifier to capture discriminative features of the input along three spatial dimensions, which facilitate the identification of secondary structures of proteins from electron microscopy images. In order to classify genes specifically expressed in different brain cell-type, I propose to use invariant image feature descriptors to capture local gene expression information from cellular-resolution in situ hybridization images. I build image-level representations by applying regularized learning and vector quantization on generated image descriptors. The developed computational methods in this dissertation are evaluated using images from medical and biological experiments in comparison with baseline methods. Experimental results demonstrate that the developed representations, formulations, and algorithms are effective and efficient in learning from brain imaging data.
Time Series Models: Development Trends of Foodstuffs Consumption in CR Considering Healthy Eating

The agrarian sector can be understood in several ways, according to the integration of the agrarian sector into national economic structures. Understanding the agrarian sector in its complex relation to the sectors that represent the final consumption of already processed agricultural products food (not agricultural raw materials) represents agribusiness. Among other issues, food consumption is affected by the newest nutrition trends and opinions on healthy eating and food safety. The paper ́s aim is an indication of development tendencies in the consumption of some selected food types and subsequent construction of a short-term forecast, considering food quality and healthy eating. Some of the prognostic procedures based on time series extrapolation have been applied in this paper. Time series analysis is therefore the starting basis for assessment of the selected foodstuffs consumption developmental tendencies and a subsequent short-term forecasts construction.The results demonstrate unambiguously that, the time series analysis methods can be applied with success in finding development tendencies of the food consumption indicators studied. Application of the adaptive models in particular, can bring good quality outcomes in this field. Anyway, it is not possible to reach an unambiguous conclusion concerning the assessment whether the consumption of some selected food types is in line with nutritional recommendations. In some cases it is, in some it is not.
Schema Theory and College English Reading Teaching

Schema theory has it that any language may have its meaning just when it is organized and responded by its reader or listener with his schemata,as either oral or written language contains no meaning itself.The teaching of English reading is composed not only of the language schemata but also of content schemata.Therefore,the backgrounds of the English reading should be more strengthened while the language schemata is emphasized in English reading at college so that the teaching quality can be more promoted.
The Theory-Based View: Economic Actors as Theorists

This paper outlines the theory-based view of strategy and markets. We argue that novel or “great” strategies come from theories. Entrepreneurs and managers originate theories and hypotheses about which activities they should engage in, which assets they might buy, and how they will create value. A firm’s strategy, then, represents a set of contrarian beliefs and a theory—a unique, firm-specific point of view—about what problems to solve, and how to organize and govern the overall process of value creation. We outline the cognitive and perceptual, organizational, and economic foundations of the theory-based view of strategy. We also discuss the essential attributes needed for a firm-level theory of strategy. Throughout the paper we offer informal examples of our argument, by briefly discussing the strategies of companies like Apple, Uber, Disney, Wal-Mart, and Airbnb. The theory-based view of strategy and markets also offers important insights for how firms govern themselves (including ownership, boards, a.
Geometric Matching and Spatial Pricing in Ride-Sourcing Markets

This paper develops a model to investigate the effects of spatial pricing on ride-sourcing markets. The model is built upon a discrete time geometric matching framework that matches customers with drivers nearby. We demonstrate that a customer may be matched to a distant vehicle when demand surges, yielding an inefficient supply state. We further investigate market equilibrium under spatial pricing assuming a revenue maximizing platform, and find that the platform may resort to relatively higher price to avoid the inefficient supply state if spatial price differentiation is not allowed. Although spatial pricing facilitates market clearing, the platform may still set price more than the efficient level, which compromises the public interest. We then propose a commission rate cap regulation that reaps the flexibility of spatial pricing and can achieve the second best under some homogeneity assumptions.