Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Preservice and Practicing Teachers' Thinking About Children and Teaching.

This study seeks to evaluate if and how undergraduate and graduate students in an early childhood education program applied theories of child development and knowledge of pedagogy in discussing and interpreting young children's behavior in two different contexts.This was assessed by analyzing their discourse as they discussed video tapes: (1) in small groups after viewing a video of children playing over the course of 18 months; and (2) in stimulated recall sessions in which the students were reflecting on lessons that they had taught. A brief description is given of the current undergraduate early childhood program at the University College of New York at Oswego, and the procedures used in the study. Data from the study indicated that the undergraduates' discussion of children revealed that they attempted to apply course content to their observations but lacked finely tuned observation skills. As compared to the more experienced teachers, their recommendations were often vague and abstract. Implications of the findings for improsd_Lng a teacher education program are discussed. (JD) ******************************g*********************g****************** Reproductions supplied by EDRS are the best that can be made from the original document. ******************************g*********************g****************** Exploring Preservice and Practicing Teachers' Thinking about Children and Teaching Barbara A. Beyerbach
Fitch CDS Implied Ratings ( CDS-IR ) Model Analysts Summary

Analysts Summary The Fitch CDS Implied Ratings (CDS-IR) model processes the collective marketplace view of firms' credit condition based on its current CDS pricing and region. It then calculates and converts these into implied ratings. The Fitch CDS-IR model is a proprietary and data-intensive rating model that covers a global universe and refines market information into a model-based credit assessment. The principal output is a forward-looking credit assessment expressed in the traditional rating grade. The model's comprehensive scope, data-intensive foundation, and exhaustive validation testing ensure robust out-of-sample predictive power. CDS-IR rating outputs are useful with regard to absolute levels and relative changes across time. Model calibration for the Fitch CDS-IR is based principally on The Fitch CDS Pricing Service data which goes back from present time to 1999 globally on a daily basis. The Fitch CDS-IR model covers over 2,500 reference entities in 84 countries globally All industries are covered by this model, including Financials and Sovereigns. The model provides daily output of market implied credit ratings. Testing and validation of Fitch CDS-IR includes: 1) in-sample Hit-Miss Matrices by region, 2) out-of-sample Forward Analysis to examine how well implied ratings forecast changes predict agency ratings, and 3) the Accuracy Ratio from Cumulative Accuracy Profile plots to gauge the ratings' ability to rank order obligors. The Fitch CDS-IR model is an accurate market implied rating tool that has undergone out-of-sample validation across its coverage universe globally.
Search personalization in hyperlinked environments by relevance propagation and ant colony optimization

Personalization is a promising way of improvement of the search services in large document collections and on the Web. User modeling is in the core of many personalization efforts because accurate user model can provide essential information for user specific search adjustments and result set processing. In this paper, we propose and study user modeling technique based on click-through data, relevance propagation and ant colony optimization.
Basque spatial cases and the ergative-absolutive synchretism

This paper has as its aim to connect two seemingly disparate phenomena: the structure of Basque adpositional phrases and the synchretisms arising in plural DPs between ergative and absolutive arguments in some of the dialects of Basque. This synchretism, which can be found in western and central dialects of Basque, resolves in favour of absolutive marking, and has been taken to be the product of relatively shallow historical morphophonological processes. If the approach held here stands a reasonable chance, there may be a different way of looking at those synchretisms, by putting them in the context of diachronic processes related to the expansion of number morphology, and by capitalizing on the incompatibility of number with a particular kind of syntactic object: the so called axial part (Jackendoff, 1996), an integral part of locative constructions cross-linguistically, if recent work on the structure of those constructions is on the right track (see Kayne, 2005; Svenonius, 2006; Pantcheva, 2011, among many others).
Metacognition and Meta-Affect in Young Students: Does it Make a Difference in Mathematical Problem Solving?

Mathematical problem solving is one of the most valuable aspects of mathematics education and the most difficult for elementary school students. Cognitive and metacognitive difficulties in this area cause students to develop negative attitudes and emotions as affective reactions, hampering their efforts and achievements. These metacognitive and meta-affective reactions are fundamental aspects of self-regulated learning (SRL), a non-innate process that requires systematic, explicit student training. This study investigated the impact of two self-regulation programs among young students (Grade 5)—metacognition (n = 64) and meta-affect (n = 54) versus a control group (n =53)—on enhancing achievements in mathematical verbal problem solving and a novel transfer task, as well as metacognitive and meta-affective regulation processes of a focus group during a thinking-aloud solution. Mixed methods indicate that students who participated in the metacognitive and meta-affective intervention programs presented similar but higher achievements than the control group. Additionally, during the thinking-aloud solution, students from each group broadly implemented the self-regulation processes they were trained in, while consistently referring to all the self-regulation phases. The current study makes an important contribution to practical implications for students with diverse abilities.
A Hybrid Face Detection System using combination of Appearance-based and Feature-based methods

Summary Human face detection is preliminary required step of face recognition systems as well as a very important task in many applications, such as security access control systems, video surveillance, human computer interface and image database management. This paper intends to combine Viola and Jones face detection method with a color-based method to propose an improved face detection method. Experimental results show that our method efficiently decreased false positive rate and subsequently increased accuracy of the face detection system especially in complex background images. Also our proposed method considerably increased face detection speed.
Separating Scheduling Algorithms with the Relative Worst Order Ratio

The relative worst order ratio is a measure for the quality of online algorithms. Unlike the competitive ratio, it compares algorithms directly without involving a n optimal offline algorithm. The measure has been successfully applied to problems like paging and bin packing. I n this paper, we apply it to machine scheduling. We show that for preemptive scheduling, the measure separates multiple pairs of algorithms which have the same competitive ratios; with the relative worst order ratio, the al gorithm which is “intuitively better" is also provably bett r. Moreover, we show one such example for non-preemptive sched uling.
Chinese Semantic Dependency Relation System and Treebank Construction

Deep semantic parsing is the key to understand sentence meaning. This paper integrates some Chinese semantic relation systems given by different scholars, and presents a more comprehensive system for semantic dependency parsing. The new semantic relation system includes the definition for the situation that a verb acts as a modifier and a verbal noun acts as the center of the noun phrase. According to the relation system, a large scale Chinese semantic dependency relation tree bank is constructed by the combination of automatic and manual means. This semantic dependency tree bank will become a basis of studying deep semantic parsing.
Rise of the Kniesians: the professor-student network of Nobel laureates in economics

Abstract The paper presents the professor-student network of Nobel laureates in economics. 82 of the 87 Nobelists belong to one family tree. The remaining five belong to three separate trees. There are 376 men in the graph, and five women. Karl Knies is the central-most professor, followed by Wassily Leontief. No classical and few neo-classical economists have left notable descendants. Harvard is the centralmost university, followed by Chicago and Berlin. Most candidates for the Nobel Prize belong to the main family tree, but new trees may arise for the students of Terence Gorman and Denis Sargan.
The effect of the non-target object position on wrist motion

In the present work we show the important role of wrist movements for the avoidance of non-target objects (obstacles) in a psychological pick-and-place experiment. The results show that humans can adjust the position of their wrist as well as the angle between their underarm and their hand during a prehension movement relative to the position of a non-target object. This indicates that humans tend to grasp the target object from the side which is further away from the non-target object. Based on these results, a preliminary model is proposed to simulate the wrist movement with respect to the position of a non-target object. We introduce this model with regard to a possible application in human-robot interaction and to better understand human's collision avoidance mechanism.
Extracting Behavioral Patterns from Relational History Data

Identifying user-dependent information that can be automatically collected helps build a user model by which to predict what the user wants to do next. Such information is often relational and is not suited to the traditional attribute-value representation. Graph-based induction (GBI), a machine learning technique to nd typical patterns in a directed graph, e ciently extracts rules to predict next command from such data. The heart of GBI is pairwise chunking. The paper shows how this simple mechanism applies to the top down induction of decision trees for nested attribute representation. The algorithm is implemented and tested against both arti cial and real data. The results clearly shows that the dependency analysis of computational processes activated by the user commands which is made possible by GBI is indeed useful to build a behavior model and increase prediction accuracy.
Trending Words Based Event Detection in Sina Weibo

Online social networks provide us an unprecedented volume of available data, which results from the pervasive adoption of online social applications. In particular, for the unique characteristics on promoting content sharing, microblogging social networks offer us a new proxy for detecting and tracking the events being taken place in the real world. In spite of large amount of social babble involved, the microblog data contains fresh news coming from human sensors at a humungous rate. As the online social network is a platform that is able to process fast changing streaming data, however it is hard to discover meaningful events in such noisy circumstances in time. In this paper, we study the keywords determining problem in event detection and propose a novel and much more effective method for discovering bursty words in microblogging social networks by leveraging temporal dynamics information. Based on this, we propose an efficient event detection framework applied in Sina Weibo---a Chinese microblogging site similar to Twitter. With experiments conducted on real data sourced from Sina Weibo, we show the effectiveness and feasibility of our proposed method and framework.
Xgrip109: A γ Tubulin–Associated Protein with an Essential Role in γ Tubulin Ring Complex (γTuRC) Assembly and Centrosome Function

Previous studies indicate that γ tubulin ring complex (γTuRC) can nucleate microtubule assembly and may be important in centrosome formation. γTuRC contains approximately eight subunits, which we refer to as Xenopus gamma ring proteins (Xgrips), in addition to γ tubulin. We found that one γTuRC subunit, Xgrip109, is a highly conserved protein, with homologues present in yeast, rice, flies, zebrafish, mice, and humans. The yeast Xgrip109 homologue, Spc98, is a spindle–pole body component that interacts with γ tubulin. In vertebrates, Xgrip109 identifies two families of related proteins. Xgrip109 and Spc98 have more homology to one family than the other. We show that Xgrip109 is a centrosomal protein that directly interacts with γ tubulin. We have developed a complementation assay for centrosome formation using demembranated Xenopus sperm and Xenopus egg extract. Using this assay, we show that Xgrip109 is necessary for the reassembly of salt-disrupted γTuRC and for the recruitment of γ tubulin to the centrosome. Xgrip109, therefore, is essential for the formation of a functional centrosome.
Leadership as Process: A theory of formal and informal organizing in complex adaptive systems

Organization theorists have increasingly recognized the need to incorporate dynamic processes that include individual intentionality into theories of organizing. In particular, the leadership and human interaction aspects of organizing and their role in the development of organizational and managerial capabilities have been identified as an under researched area. This paper addresses this gap. Using complexity-informed theories of human interaction dynamics (HID), the paper defines leadership as the process that evolves organizational capabilities. More specifically, it describes three leadership processes that serve three system survival functions, and it identifies three mechanisms that operate locally to enable organizing through local interactions within a complex adaptive system. In sum, leadership evolves the ways in which individuals interact with one another to survive and prosper as collectives. It thus shapes the particular expression of dynamic, operating and managerial capabilities that emerge.
Multi-agent Architecture for Collaborative CAD System

In order to improve the features of traditional CAD system, a system architecture based on multi-agent is put forward. The proposed system framework includes three levels, supporting the collaborative product design for different remote users. The user agents are classified as real-time monitoring agent, conflict detection agent, conflict solution agent, user communication agent and knowledge management agent. We give the detail flow architecture of the user agents. The experiment system presents the efficient cooperation among multidisciplinary designers.
Duplicate Question Detection Using Online Learning

An integral part the learning experiences for both on-campus and off-campus courses are online communities such as Piazza. Piazza is described as a “Q&A” forum which is created for every class. An integral part of many online communities is the concept of “voting” to determine popular and useful information. However in many class orientated forums, this feature is not emphasised as post relevance tends to change relative to course assessments. Creating a way to understand quality posts can be beneficial for students and instructors, as it will reduce the clutter in the community by allow students to ”see” important posts in the follow up section of different threads and for instructors to identify potential experts and teaching assistant candidates for future classes. In this project, we aim to develop suitable approach for understanding quality of posts through introducing online machine learning techniques to assist with reducing clutter within young online communities. Keywords—duplicate question, Piazza, online learning, machine learning
A Multi-ring Color Fiducial System and A Rule-Based Detection Method for Scalable Fiducial-tracking Augmented Reality

In Augmented Reality (AR), a user can see a virtual world as well as a real world. To avoid the registration problem between the virtual world and the real world, the user’s pose in both worlds should be exactly the same. Fiducial tracking AR is an attractive approach to the registration problem, but most of the developed fiducial tracking AR systems have very limited tracking ranges and require carefully prepared environments, especially lighting conditions. To provide for wide views and detailed views in large-scale applications, an AR system should have a scalable tracking capability under varying light condition. In this paper, we propose multi-ring color fiducial systems and a light-invariant fiducial detection method for scalable fiducial tracking AR systems. We analyze the optimal ring width, and develop formulas to obtain the optimal fiducial set with system specific inputs. We present a light-invariant circular fiducial detection method that uses relations among fiducials and their backgrounds for segmenting regions of an image. Our work provides a simple and convenient way to achieve wide-area tracking for AR.
The Issue of Racial Profiling in Traffic Stop Citations

This study addresses racial profiling when the traffic stop outcome is a citation. This study uses focal concerns theory as a theoretical explanation for police officer decision-making while using propensity score matching to provide similarly situated drivers based on race and/or gender. This study uses traffic stop data (N = 48,586) collected by the Louisville Police Department between January 1 and December 31, 2002. The statistical results show that focal concerns theory components matter the most for traffic stop data even though racial profiling is still an issue. Propensity score matching is a statistical technique that provides a better way to determine whether racial profiling was evident. Gender was not significant for female drivers. This study advances our understanding of race and traffic stop citations using a theoretical explanation.
An Interference Aware Dynamic Spectrum Sharing Algorithm for Local Area LTE-Advanced Networks

This paper presents a dynamic spectrum sharing algorithm to minimize the inter-cell interference, so as to achieve a high system performance. This algorithm operates in a self-organized manner without the need of any centralized control, thereby is especially useful in Local Area (LA) where the enhanced-NodeBs (eNB) are close and coordination among them is hard or expensive to achieve. Results show that with very limited signaling between the eNBs, the proposed algorithm can effectively improve the system performance, and it can even
Using ESDA to Detect Overlapping Multi-communities

Traditional algorithm in Community identification take full advantage of vertex. But in these algorithms, node’s aggregation characteristics are not obvious and the quantity of communities is not reasonable. The Edge of the Spectral Decomposition Algorithm (ESDA) is different from traditional method for Community partition. There are four steps in ESDA: first, we translate the origin graph into line graph. Second, edge degree for 1 and the special local gathered structure are dealt by pre-processing to simplify complex networks. Third, ESDA would use the second smallest, third smallest, the special eigenvalue corresponding to eigenvector to build up coordinate system. Finally, we can identify community by using coordinate system. Experiments show that this algorithm not only make more prominent characteristics of community together and has a better effect, but also speeds up partition of community by sub-step pretreatment.
Learning ability is a key outcome determinant of GSK-3 inhibition on visuospatial memory in rats

Learning aptitude has never been a focus of visuospatial performance studies, particularly on memory consolidation and reconsolidation. The aim of this study was to determine the consequences of learning ability on memory consolidation/reconsolidation following inhibition of glucose synthase kinase-3 (GSK-3) by 4-benzyl-2-methyl-1,2,4-thiadiazolidine-3,5-dione (TDZD-8). The anxiety-like nature of rats was characterized in the elevated plus maze. The rats were then trained for four days in the Morris water maze (MWM) and classified as ‘superior’, ‘intermediate’ or ‘inferior’ learners. There were no major differences between superior, intermediate or inferior learners with respect to anxiety which might have influenced learning. After training (day-5), TDZD-8 (2.0 mg/kg) was administered and half of the cohort were exposed to a MWM retrieval trial. Ten days later, animals were subjected to repeated MWM learning. TDZD-8 without a retrieval trial impaired subsequent reconsolidation in inferior learners, but enhanced it in superior learners. There was no modification of performance in intermediate learners. In TDZD-8-treated subjects exposed to retrieval, the pattern of outcomes was identical whereby impairment of reconsolidation occurred in inferior learners, enhancement occurred in superior learners but there was no modification of performance in intermediate learners. Thus, learning ability was a key determinant of the qualitative outcome from GSK-3 inhibition on visuospatial memory.
Determining the effectiveness of alternative seating systems for students with attention difficulties: A systematic review

ABSTRACT A student’s ability to attend in the classroom is an important factor in determining academic success. This systematic review examined the effects of an alternative seating system on improving attention in students with attention difficulties. Databases searched included CINAHL, Medline, PsycINFO, OTSeeker, Cochrane Library, and Google Scholar. Articles were critically appraised using a format based on the American Occupational Therapy Association’s Evidence-Based Literature Review Project. The results of this review suggest that the use of alternative seating devices generally improves attention for students with attention difficulty.
Calibrating an Optical See-Through Rig with Two Non-overlapping Cameras: The Virtual Camera Framework

We present a novel extrinsic calibration method for optical see-through systems. It is primarily aimed at tablet-like systems with a semi transparent screen, a camera tracking the user position and another camera analyzing the scene but easily generalizable to any optical see-through setup. Relative poses of the cameras and the screen are all needed for proper alignment. The proposed algorithm is based on the user indicating the projections onto the screen of several reference points chosen on a known object. A convex estimation is computed through the resectioning of virtual cameras and used to initialize a global bundle adjustment. Both synthetic and real experiments show the viability of our approach.
Extended Object Tracking: Introduction, Overview and Applications

This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.