Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of warmth and competence in the evaluation of teachers

This study investigated the effect of student performance (ESP) and nature of student feedback (NSF) on student evaluation of teaching (SET) using 150 students (n =38 males, n = 112 females). They were randomly assigned to a 2 (NSF: Anonymous versus Non-anonymous) X 3 (ESP: Pass versus No results versus Fail) between-subjects factorial experimental design (n=30). Participants evaluated a video of a teacher teaching; using the Teacher Judgement Scale (TJS). TJS was developed following universal social judgment dimensions of warmth and competence. The primary hypothesis was accepted as participants rated the teacher as more competent than warm, thereby supporting the supremacy of competence in the evaluative domain for teaching. These findings, with technology, would be integral to prolonging SET's usage.
Cross-pose Facial Expression Recognition Council of Turkey (t ¨ Ub ˙ Itak) for Supporting Me with National Scholarship Pro- Gramme for M.sc. Students. I Would like to Thank My Friends Duygu Sinem Yıldırım, N. C ¸ A˘ Grı Kılıboz, Belma

¨ Ozgür for participating in my thesis jury, their criticism and valuable feedback. Engin, Kaan Bekmezci, and Safiye C ¸ elik for always being there to have fun with. I am especially grateful to my dear friend, Beyza Ermi¸s for alleviating the burden of this painful process. I would also like to thank the members of MediaLab and PILAB for providing an enjoyable working environment. I would like to express my deepest gratitude to my parents S ¸aziye and Beytul-lah Güney, and my sister Bü¸sra Güney for their endless love, support and patience throughout this process. I am specifically grateful to N. Murat Arar. I honestly could not bear the burden without his support and motivation. iv ABSTRACT CROSS-POSE FACIAL EXPRESSION RECOGNITION Automatic facial expression recognition is a popular research topic due to its interesting applications in a wide variety of areas. The existing studies have achieved high accuracies in various formulations of the same problem. One direction which is not fully explored is multi-view facial expression recognition. Variations caused by different poses impose extra burden on the task of recognizing expressions, which is already a difficult problem due to large differences across subjects. In this thesis, we present a method to recognize six prototypic facial expressions of an individual across different pose angles. We use Partial Least Squares (PLS) to map the expressions from different poses into a common subspace, in which correlation between them is maximized. Recently, PLS has been successfully used for pose invariant face recognition problem. We show that, PLS can be effectively used for facial expression recognition across poses by training on coupled expressions of the same identity from two different poses. This way of training lets the learned bases model the differences between expressions of different poses by excluding the effect of the identity. We first align the faces and then extract block features around two eyes and the mouth on the aligned image. We experiment with Gabor filters and direct intensity values for local face representation. We demonstrate that two representations perform similarly in case frontal is the input pose, but Gabor representation outperforms intensity representation for other pose pairs. We also perform a detailed analysis of the parameters used in the experiments to show their effects on the results and to find the optimal ones for the expression recognition problem.
Avoid communication outages in decentralized planning

This paper proposes an extension to the Consensus-Based Bundle Algorithm (CBBA), a distributed task allocation framework previously developed at the Aerospace Control Laboratory (ACL), MIT, designed to address complex mission planning for a team of heterogeneous agents operating in a dynamic environment. A key assumption of CBBA involves having a connected network, a constraint which is not always feasible in realistic mission scenarios. In this paper, we present a mechanism that aims at preventing and repairing network disconnects. The dynamic network topology is predicted using available information within the planner and relay tasks are created to strengthen the connectivity of the network. By employing underutilized resources, the presented approach improves network connectivity without limiting the scope of the active agents thus improving mission performance.
Research of Moving Targets Tracking Algorithm Based on Kalman Filtering

The purpose of moving targets tracking is to determine the trajectories of moving targets, the key of which is to establish the corresponding relationships between the detected prospect targets and the tracked moving targets. The established corresponding relations are equivalent to the matching problem of target features in consecutive image frames. In the paper, Kalman filter is used as the motion model of the targets. The recursive filtering method is adopted to calculate and predict the location of moving targets. Through the experiment, the results show that the algorithm not only track well the moving targets, but also can effectively overcome the obstacles in the process of tracking and so on.
Introductory analysis of Bénard–Marangoni convection

We describe experiments on Bénard–Marangoni convection which permit a useful understanding of the main concepts involved in this phenomenon such as, for example, Bénard cells, aspect ratio, Rayleigh and Marangoni numbers, Crispation number and critical conditions. In spite of the complexity of convection theory, we carry out a simple and introductory analysis which has the additional advantage of providing very suggestive experiments. As a consequence, we recommend our device for use as a laboratory experiment for undergraduate students of the thermodynamics of nonlinear and fluid physics.
Early Childhood Open-Mindedness: An Investigation Into Preservice Teachers’ Capacity to Address Controversial Issues

We investigated the characteristics of early childhood education (ECE) preservice teachers (PSTs) who were identified as closed minded and their capacity to deal with controversial issues. We define open-mindedness as the willingness to consider experiences, beliefs, values, and perspectives that differ from one’s own. First, we used quantitative surveys to identify PSTs with less open-minded thinking dispositions among those who responded (n = 84). Then, we selected and ultimately interviewed five participants who indicated that their religious views played a significant role in the way they developed their epistemological and ontological views. Interviews suggested that concepts of social justice and the common good were seen as inimical to their core religious beliefs. As such, some respondents avoided these ideas without sufficiently entertaining the complexity involved with their religious beliefs and democratic ideals. This study has numerous implications for the macrocurriculum of teacher education.
Optimizing, Planning and Executing Analytics Workflows over Multiple Engines

Big data analytics have become a necessity to businesses worldwide. The complexity of the tasks they execute is ever increasing due to the surge in data and task heterogeneity. Current analytics platforms, while successful in harnessing multiple aspects of this \data deluge", bind their ecacy to a single data and compute model and often depend on proprietary systems. However, no single execution engine is suitable for all types of computation and no single data store is suitable for all types of data. To this end, we present and demonstrate a platform that designs, optimizes, plans and executes complex analytics workows over multiple engines. Our system enables users to create workows of variable detail concerning the execution semantics, depending on their level of expertise and interest. The workows are then analysed in order to determine missing execution semantics. Through the modelling of the cost and performance of the required tasks over the available platforms, the system is able to match distinct workow parts to the execution and/or storage engine among the available ones in order to optimize with respect to a user-dened policy.
Serum Uric Acid Levels in Different Phases of Acute Severe Manic and Depressed Patients

Background: The level of serum uric acid, as an important endogenous antioxidant, may be correlated to the different phases of mood disorders. Objectives: The aim of this study was to compare serum uric acid levels before and after treatment in patients with acute mania and depression. Patients and Methods: We measured serum uric acid in 33 manic and 10 depressed hospitalized patients, both before and after treatment. Mood disorder was diagnosed according to the DSM-IV criteria. Manic or depressive scores were measured with the Young Mania Rating Scale or the Hamilton Depression Rating Scale, respectively. Uric acid levels were compared in the acute and remission phases of the mood disorder, and the relationship of uric acid levels with the onset of response was analyzed. Results: Serum uric acid levels were increased after bipolar disorder treatment. Serum uric acid levels were increased after bipolar disorder treatment. Moreover, depressed patients with lower uric acid levels had a faster onset of response. The uric acid levels in the acute mania patients were higher than in the acute depression patients. Conclusions: The remission phase, in comparison to acute mania or depression, had higher levels of uric acid. Moreover, lower serum uric acid may correlate to a faster response in depressed patients. These results may support the roles of the purinergic system and of oxidative stress in the treatment of mood disorders.
An End-to-End Content-Aware Congestion Control Approach for MPEG Video Transmission

In this paper, we present a receiver-based, bandwidth estimation rate control mechanism with content-aware probability retransmission to limit the burden on multimedia transmission congested network. Like the TCP friendly rate control (TFRC) protocol, we compute the sending rate as a function of the loss event rate and round-trip time. Considering the different importance of four distinct types of frames in Standard MPEG encoders, we divide data packets into three grades coarsely and adopt adaptive probability retransmission strategy to assure video playback quality. It is an extension of TCP friendly congestion control. This paper describes the smooth rate algorithm and probability retransmission mechanism. The result of experiments with competing TFRC specification demonstrates the proposed approach reaches a higher throughput and higher PSNR than TFRC especially on the bottleneck links.
Mobile reading as social and embodied practice

Abstract Literacy, and particularly reading, is critical to success in schooling and full participation in contemporary societies. As one of the primary skills needed to develop proficiency in a language, the study of reading in additional languages has attracted significant research attention. Focusing on behaviourally visible and locally occasioned literacy events, this paper reports on an analysis of how public interactional practices for out-loud reading by small groups of English language learners facilitate the routinisation of interactional practices. The learners are participants in a mobile augmented reality (AR) activity that involves walking around a university campus in order to complete five serial tasks. Data come from video recordings of the game playing of eight groups (16 h of data) and their associated transcripts, analysed according to the principles of ethnomethodological conversation analysis. Results show that written texts that are part of the activity are given local meaning via various interactional practices that include public reading, co-reading and other embodied practices. We show how these dynamics of re-textualisation provide evidence for developing interactional practices over the course of the five tasks that are part of the activity.
Improved feature vectors using N-to-1 Gaussian MFCC transformation for automatic speech recognition system

In this paper, we propose a novel vector transformation projecting the feature vectors in a new space, characterized by good discriminant properties, while reducing drastically the number of parameters used in the ASR systems. We call this method “N-to-1 Gaussian MFCC transformation”. It uses the HMM acoustic parameters obtained by N and 1 Gaussian in the training process in order to calculate the transformed vectors in the new projection space. Our transformation technique permits an important reduction of the number of Gaussians (in the GMM modeling of the emission probability of each state) while improving the performances of ASR systems. Our experimental results using both TIMIT and FPSD corpus demonstrate that the proposed feature transformation, improves the phone recognition accuracy when compared with classical methods using conventional cepstral feature vectors in the context of using HMMs with a number of Gaussians less than 16 by state.
On the Precoder Design of a Wireless Energy Harvesting Node in Linear Vector Gaussian Channels with Arbitrary Input Distribution

A Wireless Energy Harvesting Node (WEHN) operating in linear vector Gaussian channels with arbitrarily distributed input symbols is considered in this paper. The precoding strategy that maximizes the mutual information along N independent channel accesses is studied under non-causal knowledge of the channel state and harvested energy (commonly known as offline approach). It is shown that, at each channel use, the left singular vectors of the precoder are equal to the eigenvectors of the Gram channel matrix. Additionally, an expression that relates the optimal singular values of the precoder with the energy harvesting profile through the Minimum Mean-Square Error (MMSE) matrix is obtained. Then, the specific situation in which the right singular vectors of the precoder are set to the identity matrix is considered. In this scenario, the optimal offline power allocation, named Mercury Water-Flowing, is derived and an intuitive graphical representation is presented. Two optimal offline algorithms to compute the Mercury Water-Flowing solution are proposed and an exhaustive study of their computational complexity is performed. Moreover, an online algorithm is designed, which only uses causal knowledge of the harvested energy and channel state. Finally, the achieved mutual information is evaluated through simulation.
DSL-Lab: A Low-Power Lightweight Platform to Experiment on Domestic Broadband Internet

This article presents the design and building of DSL-Lab, a platform to experiment on distributed computing over broadband domestic Internet. Experimental platforms such as PlanetLab and Grid'5000 are promising methodological approaches to study distributed systems. However, both platforms focus on high-end service and network deployments only available on a restricted part of the Internet, leaving aside the possibility for researchers to experiment in conditions close to what is usually available with domestic connection to the Internet. DSL-Lab is a complementary approach to PlanetLab and Grid'5000 to experiment with distributed computing in an environment closer to how Internet appears, when applications are run on end-user PCs. DSL-Lab is a set of 40 low-power and low-noise nodes, which are hosted by participants, using the participants' xDSL or cable access to the Internet. The objective is to provide a validation and experimentation platform for new protocols, services, simulators and emulators for these systems. In this paper, we report on the software design (security, resources allocation, power management) as well as on the first experiments achieved.
THE LIMIT DISTRIBUTION OF THE CUSUM OF SQUARES TEST UNDER GENERAL MIXING CONDITIONS

We consider the cumulative sum (CUSUM) of squares test in a linear regression model with general mixing assumptions on the regressors and the errors. We derive its limit distribution and show how it depends on the nature of the error process. We suggest a corrected version that has a limit distribution free of nuisance parameters. We also discuss how it provides an improvement over the standard approach to testing for a change in the variance in a univariate times series. Simulation evidence is presented to support this.
Distributed Pressure and Temperature Sensing Based on Stimulated Brillouin Scattering

Brillouin scattering has been verified to be an effective mechanism in temperature and strain sensing. This kind of sensors can be applied to civil structural monitoring of pipelines, railroads, and other industries for disaster prevention. This thesis first presents a novel fiber sensing scheme for long-span fully-distributed pressure measurement based on Brillouin scattering in a side-hole fiber. After that, it demonstrates that Brillouin frequency keeps linear relation with temperature up to 1000°C; Brillouin scattering is a promising mechanism in high temperature distributed sensing. A side-hole fiber has two longitudinal air holes in the fiber cladding. When a pressure is applied on the fiber, the two principal axes of the fiber birefringence yield different Brillouin frequency shifts in the Brillouin scattering. The differential Brillouin scattering continuously along the fiber thus permits distributed pressure measurement. Our sensor system was designed to analyze the Brillouin scattering in the two principal axes of a side-hole fiber in time domain. The developed system was tested under pressure from 0 to 10,000 psi for 100m and 600m side-hole fibers, respectively. Experimental results show fibers with side holes of different sizes possess different pressure sensitivities. The highest sensitivity of the measured pressure induced differential Brillouin frequency shift is 0.0012MHz/psi. The demonstrated spatial resolution is 2m, which maybe further improved by using shorter light pulses. A traditional Brillouin optical time domain analysis BOTDA system was launched to test the relation between Brillouin frequency and temperature from room temperature to 1000°C environment over 600m optical fiber. Our preliminary experimental results also demonstrated a
Noval design of a broadband phased array antenna

A novel broadband slot antenna array based on enhanced mutual coupling is presented. By increasing the mutual coupling effects between the slot array elements intentionally, array bandwidth can be extended effectively. The slot linear array is designed to operate in a wide frequency band ranges from 4GHz to 18GHz. Numerical results are presented to show the excellent performance of the array, such as large bandwidth, large grating-lobe free scan volume, low profile, low cross-polarization and compatible with conformal applications.
Closures of database hypergraphs

A hypergraph formalism is introduced to represent database schemata. In particular, a database schema <italic>B</italic>, described by one full join dependency and a set of functional dependencies, is represented by a (database) hypergraph <italic>H</italic>, containing both undirected and directed hyperedges. Undirected hyperedges correspond to the relations in the join dependency, and directed hyperedges correspond to the functional dependencies. In addition, two classes of database hypergraphs are defined: <italic>e</italic>-acyclic hypergraphs and <italic>e</italic>-independent hypergraphs. A hypergraph is <italic>e</italic>-acyclic if it is equivalent to some acyclic hypergraph; it is <italic>e</italic>-independent if it is equivalent to some independent (i.e., cover-embedding) hypergraph. Furthermore, the closure of a database hypergraph is defined as the extension of the transitive closure of a graph. By using a lower bound and an upper bound of the hypergraph closure (called L-closure and U-closure, respectively), it is proved that two <italic>e</italic>-acyclic (<italic>e</italic>-independent) hypergraphs are equivalent if and only if they have the same closure. Moreover, a hypergraph is <italic>e</italic>-acyclic (<italic>e</italic>-independent) if and only if its closure is acyclic (independent) and, in most cases, such a recognition can be done in polynomial time. Finally, it is shown how to use the database hypergraph closure to solve some database design problems.
Scalable implicit methods for reaction-diffusion equations in two and three space dimensions

This paper describes the implementation of a solver for systems of semi-linear parabolic partial differential equations in two and three space dimensions. The solver is based on a parallel implementation of a non-linear Alternating Direction Implicit (ADI) scheme which uses a Cartesian grid in space and an implicit time-stepping algorithm. Various reordering strategies for the linearized equations are used to reduce the stride and improve the overall effectiveness of the parallel implementation. We have successfully used this solver for large-scale reaction-diffusion problems in computational biology and medicine in which the desired solution is a traveling wave that may contain rapid transitions. A number of examples that illustrate the efficiency and accuracy of the method are given here; the theoretical analysis will be presented.
Restoration of error-diffused images using POCS

Halftoning is a process that deliberately injects noise into the original image in order to obtain visually pleasing output images with a smaller number of bits per pixel for displaying or printing purposes. In this paper, a novel inverse halftoning method is proposed to restore a continuous tone image from the given halftone image. A set theoretic formulation is used where three sets are defined using the prior information about the problem. A new space domain projection is introduced assuming the halftoning is performed with error diffusion, and the error diffusion filter kernel is known. The space domain, frequency domain, and space-scale domain projections are used alternately to obtain a feasible solution for the inverse halftoning problem which does not have a unique solution.
A Novel Semi-Supervised Learning for Collaborative Image Retrieval

Content-based image retrieval (CBIR) solutions with regular Euclidean metric usually cannot achieve satisfactory performance due to the semantic gap. Hence, relevance feedback has been adopted as a promising approach to improve the search performance. In this paper, we propose a novel idea of learning with historical relevance feedback log-data,and adopt a new methodology called“Collaborative Image Retrieval” (CIR). To effectively search the log data,we propose a novel semisupervised distance metric learning technique, called “Laplacian Regularized Metric Learning” (LRML), for learning robust distance metrics for CIR.Different from previous methods,the proposed LRML method integrates both log data and unlabeled data information through an effective graph regularization framework. We show that reliable metrics can be learned from real log data eventhey may be noisy and limited at the beginning stage of a CIR system. Keywordssemi-supervised learning ; Collaborative Image Retrieval ; semantic gap
Map generalisation technology: addressing the need for a common research platform

1 University of Zurich, Department of Geography, Winterthurerstrasse 109, 8057 Zurich, Switzerland, {aje,burg,weibel}@geo.unizh. ch 2 Institute for Cartography and Geoinformation, University of Bonn, Meckenheimer Allee 172, 53115, Bonn, Germany, bobzien@ikg.uni-bonn.de 3 National Land Survey of Sweden, SE-801 82 Gavle, Sweden, lars.harrie@lantm.lth. se 4 Finnish Geodetic Institute, P.O. Box 15, FIN-02431 Masala, Finland lassi.lehto@fgi.fi 5 Institute for Cartography and Geoinformatics, University of Hannover, Appelstrasse 9a, 30167, Hannover, Germany, monika.sester@ikg.uni-hannover.de 6 Department of Cartography, Technical University Munich, Arcisstrasse. 21, 80333 Munich, Germany, tumasch@bv.tum. de