Miracle-dz/UniFAR-FTUs huggingface dataset:
Children's Implicit Beliefs About Intelligence

Young children are commonly perceived as highly optimistic and confident, and therefore seldom arouse concern as to how they are impacted by academic failure. However, there is evidence to suggest that young children can indeed be negatively affected by failure experiences. Implicit theories of intelligence can provide individuals with a framework by which to perceive failure, though little is known about when these theories begin to develop. The current study explores whether children as young as three and a half to four years of age demonstrate patterns indicative of incremental or entity theories of intelligence as a response to challenge following failure. Children worked on a series of puzzles, some of which were impossible to solve. Children then chose puzzles to do again and provided reasons for their choices. Procedures were adapted from Smiley and Dweck (1994), with an added feedback condition of effort or ability. Descriptive comparisons confirmed the hypotheses that children would differ in their approach to challenge following failure; children who chose to approach challenge more often chose insoluble puzzles and those who avoided challenge more often chose soluble puzzles. In addition, challenge approach children expressed less performance concern, negative self-evaluation, and disengagement than children who avoided challenge. Finally, children who received ability related feedback more often chose soluble puzzles than those who received effort feedback. This study suggests that even at three and half years old, children react differently to achievement related information. It is possible that at this time, children are in the process of developing implicit theories of intelligence that could direct their future cognitions, affect, and behavior in the classroom. ACKNOWLEDGEMENTS First, I would like to thank my advisor, Dr. Kathleen Moritz Rudasill, for taking me under her wing in my second semester here and providing continued mentoring and support throughout my program. I also want to thank Dr. Roger Bruning for taking time out of his summer to be my second reader. Lastly, I'd like to thank Amanda Prokasky, program coordinator on the Toddler Sleep Study, for her tireless recruitment, without which my study would not have been possible.
Disconnected Operation in a Mobile Computation System

With the increasing use of mobile devices to access the Internet anytime and anywhere, the relevance of disconnected operation has emerged. Disconnected operation allows users to execute applications during temporary failures in networks or when they explicitly decide to work off-line. This paper presents a system, called Jamp, that uses mobile computation (or logical mobility) to handle disconnections. Jamp has abstractions supporting the migration of groups of objects and classes to other nodes of the network. In this way, programmers can pushapplications to clients with code and data that makes disconnected operation possible.
Modeling Fluid Resuscitation by Formulating Infusion Rate and Urine Output in Severe Thermal Burn Adult Patients: A Retrospective Cohort Study

Acute burn injuries are among the most devastating forms of trauma and lead to significant morbidity and mortality. Appropriate fluid resuscitation after severe burn, specifically during the first 48 hours following injury, is considered as the single most important therapeutic intervention in burn treatment. Although many formulas have been developed to estimate the required fluid amount in severe burn patients, many lines of evidence showed that patients still receive far more fluid than formulas recommend. Overresuscitation, which is known as “fluid creep,” has emerged as one of the most important problems during the initial period of burn care. If fluid titration can be personalized and automated during the resuscitation phase, more efficient burn care and outcome will be anticipated. In the present study, a dynamic urine output based infusion rate prediction model was developed and validated during the initial 48 hours in severe thermal burn adult patients. The experimental results demonstrated that the developed dynamic fluid resuscitation model might significantly reduce the total fluid volume by accurately predicting hourly urine output and has the potential to aid fluid administration in severe burn patients.
Deceased organ donation for transplantation: Challenges and opportunities

Organ transplantation saves thousands of lives every year but the shortage of donors is a major limiting factor to increase transplantation rates. To allow more patients to be transplanted before they die on the wait-list an increase in the number of donors is necessary. Patients with devastating irreversible brain injury, if medically suitable, are potential deceased donors and strategies are needed to successfully convert them into actual donors. Multiple steps in the process of deceased organ donation can be targeted to increase the number of organs suitable for transplant. In this review, after describing this process, we discuss current challenges and potential strategies to expand the pool of deceased donors.
Video-based toon character from surface performance capture

Recent advances in dynamic surface capture have made the creation of a realistic animation a promising task for modern visual media production such as computer generated movies or 3D cinematographic video games. Nonetheless, automatic creation of cartoon mesh animations, driven by real-life cues, is still a costly and time-consuming process and presents a number of hard technical challenges. To the best of our knowledge, we propose the first attempt at generating as-photorealistic-as possible cartoon animation from markerless surface performance capture. The purpose of synthesizing new puppetry animation demonstrating fidelity to the spirit of comic book style with more exaggerated motion while preserving extreme captured cloth wrinkles is difficult to achieve. Consequently, the key contribution of our work focuses on a novel cartoon stylization approach for 3D video that efficiently reuse temporally consistent dynamic surface sequence captured from real-world actor performance. In particular, our simple and effective algorithm converts realistic spatiotemporal captured surfaces and multi-view data into an exaggerated life-like squash-and-stretch shape evolution coupled with context-aware cartoon-style expressive rendering.
Environmental sex determination in a reptile varies seasonally and with yolk hormones

Most hypotheses that have been put forward in order to explain the persistence of environmental sex determination (ESD) in reptiles assume a relatively fixed association of sex with temperature–induced phenotype and no maternal influence on offspring sex. Here we demonstrate the association of maternally derived yolk hormone levels with the offspring sex ratio and describe two new aspects of temperaturedependent sex determination (TSD), i.e. seasonal variation in both thermal response and yolk steroid levels. Eggs from painted turtles (Chrysemys picta) were incubated at 28°C. The hatchling sex ratio at 28°C (i.e. the phenotypic reaction norm for sex at 28°C) shifted seasonally from ca. 72% male to ca. 76% female. Yolk oestradiol (E2) increased seasonally while testosterone (T) decreased. The proportion of males in a clutch decreased as E2 levels increased and the E2:T ratio increased. These new findings are discussed in relation to heritability and adaptive explanations for the persistence of ESD in reptiles. Maternally derived yolk hormones may provide a mechanism for the seasonal shift in the sex ratio which in turn may help explain the persistence of ESD in reptiles. They may also explain those clutches of other reptiles with TSD that fail to yield only males at maximally masculinizing conditions.
Fast parameter estimation for joint maximum entropy language models

This paper discusses efficient parameter estimation methods for joint (unconditional) maximum entropy language models such as whole-sentence models. Such models are a sound framework for formalizing arbitrary linguistic knowledge in a consistent manner. It has been shown that general-purpose gradient-based optimization methods are among the most efficient algorithms for estimating parameters of maximum entropy models for several domains in natural language processing. This paper applies gradient methods to whole-sentence language models and other domains whose sample spaces are infinite or practically innumerable and require simulation. It also presents Open Source software for easily fitting and testing joint maximum entropy models.
FCGlight: A System for Studying the Evolution of Natural Language

We defined FCGlight, a refined version of the Fluid Construction Grammar (FCG), which is a formalism for studying the evolution of the natural language. We picked a core subset of FCG, and expressed it in the semantic framework of the Order-Sorted Features (OSF) logic. This allows for efficient processing, and also gives FCG a solid formal background for further analysis and improvement. Inspired from the conception of LIGHT[5], a system for natural language processing with large scale unification grammars, we developed a prototype system which implements FCGlight and can conduct language evolution experiments in a multi-agent population. We proved the functionalities of this system by running a experiment which models the evolution of the Russian verb aspect.
Motion Constrained Simultaneous Localization and Mapping in Neighbourhood Environments

The ability to construct a consistent map whilst determining its pose is a prerequisite for mobile robot navigation. When operating in circuit-like trajectories (e.g. surveillance), acquiring large cyclic maps and closing these trajectories is very important. This paper presents the use of map-based a-priori knowledge for constraining the solution to the simultaneous localization and mapping problem. This is formulated in a Bayesian framework where a probabilistic model of the traversable environment (e.g. road network map) is used to minimise the accumulated errors in robot localization during cyclic mapping. The implementation uses an adaptive Rao-Blackwellized particle filter to optimise computation. The map-based priors are generated from a road network map that describes the robot traversable areas. The principle is to bind effectively the adaptive sample-based representation of the robot estimation using the probability map whilst detecting and mapping distinct features found at the roadsides. it allows the number of samples to be reduced and the particle depletion problem that compromises the robot closing large loops minimized. Extensive experiments were conducted using a vehicle test-bed travelling at 15Km/h in a University Campus road network. These have shown that our approach enables us to build cyclic maps and thus close the cycles (trajectories) in a much better manner than other existing methods.
Coping with Construals in Broad-Coverage Semantic Annotation of Adpositions

We consider the semantics of prepositions, revisiting a broad-coverage annotation scheme used for annotating all 4,250 preposition tokens in a 55,000 word corpus of English. Attempts to apply the scheme to adpositions and case markers in other languages, as well as some problematic cases in English, have led us to reconsider the assumption that a preposition's lexical contribution is equivalent to the role/relation that it mediates. Our proposal is to embrace the potential for construal in adposition use, expressing such phenomena directly at the token level to manage complexity and avoid sense proliferation. We suggest a framework to represent both the scene role and the adposition's lexical function so they can be annotated at scale---supporting automatic, statistical processing of domain-general language---and sketch how this representation would inform a constructional analysis.
Evaluating the Utility of Appraisal Hierarchies as a Method for Sentiment Classification

Recent studies of sentiment classification (determining whether a text is “positive” or “negative”) using Appraisal theory have provided mixed results. While some good results have been obtained, it is difficult to tell what aspects of Appraisal are particularly useful for this task. In this paper, we present a series of experiments to isolate features of Appraisal, in order to compare which parts aid the task of sentiment classification on movie reviews. We report results which on the surface challenge the utility of Appraisal Hierarchies for this task, when modelled using systemic features. However in the context of making a trade-off between coverage and scale of feature space, our results appear promising. We hence discuss the need for a balance between the size of a classifier’s structure and the overall accuracy.
Correlation-based steganalysis of modular data hiding methods

In this paper, we present a new steganalysis approach based on correlation and create four correlation-based steganalysis methods for the analysis of JPEG images. Our main goal is to evaluate the level of security provided by the modular data hiding methods developed by us and presented in our previous papers [2,3,5]. The new steganalysis methods rely on detecting increased noise levels in images containing embedded data by calculating different correlation-based metrics. The experimental results confirm that the modular data hiding methods provide excellent invisibility of the data to unauthorized third-parties.
Classification of Rice Grains Using Neural Networks

This paper presents a neural network approach for classification of rice varieties. A total of 9 different rice verities were considered for the study. Samples were drawn from each variety and images of seeds were captured. Algorithms were developed to extract thirteen morphological features, six colour features and fifteen texture features from colour images of individual seed samples. A different neural network models were developed for individual feature sets and for the combined feature set. High classification accuracy was given by textural features than morphological and colour features. An overall classification accuracy of 92% was obtained from combined feature model. Individual classification accuracies of AT307, BG250, BG358, BG450, BW262, BW267, W361, BW363 and BW364 were 94%, 98%, 84%, 100%, 94%, 68%, 98%, 94% and 94% respectively. It was noted that different neural network architectures tend to produce different accuracies.
General-purpose Spectrometer for Vacuum Breakdown Diagnostics for the 12 GHz Test Stand at CERN

We discuss a spectrometer to analyze the electrons and ions ejected from a high-gradient CLIC accelerating structure that is installed in the klystron-driven 12 GHz test-stand at CERN. The charged particles escaping the structure provide useful information about the physics of the vacuum breakdown within a single RF pulse. The spectrometer consists of a dipole magnet, a pepper-pot collimator, a fluorescent screen and a fast camera. This enables us to detect both transverse parameters such as the emittance and longitudinal parameters such as the energy distribution of the ejected beams. We can correlate these measurements with e.g. the location of the breakdown inside the structure, by using information from the measured RF powers, giving in that way a complete picture of the vacuum breakdown phenomenon. The spectrometer was installed during Spring 2014 and will be commissioned during Summer 2014.
Refining semi-automatic parallel corpus creation for Zulu to English statistical machine translation

Although their use in training quality machine translation systems has been proven, parallel corpora — large collections of translated texts — are generally hard to come by for the majority of languages. To counteract this fact, a relatively small collection may be processed in more depth by further cleaning and more accurately splitting and aligning the texts. We apply this to an existing English/Zulu parallel corpus that has been used for statistical machine translation experiments. After these preprocessing steps, we run the same experiments for comparative purposes. Our results suggest that compatibility of bitexts, the choice of sentence splitters used on different parts of the text, as well as manual work, may have a notable effect on both the corpus size and on automatic translation quality.
License Plate Extraction Based on Vertical Edge Detection and Mathematical Morphology

License plate recognition plays an important role in Intelligent Transport System; however, plate region extraction is the key step before the final recognition. In this paper, an effective license plate extraction algorithm based on vertical edge detection and mathematical morphology is proposed, which can exactly extract the plate from complex background. The proposed algorithm mainly consists of two modules: license plate region's rough detection and license plate accurate detection. The former aims to find out Region of Interest (ROI) through a sequence of steps including vertical edge detection, edge detection and mathematical morphology, while the latter analyzes the ROI by taking full advantage of outer shape feature and inner texture feature of license plate. Experiments have been conducted for this algorithm.400 images taken from various scenes were employed, including diverse angles, different lightening conditions, etc. The proposed algorithm can detect the region of license plate quickly with 98.1 percent accuracy.
Using Low-Resolution Palmprint Images and Texture Analysis for Personal Identification

Biometrics identification is an emerging technology for solving security problems in our networked society. A new branch of biometric technology, palmprint recognition, whereby the lines and points can be extracted from our palm for personal identification was proposed several years ago [1-6]. In this paper, we implement the feature extraction technique applied to iris recognition [7] on low – resolution palmprint images. A 2-D Gabor filter is used to obtain the texture information and two palmprint images are compared in term of their hamming distance. The experimental results show that our method is effective.
A Meta-model for Non-functional Property Descriptions of Web Services

In this paper we propose a meta-model for nonfunctional property descriptions targeted to support the selection of Web Services. The approach is based on the explicit distinction between NFP offered by providers and requested by users, on the concept of policy that aggregates NFP descriptions into single entities with an applicability condition, and finally on a set of constraint operators, which is particularly relevant for NFP requests. The semantic meta-model embracing the above perspective is defined by a BNF syntax whose semantics is formalized by an ontology. The ontology has been formalized in OWL-DL and WSML to provide for logical syntax. The logic upon which the meta-model supports NFP-based selection is discussed in the paper.
Including the Excluded

This paper describes the use of music therapy in supporting adolescents from a deprived, inner-city background with social, emotional and behavioural difficulties (SEBD) and draws on the author's work with adolescents in SEBD schools and pupil referral units (PRUs). The complex needs of this client group are outlined in terms of social, cultural, developmental and organic factors. The difficulties they face in accessing traditional National Health Service-based psychotherapy are described and ways of providing a more inclusive therapy service are suggested including the use of music therapy, providing therapy in schools rather than in mental health clinics and thinking about the nature of the therapeutic relationship being offered. The need to ensure that music therapy practice adapts to the cultural needs of such clients is also explored with a focus on the use of music technology and rap. A case study is used to illustrate the approach used by the author.
Stabilization of Furuta Pendulum: A backstepping based hierarchical sliding Mode approach with disturbance estimation

An adaptive backstepping sliding mode controller, which combines both the merits of adaptive backstepping control and sliding mode control, is proposed to address the control problem of the Furuta Pendulum in the presence of external disturbances. At first, the underactuated state model of the Furuta Pendulum has been divided into two separate subsystems. Then a pair of first layer sliding surfaces is defined for each second-order subsystem. Two separate adaptive backstepping based control law is designed for each of the subsystem to ensure the states of each subsystem approach to their own sliding mode surface. Based on this two first layer sliding surfaces, a second layer sliding surface is defined correspondingly, through which a total control law is derivedto make sure that all states can converge to their desired value via Lyapunov stability theorem. The asymptotic stability of all the sliding surfaces has proven theoretically, and simulation results show the controller's validity and its adaptive abilities for all kinds of extraneous disturbances.
LISREL ve AMOS Programlari Kullanilarak Gerçekleştirilen Yapisal Eşitlik Modeli (YEM) Analizlerine İlişkin Sonuçlarin Karşilaştirilmasi Comparing the Analysis Results of the Structural Equation Models (SEM) Conducted Using LISREL and AMOS

This study aimed to compare the analysis results obtained through LISREL and AMOS for the models of path analysis, Confirmatory Factor Analysis (CFA) and structural regression, which are within structural equation model and differ in levels of fit. Therefore, population and sample were not needed in the study. The study was conducted on three different data sets that reflected the models through a data file. The data set used in the path analysis was determined to reflect a low fit model, while the one used in CFA was determined to reflect an acceptable fit model. However, the data set used in the structural model reflected a good fit. In this way, it was believed that it would be possible to find an answer to the question of whether the differences in the fit indexes obtained through LISREL and AMOS were affected by the fit level of the model analyzed. The analysis results indicated that the fit indexes obtained through LISREL and AMOS were substantially similar in the data set that reflected a good fit. The differences in the fit indexes obtained through these two software packages were found to be larger in the model that reflected a low fit between the model and the data set. It was also found that this difference was remarkable, particularly in χ 2 /sd, NNFI and RFI indexes. These results indicate that the differences in the fit indexes reported by LISREL and AMOS are affected by the fit level of the model.
Study regarding the survival of patients suffering a traumatic cardiac arrest

Severe trauma is the most frequent cause of death in young people, in civilized countries with major social and vital costs. The speed of diagnostic decision making and the precocity of treatment approaches are both essential and depend on the specialists’ colaboration. The present study aims to emphasize the actual situation of medical interventions in case of cardiorespiratory arrest due to trauma. 1387 patients who suffered a cardio respiratory arrest both traumatic and non-traumatic were included in order to point out the place of traumatic arrest. Resuscitation of such patients is considered useless and resource consumer by many trauma practitioners who are reporting survival rates of 0%-3.5%. As the determinant of lesions, trauma etiology was as it follows car accidents – 43%, high falls – 30%, suicidal attempts – 3%, domestic violence – 3%, other causes – 21%. Hypovolemia remains the major cause of cardiac arrest and death and that is why the efforts of emergency providers (trauma team) must be oriented towards “hidden death” in order to avoid it. This condition could be revealed and solved easier with minimal diagnostic and therapeutic maneuvers in the emergency department.