Miracle-dz/UniFAR-FTUs huggingface dataset:
The Exploration of a Selection of Conative Constructs Relevant to Learning and Performance

The term "conative" is used to describe constructs that span both motivational and volitional aspects of human behavior, distinguished from constructs that emphasize cognition and affection. Among the conative constructs are achievement strivings, beliefs about self-esteem and self-efficacy, interests and attitudes about learning, volitional aspects, and constructs related to self-regulation and mindfulness. This pilot research project was conducted to gain familiarity with various instruments used to measure conative constructs and to gain preliminary understanding of interrelationships among the conative constructs measured by the instruments. Sixty college students completed measures of approaches to learning and studying, mindfulness, action versus state orientation, and mastery versus performance orientation. On the whole, these measures demonstrated reasonably good psychometric properties and represented a good preliminary approach to research on the conative constructs. Of particular interest was the high correlation between mindfulness and the deep approach. (Contains 24 references.) (SLD) *********************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. * *********************************************************************** U.S. DEPARTMENT OF EDUCATION Office 0 ducabonat Research and Improvement EOU TIONAL RESOURCES INFORM" ION CENTER (ERIC) This document has been reProducad as received from the person Or organization originating it 0 Minor changes have been made to improve reproduction quality Points of view or opinions staled in this doer). went do not necessarily represent official OE RI position or policy National Center for Research on Evaluation, Standards, and Student Testing Final Deliverable August 1994 Project 2.3 Complex Performance Assessments: Expanding the Scope and Approaches to Assessment Defining Conative Construct The Exploration of a Selection of Conative Constructs Relevant to Learning and Performance Richard Snow, Project Director CRESST/Stanford University U.S. Department of Education Office of Educational Research and Improvement Grant No. R117G10027 CFDA'Catalog No. 84.117G National Center for Research on Evaluation, Standards, and Student Testing (CRESST) Graduate School of Education University of California, Los Angeles Los Angeles, CA 90024-1522 (310) 206-1532 4. BEST COPY AVAILABLE The work reported herein was supported under the Educational Research and Development Center Program, cooperative agreement number R137G10027 and CFDA catalog number 84.117G, as administered by the Office of Educational Research and Improvement, U.S. Department of Education. The findings and opinions expressed in this report do not reflect the position or policies of the Office of Educational Research and Improvement or the U.S. Department of Education. Program Two, Project 2.3 1 THE EXPLORATION OF A SELECTION OF CONATIVE CONSTRUCTS RELEVANT TO LEARNING AND PERFORMANCE
Cerebral Artery Computational Fluid Dynamics of Intracranial Artery Using 3-Dimensional Angiography : Potentials and Technical Considerations 1 3

Computational fluid dynamics (CFD) is an important field in mechanical engineering. Since the 1950s, CFD has been widely utilized in various fields, not only in mechanical engineering, but also in aeromechanics, marine engineering, civil engineering, meteorological dynamics, environmental engineering, nucleonic and medical engineering. Navier-Stokes’ equation is usually used for the analysis of viscous and compressible fluid, such as blood (1). With the advance of computational science, a non-linear Navier-Stokes’ (NS) equation is easy to put into practice in CFD analysis instead of the linear NS equation, which converts the variable boundary conditions as simple constants. In contrast with the CFD assumption that the vessel wall is rigid and immovable, the fluid structure interaction, which is an integration of CFD with elastic walls such as human bodies, is also of interest in forensic medicine and medical engineering. In particular, fluid dynamics of blood flow is relatively familiar to physicians and vascular surgeons, because it is usually used in studies on atherosclerosis or cerebral aneurysm. Recently, CFD studies were published not only in the biomedical fields of which engineers comprise of the largest part, but also in the clinical fields where physicians play major roles. In many bio-medical journals, researchers have published papers regarding fluid dynamics associated with atherosclerosis and intracranial aneurysms. These studies were well-qualified techniCase Report pISSN 1738-2637 / eISSN 2288-2928 J Korean Soc Radiol 2013;69(1):5-10 http://dx.doi. org/10.3348/jksr.2013. 69.1. 5
Extracting Conceptual Knowledge from Natural Language Text Using Maximum Likelihood Principle

Domain-specific knowledge graphs constructed from natural language text are ubiquitous in today's world. In many such scenarios the base text, from which the knowledge graph is constructed, concerns itself with practical, on-hand, actual or ground-reality information about the domain. Product documentation in software engineering domain are one example of such base texts. Other examples include blogs and texts related to digital artifacts, reports on emerging markets and business models, patient medical records, etc. Though the above sources contain a wealth of knowledge about their respective domains, the conceptual knowledge on which they are based is often missing or unclear. Access to this conceptual knowledge can enormously increase the utility of available data and assist in several tasks such as knowledge graph completion, grounding, querying, etc. Our contributions in this paper are twofold. First, we propose a novel Markovian stochastic model for document generation from conceptual knowledge. The uniqueness of our approach lies in the fact that the conceptual knowledge in the writer's mind forms a component of the parameter set of our stochastic model. Secondly, we solve the inverse problem of learning the best conceptual knowledge from a given document, by finding model parameters which maximize the likelihood of generating the specific document over all possible parameter values. This likelihood maximization is done using an application of Baum-Welch algorithm, which is a known special case of Expectation-Maximization (EM) algorithm. We run our conceptualization algorithm on several well-known natural language sources and obtain very encouraging results. The results of our extensive experiments concur with the hypothesis that the information contained in these sources has a well-defined and rigorous underlying conceptual structure, which can be discovered using our method.
ContCap: A comprehensive framework for continual image captioning

While cutting-edge image captioning systems are increasingly describing an image coherently and exactly, recent progresses in continual learning allow deep learning systems to avoid catastrophic forgetting. However, the domain where image captioning working with continual learning is not exploited yet. We define the task in which we consolidate continual learning and image captioning as continual image captioning. In this work, we propose ContCap, a framework continually generating captions over a series of new tasks coming, seamlessly integrating continual learning into image captioning accompanied by tackling catastrophic forgetting. After proving catastrophic forgetting in image captioning, we employ freezing, knowledge distillation, and pseudo-labeling techniques to overcome the forgetting dilemma with the baseline is a simple fine-tuning scheme. We split MS-COCO 2014 dataset to perform experiments on incremental tasks without revisiting dataset of previously provided tasks. The experiments are designed to increase the degree of catastrophic forgetting and appraise the capacity of approaches. Experimental results show remarkable improvements in the performance on the old tasks, while the figure for the new task remains almost the same compared to fine-tuning. For example, pseudo-labeling increases CIDEr from 0.287 to 0.576 on the old task and 0.686 down to 0.657 BLEU1 on the new task.
Full-scale i ple entation of an advanced control syste on a biological aste ater treat

The implementation of a multivariable control package for the real-time control and supervision of a biological wastewater treatment plant is reported and discussed. The goal is to improve the plant operation in terms of effluent quality and operational costs. A dynamic matrix control algorithm is put into operation for controlling the ammonia concentration at the end the biological reactor in the activated sludge process. The status of the plant instrumentation is continuously monitored by multivariate statistical technology based on moving windows principal component analysis. Results of the first six months of continuous operation show the ability of the designed predictive control in reducing the aeration energy consumption whilst keeping the ammonia concentration in the effluent within the limits.
Search for strong galaxy-galaxy lensing in SDSS-III BOSS

Strong lensing is one of the most spectacular views in the universe. Many cosmological applications have been proposed, but the number of such lensing systems is still limited. In this work, we applied an improved version of a previously developed spectroscopic lensing search method to the SDSS-III BOSS and proposed a list of highly possible candidates. Follow-up CFHT Megacam imaging observations were performed for five systems, and two out of five are probably strong lensing systems with at least one image close to the central galaxy, although no counter images are detected.
Significant predictors of college band participation by college freshmen with high school

3. When a map, drawing or chart, e tc . , was part o f the material being photographed the photographer followed a definite m ethod in "sectioning" the material. It is custom ary to begin photoing a t the upper left hand corner of a large sheet and to continue photoing from left to right in equal sections with a small overlap. If necessary, sectioning is continued again — beginning below the first row and continuing on until complete.
Additive Schwarz solvers and preconditioners for the closest point method

The discretization of surface intrinsic elliptic partial differential equations (PDEs) pose interesting challenges not seen in flat space. The discretization of these PDEs typically proceeds by either parametrizing the surface, triangulating the surface, or embedding the surface in a higher dimensional flat space. The closest point method (CPM) is an embedding method that represents surfaces using a function that maps points in the embedding space to their closest points on the surface. In the CPM, this mapping also serves as an extension operator that brings surface intrinsic data onto the embedding space, allowing PDEs to be numerically approximated by standard methods in a narrow tubular neighborhood of the surface. We focus here on numerically approximating the positive Helmholtz equation, $\left(c-\Delta_{\mathcal{S}}\right)u=f,~c\in\mathbb{R}^+$ by the CPM. This yields a large, sparse, and nonsymmetric system to solve. Herein, we develop restricted additive Schwarz (RAS) and optimized restricted additive Schwarz (ORAS) solvers and preconditioners for this discrete system. In particular, we develop a general strategy for computing overlapping partitions of the computational domain, as well as defining the corresponding Dirichlet and Robin transmission conditions. We demonstrate that the convergence of the ORAS solvers and preconditioners can be improved by using a modified transmission condition where more than two overlapping subdomains meet. Numerical experiments are provided for a variety of analytical and triangulated surfaces. We find that ORAS solvers and preconditioners outperform their RAS counterparts, and that using domain decomposition as a preconditioner gives faster convergence over using it as a solver. The methods exhibit good parallel scalability over the range of process counts tested.
Statistical Methods for Testing Functional Divergence after Gene Duplication

Functional innovations after gene duplication may result in altered functional constraints between member gene clusters of a gene family. This type (type I) of functional divergence is measured by the coefficient of functional divergence (ul), which can be interpreted as the decrease in rate correlation between gene clusters, or the probability that the evolutionary rate at a site is statistically independent between two gene clusters. A simple stochastic model has been developed for estimating ul and testing its statistical significance. The current model includes the model of rate variation among sites as a special case when ul 5 0. Moreover, we have developed a site-specific profile based on the hidden Markov model to identify critical amino acid residues that are responsible for these functional differences between two gene clusters, which may have great potential in functional genomics.
Kinetic Song Comprehension: Deciphering Personal Listening Habits via Phone Vibrations

Music is an expression of our identity, showing a significant correlation with other personal traits, beliefs, and habits. If accessed by a malicious entity, an individual's music listening habits could be used to make critical inferences about the user. In this paper, we showcase an attack in which the vibrations propagated through a user's phone while playing music via its speakers can be used to detect and classify songs. Our attack shows that known songs can be detected with an accuracy of just under 80%, while a corpus of 100 songs can be classified with an accuracy greater than 80%. We investigate such questions under a wide variety of experimental scenarios involving three surfaces and five phone speaker volumes. Although users can mitigate some of the risk by using a phone cover to dampen the vibrations, we show that a sophisticated attacker could adapt the attack to still classify songs with a decent accuracy. This paper demonstrates a new way in which motion sensor data can be leveraged to intrude on user music preferences without their express permission. Whether this information is leveraged for financial gain or political purposes, our research makes a case for why more rigorous methods of protecting user data should be utilized by companies, and if necessary, individuals.
Global Optimality in Separable Dictionary Learning with Applications to the Analysis of Diffusion MRI

Sparse dictionary learning is a popular method for representing signals as linear combinations of a few elements from a dictionary that is learned from the data. In the classical setting, signals are represented as vectors and the dictionary learning problem is posed as a matrix factorization problem where the data matrix is approximately factorized into a dictionary matrix and a sparse matrix of coefficients. However, in many applications in computer vision and medical imaging, signals are better represented as matrices or tensors (e.g. images or videos), where it may be beneficial to exploit the multi-dimensional structure of the data to learn a more compact representation. One such approach is separable dictionary learning, where one learns separate dictionaries for different dimensions of the data. However, typical formulations involve solving a non-convex optimization problem; thus guaranteeing global optimality remains a challenge. In this work, we propose a framework that builds upon recent developments in matrix factorization to provide theoretical and numerical guarantees of global optimality for separable dictionary learning. We propose an algorithm to find such a globally optimal solution, which alternates between following local descent steps and checking a certificate for global optimality. We illustrate our approach on diffusion magnetic resonance imaging (dMRI) data, a medical imaging modality that measures water diffusion along multiple angular directions in every voxel of an MRI volume. State-of-the-art methods in dMRI either learn dictionaries only for the angular domain of the signals or in some cases learn spatial and angular dictionaries independently. In this work, we apply the proposed separable dictionary learning framework to learn spatial and angular dMRI dictionaries jointly and provide preliminary validation on denoising phantom and real dMRI brain data.
On the randomised stability constant for inverse problems

In this paper we introduce the randomised stability constant for abstract inverse problems, as a generalisation of the randomised observability constant, which was studied in the context of observability inequalities for the linear wave equation. We study the main properties of the randomised stability constant and discuss the implications for the practical inversion, which are not straightforward.
On Efficient Multilevel Clustering via Wasserstein Distances

We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a potentially large hierarchically structured corpus of data. Our method involves a joint optimization formulation over several spaces of discrete probability measures, which are endowed with Wasserstein distance metrics. We propose several variants of this problem, which admit fast optimization algorithms, by exploiting the connection to the problem of finding Wasserstein barycenters. Consistency properties are established for the estimates of both local and global clusters. Finally, the experimental results with both synthetic and real data are presented to demonstrate the flexibility and scalability of the proposed approach.
Classi fi cation of MRI Migraine Medical Data using 3 D Convolutional Neural Network �

While statistical approaches are being implemented in medical data analyses because of their high accuracy and efficiency, the use of deep learning computations can potentially provide out-of-the-box insights, especially when statistical approaches did not yield a good result. In this paper we classify migraine and non-migraine magnetic resonance imaging (MRI) data, using a deep learning method named convolutional neural network (CNN). 198 MRI scans, which were obtained equally from both data groups, resulted in the maximum classification test accuracy of 85% (validation accuracy: x̄=0.69, σ=0.06), compared to the baseline statistical accuracy of 50%. We then used class activation mapping (CAM) method to visualize brain regions that the CNN model took to distinguish one data group from the other and the visualization pointed at the parietal lobe, corpus callosum, brain stem and anterior cingulate cortex, of which the brain stem was mentioned in the medical findings for white matter abnormalities. Our findings suggest that CNN and CAM combined can be a useful image-based data analysis tool to add inspiration or discussion in the medical problem-solving process.
Data on association between QRS duration on prehospital ECG and mortality in patients with confirmed STEMI

Data presented in this article relates to the research article entitled “Association between QRS duration on prehospital ECG and mortality in patients with suspected STEMI” (Hansen et al., in press) [1]. Data on the prognostic effect of automatically recoded QRS duration on prehospital ECG and presence of classic left and right bundle branch block in 1777 consecutive patients with confirmed ST segment elevation AMI is presented. Multivariable analysis, suggested that QRS duration 4111 ms, left bundle branch block and right bundle branch block were independent predictors of 30 days all-cause mortality. For interpretation and discussion of these data, refer to the research article referenced above. & 2017 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). ier Inc. This is an open access article under the CC BY license /j.ijcard. 2017.07. 049 R. Hansen et al. / Data in Brief 15 (2017) 12–17 13 Specifications Table
Collaborative detection and filtering techniques against denial of service attacks in cloud computing

Nowadays, cloud computing technology is experiencing a fastest growing in terms services demand and number of cloud clients which make the business organizations against a critical issue must be addressed ”How to Secure Cloud Data Center (CDC)”. As result, this major challenge has attracted the attention of several research works. The attacker is looking for unavailability of service, dysfunctioning of resources and maximization of financial loss costs. There are many types of attack such as Denial of service (DoS) and Distributed Denial of Service (DDoS) where the key objective for the attacker is to cause an overloading of the system network. They seek to send through a victim server a huge size of data as flooding packets so as to block and prevent the users to be served. This paper introduced a defending system for DoS attack mitigation in CDC environment. Generally, it discussed the different techniques of DoS attacks and its countermeasures as well proactive filtering and detection mechanisms. Consequently, to validate our proposed solution, we have implemented our analytical model in Discrete Event Simulator. The proposed mathematical model considers many performance parameters including response time, throughput, drop rate, resource computing utilization, and mean waiting time in the system, mean number of legitimate clients in the system when varying the attack arrival rate. Indeed, we have estimated the incurred cost from the attack. Implementing performance analysis using queueing theory and simulation experiments, the proposed solution would improve the flexibility and accuracy of DoS attack prevention, and would obviously make the cloud computing environment more secured.
BPMR: Bayesian Probabilistic Multivariate Ranking

Multi-aspect user preferences are attracting wider attention in recommender systems, as they enable more detailed understanding of users' evaluations of items. Previous studies show that incorporating multi-aspect preferences can greatly improve the performance and explainability of recommendation. However, as recommendation is essentially a ranking problem, there is no principled solution for ranking multiple aspects collectively to enhance the recommendation. In this work, we derive a multi-aspect ranking criterion. To maintain the dependency among different aspects, we propose to use a vectorized representation of multi-aspect ratings and develop a probabilistic multivariate tensor factorization framework (PMTF). The framework naturally leads to a probabilistic multi-aspect ranking criterion, which generalizes the single-aspect ranking to a multivariate fashion. Experiment results on a large multi-aspect review rating dataset confirmed the effectiveness of our solution.
Spoken Speech Enhancement using EEG

In this paper we demonstrate spoken speech enhancement using electroencephalography (EEG) signals using a generative adversarial network (GAN) based model and Long short-term Memory (LSTM) regression based model. Our results demonstrate that EEG features can be used to clean speech recorded in presence of background noise.
Chapter 1 An Introduction to Privacy-Preserving Data Mining

The field of privacy has seen rapid advances in recent years because of the increases in the ability to store data. In particular, recent advances in the data mining field have lead to increased concerns about privacy. While the topic of privacy has been traditionally studied in the context of cryptography and information-hiding, recent emphasis on data mining has lead to renewed interest in the field. In this chapter, we will introduce the topic of privacy-preserving data mining and provide an overview of the different topics covered in this book.