Miracle-dz/UniFAR-FTUs huggingface dataset:
Activity-Based Intervention and Developmentally Appropriate Practice

In the current controversy over the appropriateness of using developmentally appropriate practice (DAP) with young children with disabilities, the philosophies of early childhood special education (ECSE) and early childhood education (ECE) have frequently been characterized as incompatible. This article contrasts the differences between the two disciplines and discusses a service delivery approach, known as activity-based intervention (ABI). Combining strategies from both ECSE and ECE, this model utilizes behavior analytic techniques within child-directed activities. Although ABI and DAP appear to be compatible in many ways, DAP's emphasis on emotional development creates a dilemma. In part, because emotional development is difficult to evaluate, it has not been a focus of ECSE. This dilemma, as well as its implications for intervention, is discussed. It is argued that the continued comparison and integration of the philosophies of ECE and ECSE may enhance education for all young children.
Chronic desipramine treatment rescues depression‐related, social and cognitive deficits in Engrailed‐2 knockout mice

Engrailed‐2 (En2) is a homeobox transcription factor that regulates neurodevelopmental processes including neuronal connectivity and elaboration of monoaminergic neurons in the ventral hindbrain. We previously reported abnormalities in brain noradrenergic concentrations in En2 null mutant mice that were accompanied by increased immobility in the forced swim test, relevant to depression. An EN2 genetic polymorphism has been associated with autism spectrum disorders, and mice with a deletion in En2 display social abnormalities and cognitive deficits that may be relevant to multiple neuropsychiatric conditions. This study evaluated the ability of chronic treatment with desipramine (DMI), a selective norepinephrine (NE) reuptake inhibitor and classical antidepressant, to reverse behavioral abnormalities in En2−/− mice. Desipramine treatment significantly reduced immobility in the tail suspension and forced swim tests, restored sociability in the three‐chambered social approach task and reversed impairments in contextual fear conditioning in En2−/− mice. Our findings indicate that modulation of brain noradrenergic systems rescues the depression‐related phenotype in En2−/− mice and suggest new roles for NE in the pathophysiology of the social and cognitive deficits seen in neuropsychiatric disorders such as autism or schizophrenia.
Characterization of the surfactin synthetase C-terminal thioesterase domain as a cyclic depsipeptide synthase.

The C-terminal thioesterase domain of the nonribosomal peptide synthetase producing the lipopetide surfactin (Srf TE) retains autonomous ability to generate the cyclic peptidolactone skeleton of surfactin when provided with a soluble beta-hydroxy-butyryl-heptapeptidyl thioester substrate. Utilizing the recently solved crystal structure [Bruner, S. D. , et al. (2002) Structure 10, 301-310], the active-site nucleophile, Ser80, was changed to Cys, and the other members of the catalytic triad, Asp107 and His207, were changed to Ala, with the resulting mutants lacking detectable activity. Two cationic side chains in the active site, Lys111 and Arg120, were changed to Ala, causing an increased partitioning of the product to hydrolysis, as did a P26G mutant, mimicking the behavior of lipases. To evaluate recognition elements in substrates used by Srf TE, alterations to the fatty acyl group, the heptapeptide, and the thioester leaving group were made, and the resulting substrates were characterized for kinetic competency and flux of product to cyclization or hydrolysis. Alterations that could be accepted for cyclization were identified in all three parts of the substrate, although tolerance limits for changes varied. In addition, cocrystal structures of Srf TE with dipeptidyl boronate inhibitors were solved, illustrating the critical binding determinants of the substrate. On the basis of the structures and biochemical data, the cyclizing conformation of the surfactin peptide was modeled into the enzyme active site.
XML document design via GN-DTD

Designing a well-structured XML document is important for the sake of readability and maintainability. More importantly, this will avoid data redundancies and update anomalies when maintaining a large quantity of XML based documents. In this paper, we propose a method to improve XML structural design by adopting graphical notations for Document Type Definitions (GN-DTD), which is used to describe the structure of an XML document at the schema level. Multiples levels of normal forms for GN-DTD are proposed on the basis of conceptual model approaches and theories of normalization. The normalization rules are applied to transform a poorly designed XML document into a welldesigned based on normalized GN-DTD, which is illustrated through examples.
DTV-Based Melody Cutting for DTW-Based Melody Search and Indexing in QbH Systems

Melody analysis is an important processing step in several areas of Music Information Retrieval (MIR). Processing the pitch values extracted from raw input audio signal may be computationally complex as it requires substantial effort to reduce the uncertainty resulting i.a. from tempo variability and transpositions. A typical example is the melody matching problem in Query-by-Humming (QbH) systems, where Dynamic Time Warping (DTW) and note-based approaches are typically applied. In this work we present a new, simple and efﬁcient method of investigating the melody content which may be used for approximate, preliminary matching of melodies irrespective of their tempo and length. The proposed so-lution is based on Discrete Total Variation (DTV) of the melody pitch vector, which may be computed in linear time. We demonstrate its practical application for ﬁnding the appropriate melody cutting points in the R ∗ -tree-based DTW indexing framework. The experimental validation is based on a dataset of 4431 queries and over 4000 template melodies, constructed specially for testing Query-by-Humming systems.
Measuring Parallelism in Computation-Intensive Scientific/Engineering Applications

Describes COMET, (concurrency measurement tool), a software tool for measuring parallelism in large scientific/engineering applications. The proposed tool measures the total parallelism present in programs, filtering out the effects of communication/synchronization delays, finite storage, limited number of processors, the policies for management of processors and storage, etc. Although an ideal machine that can exploit the total parallelism is not realizable, such measures would aid the calibration and design of various architectures/compilers. The proposed software tool accepts ordinary Fortran programs as input. Therefore, parallelism can be measured easily on many fairly big programs. Some measurements for parallelism obtained with the help of this tool are also reported. It is observed that the average parallelism in the chosen programs is in the range of 500-3500 Fortran statements executing concurrently in each clock cycle in an idealized environment. >
ModeS TimeBank: A Modern Spanish TimeBank Corpus

Con el objetivo de representar y analizar grandes cantidades de fuentes historicas textuales en un Sistema de Informacion Geografica (SIG), se ha creado ModeS TimeBank. ModeS TimeBank es un corpus del espanol moderno (s. XVIII) anotado con informacion semantica temporal, eventiva y espacial, donde destaca el uso de los lenguajes de marcado TimeML y SpatialML. El corpus es ademas relevante no solo por su datacion e idioma sino por su dominio ya que esta enmarcado en la tematica de las redes de cooperacion. El presente articulo pretende describir como se ha creado el corpus y que criterios se han tenido en cuenta en su creacion, ademas de senalar el alcance y las aplicaciones de ModeS TimeBank.
Deep learning of feature representation with multiple instance learning for medical image analysis

This paper studies the effectiveness of accomplishing high-level tasks with a minimum of manual annotation and good feature representations for medical images. In medical image analysis, objects like cells are characterized by significant clinical features. Previously developed features like SIFT and HARR are unable to comprehensively represent such objects. Therefore, feature representation is especially important. In this paper, we study automatic extraction of feature representation through deep learning (DNN). Furthermore, detailed annotation of objects is often an ambiguous and challenging task. We use multiple instance learning (MIL) framework in classification training with deep learning features. Several interesting conclusions can be drawn from our work: (1) automatic feature learning outperforms manual feature; (2) the unsupervised approach can achieve performance that's close to fully supervised approach (93.56%) vs. (94.52%); and (3) the MIL performance of coarse label (96.30%) outweighs the supervised performance of fine label (95.40%) in supervised deep learning features.
The Cognitive Processes in Informal Reasoning

Two experiments investigated the factors that people consider when evaluating informal arguments in newspaper and magazine editorials. Experiment 1 showed that subjects were more likely to object to the truth of the premises and the conclusions of an argument than to the strength of the link between them. Experiment 1 also revealed two manipulations that helped subjects object to the link between premises and conclusions: rating how well the premises support the conclusions and rating the believability of the premises and conclusions. Experiment 2 further demonstrated that subjects who identified the premises and conclusions of an argument were better at formulating objections to the link between premises and conclusions. Moreover, subjects in Experiment 2 were better and faster at formulating objections to the truth of the premises and conclusions than to the link between premises and conclusions. The results are discussed in terms of the constraints they pose for developing a cognitive theory of informa.
An Empirical Test of Mnemonic Devices to Improve Learning in Elementary Accounting

The author empirically examined the use of mnemonic devices to enhance learning in first-year accounting at university. The experiment was conducted on three groups using learning strategy application as between participant's factors. The means of the scores from pre- and posttests were analyzed using the student t test. No significant difference was found between the groups for the pretest; however, both treatment groups performed significantly better in the posttest than did the control group. The findings support the literature that mnemonic devices can accelerate the rate at which new information is acquired and improve formal reasoning. A model is introduced for assessing the characteristics which affect memory recall.
Delay Analysis and Performance Evaluation of a Guaranteed QoS Provisioning Mechanism for Real-Time Traffic in IEEE 802.16e BWA Systems

Nowadays, multimedia applications are expected to be an important traffic source in wireless networks. Such sources usually have stringent delay requirements and in many cases they should be delivered exactly as they were generated. In this paper, we propose a novel pulling based uplink packet scheduling policy for rtPS/ertPS (real-time polling service/extended real-time polling service) traffic to support real-time applications, with strict delay requirements, such as VBR (variable bit rate) traffic in IEEE 802.16e BWA (broadband wireless access) systems. The proposed transmit-permission policy can derive sufficient conditions such that all the rtPS/ertPS sources satisfy their time constraints to provide deterministic QoS (quality of service) guarantees. In addition to theoretical analysis, simulations are conducted to evaluate the performance of the proposed scheme. As it turns out, our design provides a good quality performance in the IEEE 802.16e BWA systems.
Effects of an advanced mathematics education graduate program on teacher practice

EFFECTS OF AN ADVANCED MATHEMATICS EDUCATION GRADUATE PROGRAM ON TEACHER PRACTICE by Mickey Newman Washburn, Jr. Public concern over the mathematical incompetence of students and adults is longstanding and justified. The No Child Left Behind act has affected the nation’s teachers, their school systems, and their communities. The act required all classrooms have a “highly-qualified teacher” by June, 2006 (United States Department of Education, 2002). Thus, the purpose of this evaluative case study was to understand if the unique National Board Certification (NBC) focused Educational Specialist (EdS) program was effective in creating change in teacher practice of six high school mathematics teachers in a suburban Georgia county. The learning outcomes of the program and perceptions of self-efficacy were evaluated and used as guidelines for the effectiveness of the program. The study was grounded in theories of metacognition, social constructivism, and selfefficacy. Metacognition provided the basis for “thinking about thinking” (McApline, Weston, et al, 1999) but reflection expanded the thought process to thinking about thinking or actions. Reflections were an integral for each of the constructs of the EdS program and this dissertation. Data for the study included written teacher reflections, action research projects, and mentoring manuals; in addition to interviews three years after the program. Data were coded and analyzed through a process of constant comparison using the NVivo 7 software. The findings at each stage of analysis, which were halfway through the program, end of the program, and three years after the program, indicate the five constructs metacognition, social constructivism, self-efficacy, community of learners, and action research were common across data sets. Four of the five constructs became more prevalent at each stage of analysis with only action research peaking prior to the third stage. The patterns developed during the study indicated long-term change in teacher practice and these constructs solidified as part of their teaching philosophy. EFFECTS OF AN ADVANCED MATHEMATICS EDUCATION GRADUATE PROGRAM ON TEACHER PRACTICE By Mickey N. Washburn, Jr.
kTC - Robust and Adaptive Wireless Ad-Hoc Topology Control

Topology control for Wireless Sensor Networks (WSN) is a frequently tackled challenge, for which no satisfying general solution for realistic deployments has been found to the current day. Aiding to minimize unnecessary transmissions, it nevertheless represents a crucial function of WSN, in the light of their pursuit of efficiency. kTC is a new WSN topology control that unlike prior art neither relies on location information, nor on complex geometric structures, which could leave doubts about a practical feasibility. Even though location-free approaches have been proposed to circumvent systematic problems, they do not address issues like robustness and adaptability satisfyingly, which may lead to disconnection in real world deployments. kTC is a location-free approach that adapts topologies dynamically in face of changing environmental influences. It is based on a local, pattern-based heuristic, and transmitting only two messages per node to construct the topology it is highly scalable. The graphs kTC creates are symmetric, connected, and planar; they have bounded degree and nodes are θ-separated. Simulative evaluations indicate that kTC outperforms known topology control schemes. A preliminary deployment on a sensor testbed corroborates the obtained results and acts as proof of concept for kTC.
A Comparative Study of Fetal Heart Rate Variability Analysis Techniques

This study examines a novel methodology for continuous fetal heart rate variability (FHRV) assessment in a non-stationary intrapartum fetal heart rate (FHR). The specific aim was to investigate simple statistics, dimension estimates and entropy estimates as methods to discriminate situations of low FHRV related to non-reassuring fetal status or as a consequence of sedatives given to the mother. Using a t-test it is found that the dimension of the zero set and sample entropy reveal a difference in mean distribution of significance >99%. Thus it may prove possible to build a discriminating system based on either one or a combination of these techniques
Analysis of Korean Compound Nouns Using Statistical Information

In this paper, we propose a method of analyzing Korean compound nouns by using statistical information. To resolve ambiguous segmentations, head word frequency information and statistical preference rules from patterns of compound nouns are employed as statistical information. We have experimented on sets of test data. The data are 19,613 compound nouns obtained from about 700,000 corpus as test data. The evaluation showed that more than 97% of the segmented words obtained a correct analysis.
Host intrusion detection for long stealthy system call sequences

In this paper, we present SC2, an unsupervised learning classifier for detecting host intrusions from sequences of process system calls. SC2 is a naïve Bayes-like classifier based on Markov Model. We describe the classifier, and then provide experimental results on the University of New Mexico's four system call trace data sets, namely Synthetic Sendmail UNM, Synthetic Sendmail CERT, live lpr UNM and live lpr MIT. SC2 classification results are compared to leading machine learning techniques namely, Naive Bayes multinomial (NBm), C4.5 (decision tree), RIPPER (RP), support vector machine (SVM), and logistic regression (LR). Initial findings show that the accuracy of SC2 is comparable to those of leading classifiers, while SC2 has a better detection rate than some of these classifiers on some data sets. SC2 can classify efficiently very long stealthy sequences by using a backtrack, scale and re-multiply technique, together with estimation of standard IEEE 754-2008 relative error of floating-point arithmetic for an acceptable classification confidence.
A study on fingerprint image segmentation algorithm

A algorithm of fingerprint image segmentation is the essence of the whole fingerprint system, and this paper proposes an improved one which based on adaptive dynamic threshold segmentation algorithm. It ameliorates Otsu algorithm according to the unevenness of grey scale. This algorithm distinguishes the target from the background in the image with the combination of threshold segmentation and region segmentation, which can receive image with better effect. Experiments show that fingerprint image segmentation algorithm proposed by this article can receive good image effect.
Good concatenated code ensembles for the binary erasure channel

In this work, we give good concatenated code ensembles for the binary erasure channel (BEC). In particular, we consider repeat multiple-accumulate (RMA) code ensembles formed by the serial concatenation of a repetition code with multiple accumulators, and the hybrid concatenated code (HCC) ensembles recently introduced by Koller et al. (5th Int. Symp. on Turbo Codes & Rel. Topics, Lausanne, Switzerland) consisting of an outer multiple parallel concatenated code serially concatenated with an inner accumulator. We introduce stopping sets for iterative constituent code oriented decoding using maximum a posteriori erasure correction in the constituent codes. We then analyze the asymptotic stopping set distribution for RMA and HCC ensembles and show that their stopping distance hmin, defined as the size of the smallest nonempty stopping set, asymptotically grows linearly with the block length. Thus, these code ensembles are good for the BEC. It is shown that for RMA code ensembles, contrary to the asymptotic minimum distance dmin, whose growth rate coefficient increases with the number of accumulate codes, the hmin growth rate coefficient diminishes with the number of accumulators. We also consider random puncturing of RMA code ensembles and show that for sufficiently high code rates, the asymptotic hmin does not grow linearly with the block length, contrary to the asymptotic dmin, whose growth rate coefficient approaches the Gilbert-Varshamov bound as the rate increases. Finally, we give iterative decoding thresholds for the different code ensembles to compare the convergence properties.
Cognitive Behavioral Treatment of Postpartum Onset

This case study describes the application of cognitive behavioral therapy (CBT) for obsessive compulsive disorder (OCD) with postpartum onset. Sara, a 29-year-old woman, presented with aggressive obsessions of strangling and drowning her 5-month-old son. When she presented at the clinic, Sara had recently begun pharmacological treatment and was highly motivated to supplement this treatment with CBT. She showed marked improvement over the course of 8 CBT sessions using exposure and ritual prevention. This case study highlights cognitive and behavioral risk factors for OCD with postpartum onset, key considerations in differential diagnosis, and the utility of CBT for OCD with this population.
Policy-based tuning for performance portability and library co-optimization

Although modular programming is a fundamental software development practice, software reuse within contemporary GPU kernels is uncommon. For GPU software assets to be reusable across problem instances, they must be inherently flexible and tunable. To illustrate, we survey the performance-portability landscape for a suite of common GPU primitives, evaluating thousands of reasonable program variants across a large diversity of problem instances (microarchitecture, problem size, and data type). While individual specializations provide excellent performance for specific instances, we find no variants with “universally reasonable” performance. In this paper, we present a policy-based design idiom for constructing reusable, tunable software components that can be co-optimized with the enclosing kernel for the specific problem and processor at hand. In particular, this approach enables flexible granularity coarsening which allows the expensive aspects of communication and the redundant aspects of data parallelism to scale with the width of the processor rather than the problem size. From a small library of tunable device subroutines, we have constructed the fastest, most versatile GPU primitives for reduction, prefix and segmented scan, duplicate removal, reduction-by-key, sorting, and sparse graph traversal.
Focusing Attention on Visual Features that Matter

An indoor navigating agent needs to efficiently understand the geometric structure of its local environment in order to act. A common scene understanding approach is to generate a set of hypotheses about the geometric structure of the indoor environment and then test the hypotheses to select the one with the highest rank, from a single image [1, 4, 5, 6] or from a continuous stream of images (e.g. a video) [8, 9]. These methods simply detect features (e.g. lines [1, 4, 6], points [8, 9], and edges [6]) that are easily detectable for evaluating the hypotheses. In fact, some of the most informative features to discriminate the hypotheses may not be extracted if features are detected by fixed thresholds, since the informative regions may not have high image contrasts for features to be detected. This paper demonstrates that by focusing attention on features in the informative regions, we can evaluate the hypotheses more efficiently. The idea of focusing on informative regions of the image space is inspired by the idea of saliency detection [2, 3, 7]. While these works typically define saliency regions based on image and motion properties of the pixels in the images [2, 3] or based on human fixations [7], our informative regions are defined in terms of the agent’s own state of knowledge, the current set of hypotheses about the geometric structure of the indoor environment. Given a set M of hypotheses, we divide the image into regions based on the expected information gain that each feature provides, which we call informativeness (Figure 1). We define the informativeness I(p j,M)∈ [0,1] of point p j, measuring its discriminating power among the set M as,